update

Make pipeline notebooks links in readme

00.configuration.ipynb

@@ -101,11 +101,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from azureml.core import Workspace\n",
+    "import os\n",
     "\n",
-    "subscription_id ='<subscription-id>'\n",
-    "resource_group ='<resource-group>'\n",
-    "workspace_name = '<workspace-name>'\n",
+    "subscription_id = os.environ.get(\"SUBSCRIPTION_ID\", \"<my-subscription-id>\")\n",
+    "resource_group = os.environ.get(\"RESOURCE_GROUP\", \"<my-resource-group>\")\n",
+    "workspace_name = os.environ.get(\"WORKSPACE_NAME\", \"<my-workspace-name>\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace\n",
     "\n",
     "try:\n",
     "   ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name)\n",
@@ -131,7 +140,7 @@
     "* Your subscription id\n",
     "* The resource group name\n",
     "\n",
-    "**Note**: As with other Azure services, there are limits on certain resources (for eg. BatchAI cluster size) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
+    "**Note**: As with other Azure services, there are limits on certain resources (for eg. AmlCompute quota) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
    ]
   },
   {
@@ -142,15 +151,6 @@
     "Specify a region where your workspace will be located from the list of [Azure Machine Learning regions](https://linktoregions)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "workspace_region = \"eastus2\""
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -159,10 +159,11 @@
    "source": [
     "import os\n",
     "\n",
-    "subscription_id = os.environ.get(\"SUBSCRIPTION_ID\", subscription_id)\n",
-    "resource_group = os.environ.get(\"RESOURCE_GROUP\", resource_group)\n",
-    "workspace_name = os.environ.get(\"WORKSPACE_NAME\", workspace_name)\n",
-    "workspace_region = os.environ.get(\"WORKSPACE_REGION\", workspace_region)"
+    "subscription_id = os.environ.get(\"SUBSCRIPTION_ID\", \"<my-subscription-id>\")\n",
+    "resource_group = os.environ.get(\"RESOURCE_GROUP\", \"my-aml-resource-group\")\n",
+    "workspace_name = os.environ.get(\"WORKSPACE_NAME\", \"my-first-workspace\")\n",
+    "\n",
+    "workspace_region = os.environ.get(\"WORKSPACE_REGION\", \"eastus2\")"
    ]
   },
   {
@@ -207,12 +208,88 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Success!\n",
-    "Great, you are ready to move on to the rest of the sample notebooks."
+    "## Create compute resources for your training experiments\n",
+    "\n",
+    "Many of the subsequent examples use Azure Machine Learning managed compute (AmlCompute) to train models at scale. To create a **CPU** cluster now, run the cell below. The autoscale settings mean that the cluster will scale down to 0 nodes when inactive and up to 4 nodes when busy."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+    "from azureml.core.compute_target import ComputeTargetException\n",
+    "\n",
+    "# Choose a name for your CPU cluster\n",
+    "cpu_cluster_name = \"cpucluster\"\n",
+    "\n",
+    "# Verify that cluster does not exist already\n",
+    "try:\n",
+    "    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
+    "    print('Found existing cluster, use it.')\n",
+    "except ComputeTargetException:\n",
+    "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
+    "                                                           max_nodes=4)\n",
+    "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
+    "\n",
+    "cpu_cluster.wait_for_completion(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create a **GPU** cluster, run the cell below. Note that your subscription must have sufficient quota for GPU VMs or the command will fail. To increase quota, see [these instructions](https://docs.microsoft.com/en-us/azure/azure-supportability/resource-manager-core-quotas-request). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+    "from azureml.core.compute_target import ComputeTargetException\n",
+    "\n",
+    "# Choose a name for your GPU cluster\n",
+    "gpu_cluster_name = \"gpucluster\"\n",
+    "\n",
+    "# Check if cluster exists already\n",
+    "try:\n",
+    "    gpu_cluster = ComputeTarget(workspace=ws, name=gpu_cluster_name)\n",
+    "    print('Found existing cluster, use it.')\n",
+    "except ComputeTargetException:\n",
+    "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6',\n",
+    "                                                           max_nodes=4)\n",
+    "    gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, compute_config)\n",
+    "\n",
+    "gpu_cluster.wait_for_completion(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Success!\n",
+    "Great, you are ready to move on to the rest of the sample notebooks. A good place to start is the [01.train-model tutorial](./tutorials/01.train-model.ipynb) to learn how to train and then deploy an image classification model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
+  "authors": [
+   {
+    "name": "roastala"
+   }
+  ],
   "kernelspec": {
    "display_name": "Python 3.6",
    "language": "python",
@@ -228,7 +305,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

01.getting-started/01.train-within-notebook/score.py

@@ -21,7 +21,9 @@ def run(raw_data):
         data = json.loads(raw_data)['data']
         data = np.array(data)
         result = model.predict(data)
-        return json.dumps({"result": result.tolist()})
+
+        # you can return any data type as long as it is JSON-serializable
+        return result.tolist()
     except Exception as e:
         result = str(e)
-        return json.dumps({"error": result})
+        return result

01.getting-started/02.train-on-local/02.train-on-local.ipynb

@@ -1,470 +1,477 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 02. Train locally\n",
-        "* Create or load workspace.\n",
-        "* Create scripts locally.\n",
-        "* Create `train.py` in a folder, along with a `my.lib` file.\n",
-        "* Configure & execute a local run in a user-managed Python environment.\n",
-        "* Configure & execute a local run in a system-managed Python environment.\n",
-        "* Configure & execute a local run in a Docker environment.\n",
-        "* Query run metrics to find the best model\n",
-        "* Register model for operationalization."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Check core SDK version number\n",
-        "import azureml.core\n",
-        "\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize Workspace\n",
-        "\n",
-        "Initialize a workspace object from persisted configuration."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.workspace import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create An Experiment\n",
-        "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Experiment\n",
-        "experiment_name = 'train-on-local'\n",
-        "exp = Experiment(workspace=ws, name=experiment_name)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## View `train.py`\n",
-        "\n",
-        "`train.py` is already created for you."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "with open('./train.py', 'r') as f:\n",
-        "    print(f.read())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Note `train.py` also references a `mylib.py` file."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "with open('./mylib.py', 'r') as f:\n",
-        "    print(f.read())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Configure & Run\n",
-        "### User-managed environment\n",
-        "Below, we use a user-managed run, which means you are responsible to ensure all the necessary packages are available in the Python environment you choose to run the script."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "\n",
-        "# Editing a run configuration property on-fly.\n",
-        "run_config_user_managed = RunConfiguration()\n",
-        "\n",
-        "run_config_user_managed.environment.python.user_managed_dependencies = True\n",
-        "\n",
-        "# You can choose a specific Python environment by pointing to a Python path \n",
-        "#run_config.environment.python.interpreter_path = '/home/johndoe/miniconda3/envs/sdk2/bin/python'"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Submit script to run in the user-managed environment\n",
-        "Note whole script folder is submitted for execution, including the `mylib.py` file."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import ScriptRunConfig\n",
-        "\n",
-        "src = ScriptRunConfig(source_directory='./', script='train.py', run_config=run_config_user_managed)\n",
-        "run = exp.submit(src)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Get run history details"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Block to wait till run finishes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### System-managed environment\n",
-        "You can also ask the system to build a new conda environment and execute your scripts in it. The environment is built once and will be reused in subsequent executions as long as the conda dependencies remain unchanged. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "\n",
-        "run_config_system_managed = RunConfiguration()\n",
-        "\n",
-        "run_config_system_managed.environment.python.user_managed_dependencies = False\n",
-        "run_config_system_managed.auto_prepare_environment = True\n",
-        "\n",
-        "# Specify conda dependencies with scikit-learn\n",
-        "cd = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
-        "run_config_system_managed.environment.python.conda_dependencies = cd"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Submit script to run in the system-managed environment\n",
-        "A new conda environment is built based on the conda dependencies object. If you are running this for the first time,  this might take up to 5 mninutes. But this conda environment is reused so long as you don't change the conda dependencies."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "src = ScriptRunConfig(source_directory=\"./\", script='train.py', run_config=run_config_system_managed)\n",
-        "run = exp.submit(src)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Get run history details"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Block and wait till run finishes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run.wait_for_completion(show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Docker-based execution\n",
-        "**IMPORTANT**: You must have Docker engine installed locally in order to use this execution mode. If your kernel is already running in a Docker container, such as **Azure Notebooks**, this mode will **NOT** work.\n",
-        "\n",
-        "You can also ask the system to pull down a Docker image and execute your scripts in it."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "\n",
-        "run_config_docker = RunConfiguration()\n",
-        "\n",
-        "run_config_docker.environment.python.user_managed_dependencies = False\n",
-        "run_config_docker.auto_prepare_environment = True\n",
-        "run_config_docker.environment.docker.enabled = True\n",
-        "run_config_docker.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
-        "\n",
-        "# Specify conda dependencies with scikit-learn\n",
-        "cd = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
-        "run_config_docker.environment.python.conda_dependencies = cd"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Submit script to run in the system-managed environment\n",
-        "A new conda environment is built based on the conda dependencies object. If you are running this for the first time, this might take up to 5 mninutes. But this conda environment is reused so long as you don't change the conda dependencies.\n",
-        "\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "src = ScriptRunConfig(source_directory=\"./\", script='train.py', run_config=run_config_docker)\n",
-        "run = exp.submit(src)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#Get run history details\n",
-        "run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Query run metrics"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "query history",
-          "get metrics"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "# get all metris logged in the run\n",
-        "run.get_metrics()\n",
-        "metrics = run.get_metrics()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's find the model that has the lowest MSE value logged."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import numpy as np\n",
-        "\n",
-        "best_alpha = metrics['alpha'][np.argmin(metrics['mse'])]\n",
-        "\n",
-        "print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n",
-        "    min(metrics['mse']), \n",
-        "    best_alpha\n",
-        "))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can also list all the files that are associated with this run record"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run.get_file_names()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We know the model `ridge_0.40.pkl` is the best performing model from the eariler queries. So let's register it with the workspace."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# supply a model name, and the full path to the serialized model file.\n",
-        "model = run.register_model(model_name='best_ridge_model', model_path='./outputs/ridge_0.40.pkl')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(model.name, model.version, model.url)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now you can deploy this model following the example in the 01 notebook."
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "roastala"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 02. Train locally\n",
+    "* Create or load workspace.\n",
+    "* Create scripts locally.\n",
+    "* Create `train.py` in a folder, along with a `my.lib` file.\n",
+    "* Configure & execute a local run in a user-managed Python environment.\n",
+    "* Configure & execute a local run in a system-managed Python environment.\n",
+    "* Configure & execute a local run in a Docker environment.\n",
+    "* Query run metrics to find the best model\n",
+    "* Register model for operationalization."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check core SDK version number\n",
+    "import azureml.core\n",
+    "\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize Workspace\n",
+    "\n",
+    "Initialize a workspace object from persisted configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.workspace import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create An Experiment\n",
+    "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Experiment\n",
+    "experiment_name = 'train-on-local'\n",
+    "exp = Experiment(workspace=ws, name=experiment_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## View `train.py`\n",
+    "\n",
+    "`train.py` is already created for you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('./train.py', 'r') as f:\n",
+    "    print(f.read())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note `train.py` also references a `mylib.py` file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('./mylib.py', 'r') as f:\n",
+    "    print(f.read())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure & Run\n",
+    "### User-managed environment\n",
+    "Below, we use a user-managed run, which means you are responsible to ensure all the necessary packages are available in the Python environment you choose to run the script."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.runconfig import RunConfiguration\n",
+    "\n",
+    "# Editing a run configuration property on-fly.\n",
+    "run_config_user_managed = RunConfiguration()\n",
+    "\n",
+    "run_config_user_managed.environment.python.user_managed_dependencies = True\n",
+    "\n",
+    "# You can choose a specific Python environment by pointing to a Python path \n",
+    "#run_config.environment.python.interpreter_path = '/home/johndoe/miniconda3/envs/sdk2/bin/python'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Submit script to run in the user-managed environment\n",
+    "Note whole script folder is submitted for execution, including the `mylib.py` file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import ScriptRunConfig\n",
+    "\n",
+    "src = ScriptRunConfig(source_directory='./', script='train.py', run_config=run_config_user_managed)\n",
+    "run = exp.submit(src)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Get run history details"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Block to wait till run finishes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run.wait_for_completion(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### System-managed environment\n",
+    "You can also ask the system to build a new conda environment and execute your scripts in it. The environment is built once and will be reused in subsequent executions as long as the conda dependencies remain unchanged. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.runconfig import RunConfiguration\n",
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "\n",
+    "run_config_system_managed = RunConfiguration()\n",
+    "\n",
+    "run_config_system_managed.environment.python.user_managed_dependencies = False\n",
+    "run_config_system_managed.auto_prepare_environment = True\n",
+    "\n",
+    "# Specify conda dependencies with scikit-learn\n",
+    "cd = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
+    "run_config_system_managed.environment.python.conda_dependencies = cd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Submit script to run in the system-managed environment\n",
+    "A new conda environment is built based on the conda dependencies object. If you are running this for the first time,  this might take up to 5 mninutes. But this conda environment is reused so long as you don't change the conda dependencies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "src = ScriptRunConfig(source_directory=\"./\", script='train.py', run_config=run_config_system_managed)\n",
+    "run = exp.submit(src)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Get run history details"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Block and wait till run finishes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run.wait_for_completion(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Docker-based execution\n",
+    "**IMPORTANT**: You must have Docker engine installed locally in order to use this execution mode. If your kernel is already running in a Docker container, such as **Azure Notebooks**, this mode will **NOT** work.\n",
+    "\n",
+    "You can also ask the system to pull down a Docker image and execute your scripts in it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_config_docker = RunConfiguration()\n",
+    "run_config_docker.environment.python.user_managed_dependencies = False\n",
+    "run_config_docker.auto_prepare_environment = True\n",
+    "run_config_docker.environment.docker.enabled = True\n",
+    "run_config_docker.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
+    "\n",
+    "# Specify conda dependencies with scikit-learn\n",
+    "cd = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
+    "run_config_docker.environment.python.conda_dependencies = cd\n",
+    "\n",
+    "src = ScriptRunConfig(source_directory=\"./\", script='train.py', run_config=run_config_docker)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Submit script to run in the system-managed environment\n",
+    "A new conda environment is built based on the conda dependencies object. If you are running this for the first time, this might take up to 5 mninutes. But this conda environment is reused so long as you don't change the conda dependencies.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import subprocess\n",
+    "\n",
+    "# Check if Docker is installed and Linux containers are enables\n",
+    "if subprocess.run(\"docker -v\", shell=True) == 0:\n",
+    "    out = subprocess.check_output(\"docker system info\", shell=True, encoding=\"ascii\").split(\"\\n\")\n",
+    "    if not \"OSType: linux\" in out:\n",
+    "        print(\"Switch Docker engine to use Linux containers.\")\n",
+    "    else:\n",
+    "        run = exp.submit(src)\n",
+    "else:\n",
+    "    print(\"Docker engine not installed.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Get run history details\n",
+    "run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run.wait_for_completion(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Query run metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "query history",
+     "get metrics"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# get all metris logged in the run\n",
+    "run.get_metrics()\n",
+    "metrics = run.get_metrics()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's find the model that has the lowest MSE value logged."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "best_alpha = metrics['alpha'][np.argmin(metrics['mse'])]\n",
+    "\n",
+    "print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n",
+    "    min(metrics['mse']), \n",
+    "    best_alpha\n",
+    "))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also list all the files that are associated with this run record"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run.get_file_names()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We know the model `ridge_0.40.pkl` is the best performing model from the eariler queries. So let's register it with the workspace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# supply a model name, and the full path to the serialized model file.\n",
+    "model = run.register_model(model_name='best_ridge_model', model_path='./outputs/ridge_0.40.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(model.name, model.version, model.url)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now you can deploy this model following the example in the 01 notebook."
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "roastala"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

01.getting-started/03.train-on-aci/03.train-on-aci.ipynb

@@ -1,289 +1,289 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 03. Train on Azure Container Instance\n",
-        "\n",
-        "* Create Workspace\n",
-        "* Create `train.py` in the project folder.\n",
-        "* Configure an ACI (Azure Container Instance) run\n",
-        "* Execute in ACI"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Check core SDK version number\n",
-        "import azureml.core\n",
-        "\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize Workspace\n",
-        "\n",
-        "Initialize a workspace object from persisted configuration"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "create workspace"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create An Experiment\n",
-        "\n",
-        "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Experiment\n",
-        "experiment_name = 'train-on-aci'\n",
-        "experiment = Experiment(workspace = ws, name = experiment_name)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Remote execution on ACI\n",
-        "\n",
-        "The training script `train.py` is already created for you. Let's have a look."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "with open('./train.py', 'r') as f:\n",
-        "    print(f.read())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Configure for using ACI\n",
-        "Linux-based ACI is available in `West US`, `East US`, `West Europe`, `North Europe`, `West US 2`, `Southeast Asia`, `Australia East`, `East US 2`, and `Central US` regions. See details [here](https://docs.microsoft.com/en-us/azure/container-instances/container-instances-quotas#region-availability)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "configure run"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "\n",
-        "# create a new runconfig object\n",
-        "run_config = RunConfiguration()\n",
-        "\n",
-        "# signal that you want to use ACI to execute script.\n",
-        "run_config.target = \"containerinstance\"\n",
-        "\n",
-        "# ACI container group is only supported in certain regions, which can be different than the region the Workspace is in.\n",
-        "run_config.container_instance.region = 'eastus2'\n",
-        "\n",
-        "# set the ACI CPU and Memory \n",
-        "run_config.container_instance.cpu_cores = 1\n",
-        "run_config.container_instance.memory_gb = 2\n",
-        "\n",
-        "# enable Docker \n",
-        "run_config.environment.docker.enabled = True\n",
-        "\n",
-        "# set Docker base image to the default CPU-based image\n",
-        "run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
-        "\n",
-        "# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
-        "run_config.environment.python.user_managed_dependencies = False\n",
-        "\n",
-        "# auto-prepare the Docker image when used for execution (if it is not already prepared)\n",
-        "run_config.auto_prepare_environment = True\n",
-        "\n",
-        "# specify CondaDependencies obj\n",
-        "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Submit the Experiment\n",
-        "Finally, run the training job on the ACI"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "remote run",
-          "aci"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "%%time \n",
-        "from azureml.core.script_run_config import ScriptRunConfig\n",
-        "\n",
-        "script_run_config = ScriptRunConfig(source_directory='./',\n",
-        "                                    script='train.py',\n",
-        "                                    run_config=run_config)\n",
-        "\n",
-        "run = experiment.submit(script_run_config)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "query history"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "# Show run details\n",
-        "run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "remote run",
-          "aci"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "# Shows output of the run on stdout.\n",
-        "run.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "get metrics"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "# get all metris logged in the run\n",
-        "run.get_metrics()\n",
-        "metrics = run.get_metrics()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import numpy as np\n",
-        "print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n",
-        "    min(metrics['mse']), \n",
-        "    metrics['alpha'][np.argmin(metrics['mse'])]\n",
-        "))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# show all the files stored within the run record\n",
-        "run.get_file_names()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now you can take a model produced here, register it and then deploy as a web service."
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "roastala"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 03. Train on Azure Container Instance\n",
+    "\n",
+    "* Create Workspace\n",
+    "* Create `train.py` in the project folder.\n",
+    "* Configure an ACI (Azure Container Instance) run\n",
+    "* Execute in ACI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check core SDK version number\n",
+    "import azureml.core\n",
+    "\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize Workspace\n",
+    "\n",
+    "Initialize a workspace object from persisted configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "create workspace"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create An Experiment\n",
+    "\n",
+    "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Experiment\n",
+    "experiment_name = 'train-on-aci'\n",
+    "experiment = Experiment(workspace = ws, name = experiment_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Remote execution on ACI\n",
+    "\n",
+    "The training script `train.py` is already created for you. Let's have a look."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('./train.py', 'r') as f:\n",
+    "    print(f.read())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure for using ACI\n",
+    "Linux-based ACI is available in `West US`, `East US`, `West Europe`, `North Europe`, `West US 2`, `Southeast Asia`, `Australia East`, `East US 2`, and `Central US` regions. See details [here](https://docs.microsoft.com/en-us/azure/container-instances/container-instances-quotas#region-availability)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "configure run"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from azureml.core.runconfig import RunConfiguration\n",
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "\n",
+    "# create a new runconfig object\n",
+    "run_config = RunConfiguration()\n",
+    "\n",
+    "# signal that you want to use ACI to execute script.\n",
+    "run_config.target = \"containerinstance\"\n",
+    "\n",
+    "# ACI container group is only supported in certain regions, which can be different than the region the Workspace is in.\n",
+    "run_config.container_instance.region = 'eastus2'\n",
+    "\n",
+    "# set the ACI CPU and Memory \n",
+    "run_config.container_instance.cpu_cores = 1\n",
+    "run_config.container_instance.memory_gb = 2\n",
+    "\n",
+    "# enable Docker \n",
+    "run_config.environment.docker.enabled = True\n",
+    "\n",
+    "# set Docker base image to the default CPU-based image\n",
+    "run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
+    "\n",
+    "# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
+    "run_config.environment.python.user_managed_dependencies = False\n",
+    "\n",
+    "# auto-prepare the Docker image when used for execution (if it is not already prepared)\n",
+    "run_config.auto_prepare_environment = True\n",
+    "\n",
+    "# specify CondaDependencies obj\n",
+    "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Submit the Experiment\n",
+    "Finally, run the training job on the ACI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "remote run",
+     "aci"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "%%time \n",
+    "from azureml.core.script_run_config import ScriptRunConfig\n",
+    "\n",
+    "script_run_config = ScriptRunConfig(source_directory='./',\n",
+    "                                    script='train.py',\n",
+    "                                    run_config=run_config)\n",
+    "\n",
+    "run = experiment.submit(script_run_config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "query history"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Show run details\n",
+    "run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "remote run",
+     "aci"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "# Shows output of the run on stdout.\n",
+    "run.wait_for_completion(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "get metrics"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# get all metris logged in the run\n",
+    "run.get_metrics()\n",
+    "metrics = run.get_metrics()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n",
+    "    min(metrics['mse']), \n",
+    "    metrics['alpha'][np.argmin(metrics['mse'])]\n",
+    "))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# show all the files stored within the run record\n",
+    "run.get_file_names()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now you can take a model produced here, register it and then deploy as a web service."
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "roastala"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

01.getting-started/05.train-in-spark/05.train-in-spark.ipynb

@@ -1,331 +1,331 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 05. Train in Spark\n",
-        "* Create Workspace\n",
-        "* Create Experiment\n",
-        "* Copy relevant files to the script folder\n",
-        "* Configure and Run"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Check core SDK version number\n",
-        "import azureml.core\n",
-        "\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize Workspace\n",
-        "\n",
-        "Initialize a workspace object from persisted configuration."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create Experiment\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "experiment_name = 'train-on-spark'\n",
-        "\n",
-        "from azureml.core import Experiment\n",
-        "exp = Experiment(workspace=ws, name=experiment_name)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## View `train-spark.py`\n",
-        "\n",
-        "For convenience, we created a training script for you. It is printed below as a text, but you can also run `%pfile ./train-spark.py` in a cell to show the file."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "with open('train-spark.py', 'r') as training_script:\n",
-        "    print(training_script.read())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Configure & Run"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Configure an ACI run\n",
-        "Before you try running on an actual Spark cluster, you can use a Docker image with Spark already baked in, and run it in ACI(Azure Container Registry)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "\n",
-        "# use pyspark framework\n",
-        "aci_run_config = RunConfiguration(framework=\"pyspark\")\n",
-        "\n",
-        "# use ACI to run the Spark job\n",
-        "aci_run_config.target = 'containerinstance'\n",
-        "aci_run_config.container_instance.region = 'eastus2'\n",
-        "aci_run_config.container_instance.cpu_cores = 1\n",
-        "aci_run_config.container_instance.memory_gb = 2\n",
-        "\n",
-        "# specify base Docker image to use\n",
-        "aci_run_config.environment.docker.enabled = True\n",
-        "aci_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_MMLSPARK_CPU_IMAGE\n",
-        "\n",
-        "# specify CondaDependencies\n",
-        "cd = CondaDependencies()\n",
-        "cd.add_conda_package('numpy')\n",
-        "aci_run_config.environment.python.conda_dependencies = cd"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Submit script to ACI to run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import ScriptRunConfig\n",
-        "\n",
-        "script_run_config = ScriptRunConfig(source_directory = '.',\n",
-        "                                    script= 'train-spark.py',\n",
-        "                                    run_config = aci_run_config)\n",
-        "run = exp.submit(script_run_config)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "**Note** you can also create a new VM, or attach an existing VM, and use Docker-based execution to run the Spark job. Please see the `04.train-in-vm` for example on how to configure and run in Docker mode in a VM."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Attach an HDI cluster\n",
-        "Now we can use a real Spark cluster, HDInsight for Spark, to run this job. To use HDI commpute target:\n",
-        " 1. Create a Spark for HDI cluster in Azure. Here are some [quick instructions](https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-jupyter-spark-sql). Make sure you use the Ubuntu flavor, NOT CentOS.\n",
-        " 2. Enter the IP address, username and password below"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import HDInsightCompute\n",
-        "from azureml.exceptions import ComputeTargetException\n",
-        "\n",
-        "try:\n",
-        "    # if you want to connect using SSH key instead of username/password you can provide parameters private_key_file and private_key_passphrase\n",
-        "    hdi_compute = HDInsightCompute.attach(workspace=ws, \n",
-        "                                          name=\"myhdi\", \n",
-        "                                          address=\"<myhdi-ssh>.azurehdinsight.net\", \n",
-        "                                          ssh_port=22, \n",
-        "                                          username='<ssh-username>', \n",
-        "                                          password='<ssh-pwd>')\n",
-        "\n",
-        "except ComputeTargetException as e:\n",
-        "    print(\"Caught = {}\".format(e.message))\n",
-        "    \n",
-        "        \n",
-        "hdi_compute.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Configure HDI run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "\n",
-        "\n",
-        "# use pyspark framework\n",
-        "hdi_run_config = RunConfiguration(framework=\"pyspark\")\n",
-        "\n",
-        "# Set compute target to the HDI cluster\n",
-        "hdi_run_config.target = hdi_compute.name\n",
-        "\n",
-        "# specify CondaDependencies object to ask system installing numpy\n",
-        "cd = CondaDependencies()\n",
-        "cd.add_conda_package('numpy')\n",
-        "hdi_run_config.environment.python.conda_dependencies = cd"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Submit the script to HDI"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import ScriptRunConfig\n",
-        "\n",
-        "script_run_config = ScriptRunConfig(source_directory = '.',\n",
-        "                                    script= 'train-spark.py',\n",
-        "                                    run_config = hdi_run_config)\n",
-        "run = exp.submit(config=script_run_config)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# get the URL of the run history web page\n",
-        "run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# get all metris logged in the run\n",
-        "metrics = run.get_metrics()\n",
-        "print(metrics)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "aashishb"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 05. Train in Spark\n",
+    "* Create Workspace\n",
+    "* Create Experiment\n",
+    "* Copy relevant files to the script folder\n",
+    "* Configure and Run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check core SDK version number\n",
+    "import azureml.core\n",
+    "\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize Workspace\n",
+    "\n",
+    "Initialize a workspace object from persisted configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Experiment\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment_name = 'train-on-spark'\n",
+    "\n",
+    "from azureml.core import Experiment\n",
+    "exp = Experiment(workspace=ws, name=experiment_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## View `train-spark.py`\n",
+    "\n",
+    "For convenience, we created a training script for you. It is printed below as a text, but you can also run `%pfile ./train-spark.py` in a cell to show the file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('train-spark.py', 'r') as training_script:\n",
+    "    print(training_script.read())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure & Run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Configure an ACI run\n",
+    "Before you try running on an actual Spark cluster, you can use a Docker image with Spark already baked in, and run it in ACI(Azure Container Registry)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.runconfig import RunConfiguration\n",
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "\n",
+    "# use pyspark framework\n",
+    "aci_run_config = RunConfiguration(framework=\"pyspark\")\n",
+    "\n",
+    "# use ACI to run the Spark job\n",
+    "aci_run_config.target = 'containerinstance'\n",
+    "aci_run_config.container_instance.region = 'eastus2'\n",
+    "aci_run_config.container_instance.cpu_cores = 1\n",
+    "aci_run_config.container_instance.memory_gb = 2\n",
+    "\n",
+    "# specify base Docker image to use\n",
+    "aci_run_config.environment.docker.enabled = True\n",
+    "aci_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_MMLSPARK_CPU_IMAGE\n",
+    "\n",
+    "# specify CondaDependencies\n",
+    "cd = CondaDependencies()\n",
+    "cd.add_conda_package('numpy')\n",
+    "aci_run_config.environment.python.conda_dependencies = cd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Submit script to ACI to run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import ScriptRunConfig\n",
+    "\n",
+    "script_run_config = ScriptRunConfig(source_directory = '.',\n",
+    "                                    script= 'train-spark.py',\n",
+    "                                    run_config = aci_run_config)\n",
+    "run = exp.submit(script_run_config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run.wait_for_completion(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note** you can also create a new VM, or attach an existing VM, and use Docker-based execution to run the Spark job. Please see the `04.train-in-vm` for example on how to configure and run in Docker mode in a VM."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Attach an HDI cluster\n",
+    "Now we can use a real Spark cluster, HDInsight for Spark, to run this job. To use HDI commpute target:\n",
+    " 1. Create a Spark for HDI cluster in Azure. Here are some [quick instructions](https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-jupyter-spark-sql). Make sure you use the Ubuntu flavor, NOT CentOS.\n",
+    " 2. Enter the IP address, username and password below"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import HDInsightCompute\n",
+    "from azureml.exceptions import ComputeTargetException\n",
+    "\n",
+    "try:\n",
+    "    # if you want to connect using SSH key instead of username/password you can provide parameters private_key_file and private_key_passphrase\n",
+    "    hdi_compute = HDInsightCompute.attach(workspace=ws, \n",
+    "                                          name=\"myhdi\", \n",
+    "                                          address=\"<myhdi-ssh>.azurehdinsight.net\", \n",
+    "                                          ssh_port=22, \n",
+    "                                          username='<ssh-username>', \n",
+    "                                          password='<ssh-pwd>')\n",
+    "\n",
+    "except ComputeTargetException as e:\n",
+    "    print(\"Caught = {}\".format(e.message))\n",
+    "    \n",
+    "        \n",
+    "hdi_compute.wait_for_completion(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Configure HDI run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.runconfig import RunConfiguration\n",
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "\n",
+    "\n",
+    "# use pyspark framework\n",
+    "hdi_run_config = RunConfiguration(framework=\"pyspark\")\n",
+    "\n",
+    "# Set compute target to the HDI cluster\n",
+    "hdi_run_config.target = hdi_compute.name\n",
+    "\n",
+    "# specify CondaDependencies object to ask system installing numpy\n",
+    "cd = CondaDependencies()\n",
+    "cd.add_conda_package('numpy')\n",
+    "hdi_run_config.environment.python.conda_dependencies = cd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Submit the script to HDI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import ScriptRunConfig\n",
+    "\n",
+    "script_run_config = ScriptRunConfig(source_directory = '.',\n",
+    "                                    script= 'train-spark.py',\n",
+    "                                    run_config = hdi_run_config)\n",
+    "run = exp.submit(config=script_run_config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the URL of the run history web page\n",
+    "run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get all metris logged in the run\n",
+    "metrics = run.get_metrics()\n",
+    "print(metrics)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "aashishb"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

01.getting-started/06.logging-api/06.logging-api.ipynb

@@ -0,0 +1,328 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 06. Logging APIs\n",
+    "This notebook showcase various ways to use the Azure Machine Learning service run logging APIs, and view the results in the Azure portal."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "Make sure you go through the [00. Installation and Configuration](../../00.configuration.ipynb) Notebook first if you haven't. Also make sure you have tqdm and matplotlib installed in the current kernel.\n",
+    "\n",
+    "```\n",
+    "(myenv) $ conda install -y tqdm matplotlib\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Validate Azure ML SDK installation and get version number for debugging purposes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "install"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from azureml.core import Experiment, Run, Workspace\n",
+    "import azureml.core\n",
+    "import numpy as np\n",
+    "\n",
+    "# Check core SDK version number\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize Workspace\n",
+    "\n",
+    "Initialize a workspace object from persisted configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "create workspace"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "print('Workspace name: ' + ws.name, \n",
+    "      'Azure region: ' + ws.location, \n",
+    "      'Subscription id: ' + ws.subscription_id, \n",
+    "      'Resource group: ' + ws.resource_group, sep='\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set experiment\n",
+    "Create a new experiment (or get the one with such name)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "exp = Experiment(workspace=ws, name='logging-api-test')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Log metrics\n",
+    "We will start a run, and use the various logging APIs to record different types of metrics during the run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm\n",
+    "\n",
+    "# start logging for the run\n",
+    "run = exp.start_logging()\n",
+    "\n",
+    "# log a string value\n",
+    "run.log(name='Name', value='Logging API run')\n",
+    "\n",
+    "# log a numerical value\n",
+    "run.log(name='Magic Number', value=42)\n",
+    "\n",
+    "# Log a list of values. Note this will generate a single-variable line chart.\n",
+    "run.log_list(name='Fibonacci', value=[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89])\n",
+    "\n",
+    "# create a dictionary to hold a table of values\n",
+    "sines = {}\n",
+    "sines['angle'] = []\n",
+    "sines['sine'] = []\n",
+    "\n",
+    "for i in tqdm(range(-10, 10)):\n",
+    "    # log a metric value repeatedly, this will generate a single-variable line chart.\n",
+    "    run.log(name='Sigmoid', value=1 / (1 + np.exp(-i)))\n",
+    "    angle = i / 2.0\n",
+    "    \n",
+    "    # log a 2 (or more) values as a metric repeatedly. This will generate a 2-variable line chart if you have 2 numerical columns.\n",
+    "    run.log_row(name='Cosine Wave', angle=angle, cos=np.cos(angle))\n",
+    "        \n",
+    "    sines['angle'].append(angle)\n",
+    "    sines['sine'].append(np.sin(angle))\n",
+    "\n",
+    "# log a dictionary as a table, this will generate a 2-variable chart if you have 2 numerical columns\n",
+    "run.log_table(name='Sine Wave', value=sines)\n",
+    "\n",
+    "run.complete()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Even after the run is marked completed, you can still log things."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Log an image\n",
+    "This is how to log a _matplotlib_ pyplot object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "angle = np.linspace(-3, 3, 50)\n",
+    "plt.plot(angle, np.tanh(angle), label='tanh')\n",
+    "plt.legend(fontsize=12)\n",
+    "plt.title('Hyperbolic Tangent', fontsize=16)\n",
+    "plt.grid(True)\n",
+    "\n",
+    "run.log_image(name='Hyperbolic Tangent', plot=plt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Upload a file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also upload an abitrary file. First, let's create a dummy file locally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile myfile.txt\n",
+    "\n",
+    "This is a dummy file."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's upload this file into the run record as a run artifact, and display the properties after the upload."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "props = run.upload_file(name='myfile_in_the_cloud.txt', path_or_stream='./myfile.txt')\n",
+    "props.serialize()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Examine the run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's take a look at the run detail page in Azure portal. Make sure you checkout the various charts and plots generated/uploaded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can get all the metrics in that run back."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run.get_metrics()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also see the files uploaded for this run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run.get_file_names()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also download all the files locally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.makedirs('files', exist_ok=True)\n",
+    "\n",
+    "for f in run.get_file_names():\n",
+    "    dest = os.path.join('files', f.split('/')[-1])\n",
+    "    print('Downloading file {} to {}...'.format(f, dest))\n",
+    "    run.download_file(f, dest)   "
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "haining"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

01.getting-started/10.register-model-create-image-deploy-service/10.register-model-create-image-deploy-service.ipynb

@@ -1,425 +1,420 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 10. Register Model, Create Image and Deploy Service\n",
-        "\n",
-        "This example shows how to deploy a web service in step-by-step fashion:\n",
-        "\n",
-        " 1. Register model\n",
-        " 2. Query versions of models and select one to deploy\n",
-        " 3. Create Docker image\n",
-        " 4. Query versions of images\n",
-        " 5. Deploy the image as web service\n",
-        " \n",
-        "**IMPORTANT**:\n",
-        " * This notebook requires you to first complete \"01.SDK-101-Train-and-Deploy-to-ACI.ipynb\" Notebook\n",
-        " \n",
-        "The 101 Notebook taught you how to deploy a web service directly from model in one step. This Notebook shows a more advanced approach that gives you more control over model versions and Docker image versions.  "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Check core SDK version number\n",
-        "import azureml.core\n",
-        "\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize Workspace\n",
-        "\n",
-        "Initialize a workspace object from persisted configuration."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "create workspace"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Register Model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can add tags and descriptions to your models. Note you need to have a `sklearn_linreg_model.pkl` file in the current directory. This file is generated by the 01 notebook. The below call registers that file as a model with the same name `sklearn_linreg_model.pkl` in the workspace.\n",
-        "\n",
-        "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "register model from file"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.model import Model\n",
-        "import sklearn\n",
-        "\n",
-        "library_version = \"sklearn\"+sklearn.__version__.replace(\".\",\"x\")\n",
-        "\n",
-        "model = Model.register(model_path = \"sklearn_regression_model.pkl\",\n",
-        "                       model_name = \"sklearn_regression_model.pkl\",\n",
-        "                       tags = {'area': \"diabetes\", 'type': \"regression\", 'version': library_version},\n",
-        "                       description = \"Ridge regression model to predict diabetes\",\n",
-        "                       workspace = ws)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can explore the registered models within your workspace and query by tag. Models are versioned. If you call the register_model command many times with same model name, you will get multiple versions of the model with increasing version numbers."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "register model from file"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "regression_models = Model.list(workspace=ws, tags=['area'])\n",
-        "for m in regression_models:\n",
-        "    print(\"Name:\", m.name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can pick a specific model to deploy"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(model.name, model.description, model.version, sep = '\\t')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create Docker Image"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Show `score.py`. Note that the `sklearn_regression_model.pkl` in the `get_model_path` call is referring to a model named `sklearn_linreg_model.pkl` registered under the workspace. It is NOT referenceing the local file."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile score.py\n",
-        "import pickle\n",
-        "import json\n",
-        "import numpy\n",
-        "from sklearn.externals import joblib\n",
-        "from sklearn.linear_model import Ridge\n",
-        "from azureml.core.model import Model\n",
-        "\n",
-        "def init():\n",
-        "    global model\n",
-        "    # note here \"sklearn_regression_model.pkl\" is the name of the model registered under\n",
-        "    # this is a different behavior than before when the code is run locally, even though the code is the same.\n",
-        "    model_path = Model.get_model_path('sklearn_regression_model.pkl')\n",
-        "    # deserialize the model file back into a sklearn model\n",
-        "    model = joblib.load(model_path)\n",
-        "\n",
-        "# note you can pass in multiple rows for scoring\n",
-        "def run(raw_data):\n",
-        "    try:\n",
-        "        data = json.loads(raw_data)['data']\n",
-        "        data = numpy.array(data)\n",
-        "        result = model.predict(data)\n",
-        "    except Exception as e:\n",
-        "        result = str(e)\n",
-        "    return json.dumps({\"result\": result.tolist()})"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies \n",
-        "\n",
-        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
-        "\n",
-        "with open(\"myenv.yml\",\"w\") as f:\n",
-        "    f.write(myenv.serialize_to_string())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Note that following command can take few minutes. \n",
-        "\n",
-        "You can add tags and descriptions to images. Also, an image can contain multiple models."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "create image"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.image import Image, ContainerImage\n",
-        "\n",
-        "image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
-        "                                 execution_script=\"score.py\",\n",
-        "                                 conda_file=\"myenv.yml\",\n",
-        "                                 tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
-        "                                 description = \"Image with ridge regression model\")\n",
-        "\n",
-        "image = Image.create(name = \"myimage1\",\n",
-        "                     # this is the model object \n",
-        "                     models = [model],\n",
-        "                     image_config = image_config, \n",
-        "                     workspace = ws)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "create image"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "image.wait_for_creation(show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "List images by tag and find out the detailed build log for debugging."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "create image"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "for i in Image.list(workspace = ws,tags = [\"area\"]):\n",
-        "    print('{}(v.{} [{}]) stored at {} with build log {}'.format(i.name, i.version, i.creation_state, i.image_location, i.image_build_log_uri))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Deploy image as web service on Azure Container Instance\n",
-        "\n",
-        "Note that the service creation can take few minutes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "deploy service",
-          "aci"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import AciWebservice\n",
-        "\n",
-        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
-        "                                               memory_gb = 1, \n",
-        "                                               tags = {'area': \"diabetes\", 'type': \"regression\"}, \n",
-        "                                               description = 'Predict diabetes using regression model')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "deploy service",
-          "aci"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import Webservice\n",
-        "\n",
-        "aci_service_name = 'my-aci-service-2'\n",
-        "print(aci_service_name)\n",
-        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
-        "                                           image = image,\n",
-        "                                           name = aci_service_name,\n",
-        "                                           workspace = ws)\n",
-        "aci_service.wait_for_deployment(True)\n",
-        "print(aci_service.state)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Test web service"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Call the web service with some dummy input data to get a prediction."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "deploy service",
-          "aci"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "import json\n",
-        "\n",
-        "test_sample = json.dumps({'data': [\n",
-        "    [1,2,3,4,5,6,7,8,9,10], \n",
-        "    [10,9,8,7,6,5,4,3,2,1]\n",
-        "]})\n",
-        "test_sample = bytes(test_sample,encoding = 'utf8')\n",
-        "\n",
-        "prediction = aci_service.run(input_data = test_sample)\n",
-        "print(prediction)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Delete ACI to clean up"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "deploy service",
-          "aci"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "aci_service.delete()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "raymondl"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.5"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 10. Register Model, Create Image and Deploy Service\n",
+    "\n",
+    "This example shows how to deploy a web service in step-by-step fashion:\n",
+    "\n",
+    " 1. Register model\n",
+    " 2. Query versions of models and select one to deploy\n",
+    " 3. Create Docker image\n",
+    " 4. Query versions of images\n",
+    " 5. Deploy the image as web service\n",
+    " \n",
+    "**IMPORTANT**:\n",
+    " * This notebook requires you to first complete \"01.SDK-101-Train-and-Deploy-to-ACI.ipynb\" Notebook\n",
+    " \n",
+    "The 101 Notebook taught you how to deploy a web service directly from model in one step. This Notebook shows a more advanced approach that gives you more control over model versions and Docker image versions.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check core SDK version number\n",
+    "import azureml.core\n",
+    "\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize Workspace\n",
+    "\n",
+    "Initialize a workspace object from persisted configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "create workspace"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Register Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can add tags and descriptions to your models. Note you need to have a `sklearn_linreg_model.pkl` file in the current directory. This file is generated by the 01 notebook. The below call registers that file as a model with the same name `sklearn_linreg_model.pkl` in the workspace.\n",
+    "\n",
+    "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "register model from file"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from azureml.core.model import Model\n",
+    "import sklearn\n",
+    "\n",
+    "library_version = \"sklearn\"+sklearn.__version__.replace(\".\",\"x\")\n",
+    "\n",
+    "model = Model.register(model_path = \"sklearn_regression_model.pkl\",\n",
+    "                       model_name = \"sklearn_regression_model.pkl\",\n",
+    "                       tags = {'area': \"diabetes\", 'type': \"regression\", 'version': library_version},\n",
+    "                       description = \"Ridge regression model to predict diabetes\",\n",
+    "                       workspace = ws)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can explore the registered models within your workspace and query by tag. Models are versioned. If you call the register_model command many times with same model name, you will get multiple versions of the model with increasing version numbers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "register model from file"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "regression_models = Model.list(workspace=ws, tags=['area'])\n",
+    "for m in regression_models:\n",
+    "    print(\"Name:\", m.name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can pick a specific model to deploy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(model.name, model.description, model.version, sep = '\\t')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create Docker Image"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Show `score.py`. Note that the `sklearn_regression_model.pkl` in the `get_model_path` call is referring to a model named `sklearn_linreg_model.pkl` registered under the workspace. It is NOT referenceing the local file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile score.py\n",
+    "import pickle\n",
+    "import json\n",
+    "import numpy\n",
+    "from sklearn.externals import joblib\n",
+    "from sklearn.linear_model import Ridge\n",
+    "from azureml.core.model import Model\n",
+    "\n",
+    "def init():\n",
+    "    global model\n",
+    "    # note here \"sklearn_regression_model.pkl\" is the name of the model registered under\n",
+    "    # this is a different behavior than before when the code is run locally, even though the code is the same.\n",
+    "    model_path = Model.get_model_path('sklearn_regression_model.pkl')\n",
+    "    # deserialize the model file back into a sklearn model\n",
+    "    model = joblib.load(model_path)\n",
+    "\n",
+    "# note you can pass in multiple rows for scoring\n",
+    "def run(raw_data):\n",
+    "    try:\n",
+    "        data = json.loads(raw_data)['data']\n",
+    "        data = numpy.array(data)\n",
+    "        result = model.predict(data)\n",
+    "        # you can return any datatype as long as it is JSON-serializable\n",
+    "        return result.tolist()\n",
+    "    except Exception as e:\n",
+    "        error = str(e)\n",
+    "        return error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.conda_dependencies import CondaDependencies \n",
+    "\n",
+    "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
+    "\n",
+    "with open(\"myenv.yml\",\"w\") as f:\n",
+    "    f.write(myenv.serialize_to_string())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that following command can take few minutes. \n",
+    "\n",
+    "You can add tags and descriptions to images. Also, an image can contain multiple models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "create image"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from azureml.core.image import Image, ContainerImage\n",
+    "\n",
+    "image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
+    "                                 execution_script=\"score.py\",\n",
+    "                                 conda_file=\"myenv.yml\",\n",
+    "                                 tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
+    "                                 description = \"Image with ridge regression model\")\n",
+    "\n",
+    "image = Image.create(name = \"myimage1\",\n",
+    "                     # this is the model object \n",
+    "                     models = [model],\n",
+    "                     image_config = image_config, \n",
+    "                     workspace = ws)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "create image"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "image.wait_for_creation(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "List images by tag and find out the detailed build log for debugging."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "create image"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "for i in Image.list(workspace = ws,tags = [\"area\"]):\n",
+    "    print('{}(v.{} [{}]) stored at {} with build log {}'.format(i.name, i.version, i.creation_state, i.image_location, i.image_build_log_uri))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deploy image as web service on Azure Container Instance\n",
+    "\n",
+    "Note that the service creation can take few minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "deploy service",
+     "aci"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from azureml.core.webservice import AciWebservice\n",
+    "\n",
+    "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
+    "                                               memory_gb = 1, \n",
+    "                                               tags = {'area': \"diabetes\", 'type': \"regression\"}, \n",
+    "                                               description = 'Predict diabetes using regression model')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "deploy service",
+     "aci"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from azureml.core.webservice import Webservice\n",
+    "\n",
+    "aci_service_name = 'my-aci-service-2'\n",
+    "print(aci_service_name)\n",
+    "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
+    "                                           image = image,\n",
+    "                                           name = aci_service_name,\n",
+    "                                           workspace = ws)\n",
+    "aci_service.wait_for_deployment(True)\n",
+    "print(aci_service.state)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test web service"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Call the web service with some dummy input data to get a prediction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "deploy service",
+     "aci"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "test_sample = json.dumps({'data': [\n",
+    "    [1,2,3,4,5,6,7,8,9,10], \n",
+    "    [10,9,8,7,6,5,4,3,2,1]\n",
+    "]})\n",
+    "test_sample = bytes(test_sample,encoding = 'utf8')\n",
+    "\n",
+    "prediction = aci_service.run(input_data=test_sample)\n",
+    "print(prediction)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Delete ACI to clean up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "deploy service",
+     "aci"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "aci_service.delete()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "raymondl"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

01.getting-started/11.production-deploy-to-aks/11.production-deploy-to-aks.ipynb

@@ -1,340 +1,342 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Deploying a web service to Azure Kubernetes Service (AKS)\n",
-        "This notebook shows the steps for deploying a service: registering a model, creating an image, provisioning a cluster (one time action), and deploying a service to it. \n",
-        "We then test and delete the service, image and model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "from azureml.core.compute import AksCompute, ComputeTarget\n",
-        "from azureml.core.webservice import Webservice, AksWebservice\n",
-        "from azureml.core.image import Image\n",
-        "from azureml.core.model import Model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.core\n",
-        "print(azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Get workspace\n",
-        "Load existing workspace from the config file info."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.workspace import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Register the model\n",
-        "Register an existing trained model, add descirption and tags."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#Register the model\n",
-        "from azureml.core.model import Model\n",
-        "model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n",
-        "                       model_name = \"sklearn_regression_model.pkl\", # this is the name the model is registered as\n",
-        "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
-        "                       description = \"Ridge regression model to predict diabetes\",\n",
-        "                       workspace = ws)\n",
-        "\n",
-        "print(model.name, model.description, model.version)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Create an image\n",
-        "Create an image using the registered model the script that will load and run the model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile score.py\n",
-        "import pickle\n",
-        "import json\n",
-        "import numpy\n",
-        "from sklearn.externals import joblib\n",
-        "from sklearn.linear_model import Ridge\n",
-        "from azureml.core.model import Model\n",
-        "\n",
-        "def init():\n",
-        "    global model\n",
-        "    # note here \"sklearn_regression_model.pkl\" is the name of the model registered under\n",
-        "    # this is a different behavior than before when the code is run locally, even though the code is the same.\n",
-        "    model_path = Model.get_model_path('sklearn_regression_model.pkl')\n",
-        "    # deserialize the model file back into a sklearn model\n",
-        "    model = joblib.load(model_path)\n",
-        "\n",
-        "# note you can pass in multiple rows for scoring\n",
-        "def run(raw_data):\n",
-        "    try:\n",
-        "        data = json.loads(raw_data)['data']\n",
-        "        data = numpy.array(data)\n",
-        "        result = model.predict(data)\n",
-        "    except Exception as e:\n",
-        "        result = str(e)\n",
-        "    return json.dumps({\"result\": result.tolist()})"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies \n",
-        "\n",
-        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
-        "\n",
-        "with open(\"myenv.yml\",\"w\") as f:\n",
-        "    f.write(myenv.serialize_to_string())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.image import ContainerImage\n",
-        "\n",
-        "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
-        "                                                  runtime = \"python\",\n",
-        "                                                  conda_file = \"myenv.yml\",\n",
-        "                                                  description = \"Image with ridge regression model\",\n",
-        "                                                  tags = {'area': \"diabetes\", 'type': \"regression\"}\n",
-        "                                                 )\n",
-        "\n",
-        "image = ContainerImage.create(name = \"myimage1\",\n",
-        "                              # this is the model object\n",
-        "                              models = [model],\n",
-        "                              image_config = image_config,\n",
-        "                              workspace = ws)\n",
-        "\n",
-        "image.wait_for_creation(show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Provision the AKS Cluster\n",
-        "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Use the default configuration (can also provide parameters to customize)\n",
-        "prov_config = AksCompute.provisioning_configuration()\n",
-        "\n",
-        "aks_name = 'my-aks-9' \n",
-        "# Create the cluster\n",
-        "aks_target = ComputeTarget.create(workspace = ws, \n",
-        "                                  name = aks_name, \n",
-        "                                  provisioning_configuration = prov_config)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "aks_target.wait_for_completion(show_output = True)\n",
-        "print(aks_target.provisioning_state)\n",
-        "print(aks_target.provisioning_errors)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Optional step: Attach existing AKS cluster\n",
-        "\n",
-        "If you have existing AKS cluster in your Azure subscription, you can attach it to the Workspace."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "'''\n",
-        "# Use the default configuration (can also provide parameters to customize)\n",
-        "resource_id = '/subscriptions/92c76a2f-0e1c-4216-b65e-abf7a3f34c1e/resourcegroups/raymondsdk0604/providers/Microsoft.ContainerService/managedClusters/my-aks-0605d37425356b7d01'\n",
-        "\n",
-        "create_name='my-existing-aks' \n",
-        "# Create the cluster\n",
-        "aks_target = AksCompute.attach(workspace=ws, name=create_name, resource_id=resource_id)\n",
-        "# Wait for the operation to complete\n",
-        "aks_target.wait_for_completion(True)\n",
-        "'''"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Deploy web service to AKS"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#Set the web service configuration (using default here)\n",
-        "aks_config = AksWebservice.deploy_configuration()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "aks_service_name ='aks-service-1'\n",
-        "\n",
-        "aks_service = Webservice.deploy_from_image(workspace = ws, \n",
-        "                                           name = aks_service_name,\n",
-        "                                           image = image,\n",
-        "                                           deployment_config = aks_config,\n",
-        "                                           deployment_target = aks_target)\n",
-        "aks_service.wait_for_deployment(show_output = True)\n",
-        "print(aks_service.state)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Test the web service\n",
-        "We test the web sevice by passing data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "import json\n",
-        "\n",
-        "test_sample = json.dumps({'data': [\n",
-        "    [1,2,3,4,5,6,7,8,9,10], \n",
-        "    [10,9,8,7,6,5,4,3,2,1]\n",
-        "]})\n",
-        "test_sample = bytes(test_sample,encoding = 'utf8')\n",
-        "\n",
-        "prediction = aks_service.run(input_data = test_sample)\n",
-        "print(prediction)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Clean up\n",
-        "Delete the service, image and model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "aks_service.delete()\n",
-        "image.delete()\n",
-        "model.delete()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "raymondl"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.5"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deploying a web service to Azure Kubernetes Service (AKS)\n",
+    "This notebook shows the steps for deploying a service: registering a model, creating an image, provisioning a cluster (one time action), and deploying a service to it. \n",
+    "We then test and delete the service, image and model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace\n",
+    "from azureml.core.compute import AksCompute, ComputeTarget\n",
+    "from azureml.core.webservice import Webservice, AksWebservice\n",
+    "from azureml.core.image import Image\n",
+    "from azureml.core.model import Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import azureml.core\n",
+    "print(azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Get workspace\n",
+    "Load existing workspace from the config file info."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.workspace import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Register the model\n",
+    "Register an existing trained model, add descirption and tags."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Register the model\n",
+    "from azureml.core.model import Model\n",
+    "model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n",
+    "                       model_name = \"sklearn_regression_model.pkl\", # this is the name the model is registered as\n",
+    "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
+    "                       description = \"Ridge regression model to predict diabetes\",\n",
+    "                       workspace = ws)\n",
+    "\n",
+    "print(model.name, model.description, model.version)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create an image\n",
+    "Create an image using the registered model the script that will load and run the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile score.py\n",
+    "import pickle\n",
+    "import json\n",
+    "import numpy\n",
+    "from sklearn.externals import joblib\n",
+    "from sklearn.linear_model import Ridge\n",
+    "from azureml.core.model import Model\n",
+    "\n",
+    "def init():\n",
+    "    global model\n",
+    "    # note here \"sklearn_regression_model.pkl\" is the name of the model registered under\n",
+    "    # this is a different behavior than before when the code is run locally, even though the code is the same.\n",
+    "    model_path = Model.get_model_path('sklearn_regression_model.pkl')\n",
+    "    # deserialize the model file back into a sklearn model\n",
+    "    model = joblib.load(model_path)\n",
+    "\n",
+    "# note you can pass in multiple rows for scoring\n",
+    "def run(raw_data):\n",
+    "    try:\n",
+    "        data = json.loads(raw_data)['data']\n",
+    "        data = numpy.array(data)\n",
+    "        result = model.predict(data)\n",
+    "        # you can return any data type as long as it is JSON-serializable\n",
+    "        return result.tolist()\n",
+    "    except Exception as e:\n",
+    "        error = str(e)\n",
+    "        return error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.conda_dependencies import CondaDependencies \n",
+    "\n",
+    "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
+    "\n",
+    "with open(\"myenv.yml\",\"w\") as f:\n",
+    "    f.write(myenv.serialize_to_string())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.image import ContainerImage\n",
+    "\n",
+    "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
+    "                                                  runtime = \"python\",\n",
+    "                                                  conda_file = \"myenv.yml\",\n",
+    "                                                  description = \"Image with ridge regression model\",\n",
+    "                                                  tags = {'area': \"diabetes\", 'type': \"regression\"}\n",
+    "                                                 )\n",
+    "\n",
+    "image = ContainerImage.create(name = \"myimage1\",\n",
+    "                              # this is the model object\n",
+    "                              models = [model],\n",
+    "                              image_config = image_config,\n",
+    "                              workspace = ws)\n",
+    "\n",
+    "image.wait_for_creation(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Provision the AKS Cluster\n",
+    "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use the default configuration (can also provide parameters to customize)\n",
+    "prov_config = AksCompute.provisioning_configuration()\n",
+    "\n",
+    "aks_name = 'my-aks-9' \n",
+    "# Create the cluster\n",
+    "aks_target = ComputeTarget.create(workspace = ws, \n",
+    "                                  name = aks_name, \n",
+    "                                  provisioning_configuration = prov_config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "aks_target.wait_for_completion(show_output = True)\n",
+    "print(aks_target.provisioning_state)\n",
+    "print(aks_target.provisioning_errors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Optional step: Attach existing AKS cluster\n",
+    "\n",
+    "If you have existing AKS cluster in your Azure subscription, you can attach it to the Workspace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'''\n",
+    "# Use the default configuration (can also provide parameters to customize)\n",
+    "resource_id = '/subscriptions/92c76a2f-0e1c-4216-b65e-abf7a3f34c1e/resourcegroups/raymondsdk0604/providers/Microsoft.ContainerService/managedClusters/my-aks-0605d37425356b7d01'\n",
+    "\n",
+    "create_name='my-existing-aks' \n",
+    "# Create the cluster\n",
+    "aks_target = AksCompute.attach(workspace=ws, name=create_name, resource_id=resource_id)\n",
+    "# Wait for the operation to complete\n",
+    "aks_target.wait_for_completion(True)\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deploy web service to AKS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Set the web service configuration (using default here)\n",
+    "aks_config = AksWebservice.deploy_configuration()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "aks_service_name ='aks-service-1'\n",
+    "\n",
+    "aks_service = Webservice.deploy_from_image(workspace = ws, \n",
+    "                                           name = aks_service_name,\n",
+    "                                           image = image,\n",
+    "                                           deployment_config = aks_config,\n",
+    "                                           deployment_target = aks_target)\n",
+    "aks_service.wait_for_deployment(show_output = True)\n",
+    "print(aks_service.state)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Test the web service\n",
+    "We test the web sevice by passing data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "import json\n",
+    "\n",
+    "test_sample = json.dumps({'data': [\n",
+    "    [1,2,3,4,5,6,7,8,9,10], \n",
+    "    [10,9,8,7,6,5,4,3,2,1]\n",
+    "]})\n",
+    "test_sample = bytes(test_sample,encoding = 'utf8')\n",
+    "\n",
+    "prediction = aks_service.run(input_data = test_sample)\n",
+    "print(prediction)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clean up\n",
+    "Delete the service, image and model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "aks_service.delete()\n",
+    "image.delete()\n",
+    "model.delete()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "raymondl"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

01.getting-started/12.enable-data-collection-for-models-in-aks/12.enable-data-collection-for-models-in-aks.ipynb

@@ -156,11 +156,12 @@
     "        inputs_dc.collect(data) #this call is saving our input data into our blob\n",
     "        prediction_dc.collect(result)#this call is saving our prediction data into our blob\n",
     "        print (\"saving prediction data\" + time.strftime(\"%H:%M:%S\"))\n",
-    "        return json.dumps({\"result\": result.tolist()})\n",
+    "        # you can return any data type as long as it is JSON-serializable\n",
+    "        return result.tolist()\n",
     "    except Exception as e:\n",
-    "        result = str(e)\n",
-    "        print (result + time.strftime(\"%H:%M:%S\"))\n",
-    "        return json.dumps({\"error\": result})"
+    "        error = str(e)\n",
+    "        print (error + time.strftime(\"%H:%M:%S\"))\n",
+    "        return error"
    ]
   },
   {

01.getting-started/13.enable-app-insights/13.enable-app-insights-in-production-service.ipynb

@@ -1,415 +1,414 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Enabling App Insights for Services in Production\n",
-        "With this notebook, you can learn how to enable App Insights for standard service monitoring, plus, we provide examples for doing custom logging within a scoring files in a model. \n",
-        "\n",
-        "\n",
-        "## What does Application Insights monitor?\n",
-        "It monitors request rates, response times, failure rates, etc. For more information visit [App Insights docs.](https://docs.microsoft.com/en-us/azure/application-insights/app-insights-overview)\n",
-        "\n",
-        "\n",
-        "## What is different compared to standard production deployment process?\n",
-        "If you want to enable generic App Insights for a service run:\n",
-        "```python\n",
-        "aks_service= Webservice(ws, \"aks-w-dc2\")\n",
-        "aks_service.update(enable_app_insights=True)```\n",
-        "Where \"aks-w-dc2\" is your service name. You can also do this from the Azure Portal under your Workspace--> deployments--> Select deployment--> Edit--> Advanced Settings--> Select \"Enable AppInsights diagnostics\"\n",
-        "\n",
-        "If you want to log custom traces, you will follow the standard deplyment process for AKS and you will:\n",
-        "1. Update scoring file.\n",
-        "2. Update aks configuration.\n",
-        "3. Build new image and deploy it. "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 1. Import your dependencies"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace, Run\n",
-        "from azureml.core.compute import AksCompute, ComputeTarget\n",
-        "from azureml.core.webservice import Webservice, AksWebservice\n",
-        "from azureml.core.image import Image\n",
-        "from azureml.core.model import Model\n",
-        "\n",
-        "import azureml.core\n",
-        "print(azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 2. Set up your configuration and create a workspace\n",
-        "Follow Notebook 00 instructions to do this.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 3. Register Model\n",
-        "Register an existing trained model, add descirption and tags."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#Register the model\n",
-        "from azureml.core.model import Model\n",
-        "model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n",
-        "                       model_name = \"sklearn_regression_model.pkl\", # this is the name the model is registered as\n",
-        "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
-        "                       description = \"Ridge regression model to predict diabetes\",\n",
-        "                       workspace = ws)\n",
-        "\n",
-        "print(model.name, model.description, model.version)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 4. *Update your scoring file with custom print statements*\n",
-        "Here is an example:\n",
-        "### a. In your init function add:\n",
-        "```python\n",
-        "print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))```\n",
-        "\n",
-        "### b. In your run function add:\n",
-        "```python\n",
-        "print (\"saving input data\" + time.strftime(\"%H:%M:%S\"))\n",
-        "print (\"saving prediction data\" + time.strftime(\"%H:%M:%S\"))```"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile score.py\n",
-        "import pickle\n",
-        "import json\n",
-        "import numpy \n",
-        "from sklearn.externals import joblib\n",
-        "from sklearn.linear_model import Ridge\n",
-        "from azureml.core.model import Model\n",
-        "from azureml.monitoring import ModelDataCollector\n",
-        "import time\n",
-        "\n",
-        "def init():\n",
-        "    global model\n",
-        "    #Print statement for appinsights custom traces:\n",
-        "    print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))\n",
-        "    \n",
-        "    # note here \"sklearn_regression_model.pkl\" is the name of the model registered under the workspace\n",
-        "    # this call should return the path to the model.pkl file on the local disk.\n",
-        "    model_path = Model.get_model_path(model_name = 'sklearn_regression_model.pkl')\n",
-        "    \n",
-        "    # deserialize the model file back into a sklearn model\n",
-        "    model = joblib.load(model_path)\n",
-        "    \n",
-        "    global inputs_dc, prediction_dc\n",
-        "    \n",
-        "    # this setup will help us save our inputs under the \"inputs\" path in our Azure Blob\n",
-        "    inputs_dc = ModelDataCollector(model_name=\"sklearn_regression_model\", identifier=\"inputs\", feature_names=[\"feat1\", \"feat2\"]) \n",
-        "    \n",
-        "    # this setup will help us save our ipredictions under the \"predictions\" path in our Azure Blob\n",
-        "    prediction_dc = ModelDataCollector(\"sklearn_regression_model\", identifier=\"predictions\", feature_names=[\"prediction1\", \"prediction2\"]) \n",
-        "  \n",
-        "# note you can pass in multiple rows for scoring\n",
-        "def run(raw_data):\n",
-        "    global inputs_dc, prediction_dc\n",
-        "    try:\n",
-        "        data = json.loads(raw_data)['data']\n",
-        "        data = numpy.array(data)\n",
-        "        result = model.predict(data)\n",
-        "        \n",
-        "        #Print statement for appinsights custom traces:\n",
-        "        print (\"saving input data\" + time.strftime(\"%H:%M:%S\"))\n",
-        "        \n",
-        "        #this call is saving our input data into our blob\n",
-        "        inputs_dc.collect(data) \n",
-        "        #this call is saving our prediction data into our blob\n",
-        "        prediction_dc.collect(result)\n",
-        "        \n",
-        "        #Print statement for appinsights custom traces:\n",
-        "        print (\"saving prediction data\" + time.strftime(\"%H:%M:%S\"))\n",
-        "        \n",
-        "        return json.dumps({\"result\": result.tolist()})\n",
-        "    \n",
-        "    except Exception as e:\n",
-        "        result = str(e)\n",
-        "        print (result + time.strftime(\"%H:%M:%S\"))\n",
-        "        return json.dumps({\"error\": result})"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 5. *Create myenv.yml file*"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies \n",
-        "\n",
-        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
-        "\n",
-        "with open(\"myenv.yml\",\"w\") as f:\n",
-        "    f.write(myenv.serialize_to_string())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 6. Create your new Image"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.image import ContainerImage\n",
-        "\n",
-        "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
-        "                                                  runtime = \"python\",\n",
-        "                                                  conda_file = \"myenv.yml\",\n",
-        "                                                  description = \"Image with ridge regression model\",\n",
-        "                                                  tags = {'area': \"diabetes\", 'type': \"regression\"}\n",
-        "                                                 )\n",
-        "\n",
-        "image = ContainerImage.create(name = \"myimage1\",\n",
-        "                              # this is the model object\n",
-        "                              models = [model],\n",
-        "                              image_config = image_config,\n",
-        "                              workspace = ws)\n",
-        "\n",
-        "image.wait_for_creation(show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 7. Deploy to AKS service"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create AKS compute if you haven't done so (Notebook 11)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Use the default configuration (can also provide parameters to customize)\n",
-        "prov_config = AksCompute.provisioning_configuration()\n",
-        "\n",
-        "aks_name = 'my-aks-test1' \n",
-        "# Create the cluster\n",
-        "aks_target = ComputeTarget.create(workspace = ws, \n",
-        "                                  name = aks_name, \n",
-        "                                  provisioning_configuration = prov_config)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "aks_target.wait_for_completion(show_output = True)\n",
-        "print(aks_target.provisioning_state)\n",
-        "print(aks_target.provisioning_errors)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If you already have a cluster you can attach the service to it:"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "```python \n",
-        "%%time\n",
-        "resource_id = '/subscriptions/<subscriptionid>/resourcegroups/<resourcegroupname>/providers/Microsoft.ContainerService/managedClusters/<aksservername>'\n",
-        "create_name= 'myaks4'\n",
-        "aks_target = AksCompute.attach(workspace = ws, \n",
-        "                                  name = create_name, \n",
-        "                                  #esource_id=resource_id)\n",
-        "## Wait for the operation to complete\n",
-        "aks_target.wait_for_provisioning(True)```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### a. *Activate App Insights through updating AKS Webservice configuration*\n",
-        "In order to enable App Insights in your service you will need to update your AKS configuration file:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#Set the web service configuration\n",
-        "aks_config = AksWebservice.deploy_configuration(enable_app_insights=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### b. Deploy your service"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "aks_service_name ='aks-w-dc3'\n",
-        "\n",
-        "aks_service = Webservice.deploy_from_image(workspace = ws, \n",
-        "                                           name = aks_service_name,\n",
-        "                                           image = image,\n",
-        "                                           deployment_config = aks_config,\n",
-        "                                           deployment_target = aks_target\n",
-        "                                           )\n",
-        "aks_service.wait_for_deployment(show_output = True)\n",
-        "print(aks_service.state)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 8. Test your service "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "import json\n",
-        "\n",
-        "test_sample = json.dumps({'data': [\n",
-        "    [1,28,13,45,54,6,57,8,8,10], \n",
-        "    [101,9,8,37,6,45,4,3,2,41]\n",
-        "]})\n",
-        "test_sample = bytes(test_sample,encoding = 'utf8')\n",
-        "\n",
-        "prediction = aks_service.run(input_data = test_sample)\n",
-        "print(prediction)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 9. See your service telemetry in App Insights\n",
-        "1. Go to the [Azure Portal](https://portal.azure.com/)\n",
-        "2. All resources--> Select the subscription/resource group where you created your Workspace--> Select the App Insights type\n",
-        "3. Click on the AppInsights resource. You'll see a highlevel dashboard with information on Requests, Server response time and availability.\n",
-        "4. Click on the top banner \"Analytics\"\n",
-        "5. In the \"Schema\" section select \"traces\" and run your query.\n",
-        "6. Voila! All your custom traces should be there."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Disable App Insights"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "aks_service.update(enable_app_insights=False)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "marthalc"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Enabling App Insights for Services in Production\n",
+    "With this notebook, you can learn how to enable App Insights for standard service monitoring, plus, we provide examples for doing custom logging within a scoring files in a model. \n",
+    "\n",
+    "\n",
+    "## What does Application Insights monitor?\n",
+    "It monitors request rates, response times, failure rates, etc. For more information visit [App Insights docs.](https://docs.microsoft.com/en-us/azure/application-insights/app-insights-overview)\n",
+    "\n",
+    "\n",
+    "## What is different compared to standard production deployment process?\n",
+    "If you want to enable generic App Insights for a service run:\n",
+    "```python\n",
+    "aks_service= Webservice(ws, \"aks-w-dc2\")\n",
+    "aks_service.update(enable_app_insights=True)```\n",
+    "Where \"aks-w-dc2\" is your service name. You can also do this from the Azure Portal under your Workspace--> deployments--> Select deployment--> Edit--> Advanced Settings--> Select \"Enable AppInsights diagnostics\"\n",
+    "\n",
+    "If you want to log custom traces, you will follow the standard deplyment process for AKS and you will:\n",
+    "1. Update scoring file.\n",
+    "2. Update aks configuration.\n",
+    "3. Build new image and deploy it. "
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Import your dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace, Run\n",
+    "from azureml.core.compute import AksCompute, ComputeTarget\n",
+    "from azureml.core.webservice import Webservice, AksWebservice\n",
+    "from azureml.core.image import Image\n",
+    "from azureml.core.model import Model\n",
+    "\n",
+    "import azureml.core\n",
+    "print(azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Set up your configuration and create a workspace\n",
+    "Follow Notebook 00 instructions to do this.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Register Model\n",
+    "Register an existing trained model, add descirption and tags."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Register the model\n",
+    "from azureml.core.model import Model\n",
+    "model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n",
+    "                       model_name = \"sklearn_regression_model.pkl\", # this is the name the model is registered as\n",
+    "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
+    "                       description = \"Ridge regression model to predict diabetes\",\n",
+    "                       workspace = ws)\n",
+    "\n",
+    "print(model.name, model.description, model.version)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. *Update your scoring file with custom print statements*\n",
+    "Here is an example:\n",
+    "### a. In your init function add:\n",
+    "```python\n",
+    "print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))```\n",
+    "\n",
+    "### b. In your run function add:\n",
+    "```python\n",
+    "print (\"saving input data\" + time.strftime(\"%H:%M:%S\"))\n",
+    "print (\"saving prediction data\" + time.strftime(\"%H:%M:%S\"))```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile score.py\n",
+    "import pickle\n",
+    "import json\n",
+    "import numpy \n",
+    "from sklearn.externals import joblib\n",
+    "from sklearn.linear_model import Ridge\n",
+    "from azureml.core.model import Model\n",
+    "from azureml.monitoring import ModelDataCollector\n",
+    "import time\n",
+    "\n",
+    "def init():\n",
+    "    global model\n",
+    "    #Print statement for appinsights custom traces:\n",
+    "    print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))\n",
+    "    \n",
+    "    # note here \"sklearn_regression_model.pkl\" is the name of the model registered under the workspace\n",
+    "    # this call should return the path to the model.pkl file on the local disk.\n",
+    "    model_path = Model.get_model_path(model_name = 'sklearn_regression_model.pkl')\n",
+    "    \n",
+    "    # deserialize the model file back into a sklearn model\n",
+    "    model = joblib.load(model_path)\n",
+    "    \n",
+    "    global inputs_dc, prediction_dc\n",
+    "    \n",
+    "    # this setup will help us save our inputs under the \"inputs\" path in our Azure Blob\n",
+    "    inputs_dc = ModelDataCollector(model_name=\"sklearn_regression_model\", identifier=\"inputs\", feature_names=[\"feat1\", \"feat2\"]) \n",
+    "    \n",
+    "    # this setup will help us save our ipredictions under the \"predictions\" path in our Azure Blob\n",
+    "    prediction_dc = ModelDataCollector(\"sklearn_regression_model\", identifier=\"predictions\", feature_names=[\"prediction1\", \"prediction2\"]) \n",
+    "  \n",
+    "# note you can pass in multiple rows for scoring\n",
+    "def run(raw_data):\n",
+    "    global inputs_dc, prediction_dc\n",
+    "    try:\n",
+    "        data = json.loads(raw_data)['data']\n",
+    "        data = numpy.array(data)\n",
+    "        result = model.predict(data)\n",
+    "        \n",
+    "        #Print statement for appinsights custom traces:\n",
+    "        print (\"saving input data\" + time.strftime(\"%H:%M:%S\"))\n",
+    "        \n",
+    "        #this call is saving our input data into our blob\n",
+    "        inputs_dc.collect(data) \n",
+    "        #this call is saving our prediction data into our blob\n",
+    "        prediction_dc.collect(result)\n",
+    "        \n",
+    "        #Print statement for appinsights custom traces:\n",
+    "        print (\"saving prediction data\" + time.strftime(\"%H:%M:%S\"))\n",
+    "        # you can return any data type as long as it is JSON-serializable\n",
+    "        return result.tolist()\n",
+    "    except Exception as e:\n",
+    "        error = str(e)\n",
+    "        print (error + time.strftime(\"%H:%M:%S\"))\n",
+    "        return error"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. *Create myenv.yml file*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.conda_dependencies import CondaDependencies \n",
+    "\n",
+    "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
+    "\n",
+    "with open(\"myenv.yml\",\"w\") as f:\n",
+    "    f.write(myenv.serialize_to_string())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Create your new Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.image import ContainerImage\n",
+    "\n",
+    "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
+    "                                                  runtime = \"python\",\n",
+    "                                                  conda_file = \"myenv.yml\",\n",
+    "                                                  description = \"Image with ridge regression model\",\n",
+    "                                                  tags = {'area': \"diabetes\", 'type': \"regression\"}\n",
+    "                                                 )\n",
+    "\n",
+    "image = ContainerImage.create(name = \"myimage1\",\n",
+    "                              # this is the model object\n",
+    "                              models = [model],\n",
+    "                              image_config = image_config,\n",
+    "                              workspace = ws)\n",
+    "\n",
+    "image.wait_for_creation(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Deploy to AKS service"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create AKS compute if you haven't done so (Notebook 11)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use the default configuration (can also provide parameters to customize)\n",
+    "prov_config = AksCompute.provisioning_configuration()\n",
+    "\n",
+    "aks_name = 'my-aks-test1' \n",
+    "# Create the cluster\n",
+    "aks_target = ComputeTarget.create(workspace = ws, \n",
+    "                                  name = aks_name, \n",
+    "                                  provisioning_configuration = prov_config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "aks_target.wait_for_completion(show_output = True)\n",
+    "print(aks_target.provisioning_state)\n",
+    "print(aks_target.provisioning_errors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you already have a cluster you can attach the service to it:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python \n",
+    "%%time\n",
+    "resource_id = '/subscriptions/<subscriptionid>/resourcegroups/<resourcegroupname>/providers/Microsoft.ContainerService/managedClusters/<aksservername>'\n",
+    "create_name= 'myaks4'\n",
+    "aks_target = AksCompute.attach(workspace = ws, \n",
+    "                                  name = create_name, \n",
+    "                                  #esource_id=resource_id)\n",
+    "## Wait for the operation to complete\n",
+    "aks_target.wait_for_provisioning(True)```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### a. *Activate App Insights through updating AKS Webservice configuration*\n",
+    "In order to enable App Insights in your service you will need to update your AKS configuration file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Set the web service configuration\n",
+    "aks_config = AksWebservice.deploy_configuration(enable_app_insights=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### b. Deploy your service"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "aks_service_name ='aks-w-dc3'\n",
+    "\n",
+    "aks_service = Webservice.deploy_from_image(workspace = ws, \n",
+    "                                           name = aks_service_name,\n",
+    "                                           image = image,\n",
+    "                                           deployment_config = aks_config,\n",
+    "                                           deployment_target = aks_target\n",
+    "                                           )\n",
+    "aks_service.wait_for_deployment(show_output = True)\n",
+    "print(aks_service.state)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 8. Test your service "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "import json\n",
+    "\n",
+    "test_sample = json.dumps({'data': [\n",
+    "    [1,28,13,45,54,6,57,8,8,10], \n",
+    "    [101,9,8,37,6,45,4,3,2,41]\n",
+    "]})\n",
+    "test_sample = bytes(test_sample,encoding='utf8')\n",
+    "\n",
+    "prediction = aks_service.run(input_data=test_sample)\n",
+    "print(prediction)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 9. See your service telemetry in App Insights\n",
+    "1. Go to the [Azure Portal](https://portal.azure.com/)\n",
+    "2. All resources--> Select the subscription/resource group where you created your Workspace--> Select the App Insights type\n",
+    "3. Click on the AppInsights resource. You'll see a highlevel dashboard with information on Requests, Server response time and availability.\n",
+    "4. Click on the top banner \"Analytics\"\n",
+    "5. In the \"Schema\" section select \"traces\" and run your query.\n",
+    "6. Voila! All your custom traces should be there."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Disable App Insights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aks_service.update(enable_app_insights=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "marthalc"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

README.md

@@ -1,10 +1,5 @@
-Get the full documentation for Azure Machine Learning service at:
-
-https://docs.microsoft.com/azure/machine-learning/service/
-
-<br>
-
-# Sample notebooks for Azure Machine Learning service
+For full documentation for Azure Machine Learning service, visit **https://aka.ms/aml-docs**.
+# Sample Notebooks for Azure Machine Learning service
 
 To run the notebooks in this repository use one of these methods:
 
@@ -22,13 +17,24 @@ To run the notebooks in this repository use one of these methods:
 
 ## **Use your own notebook server**
 
+Video walkthrough:
+
+[![get started video](images/yt_cover.png)](https://youtu.be/VIsXeTuW3FU)
+
 1. Setup a Jupyter Notebook server and [install the Azure Machine Learning SDK](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python).
 1. Clone [this repository](https://aka.ms/aml-notebooks).
-1. You may need to install other packages for specific notebooks
+1. You may need to install other packages for specific notebook. 
+    - For example, to run the Azure Machine Learning Data Prep notebooks, install the extra dataprep SDK:
+    ```
+     pip install --upgrade azureml-dataprep
+    ```
+
 1. Start your notebook server.
 1. Follow the instructions in the [00.configuration](00.configuration.ipynb) notebook to create and connect to a workspace.
 1. Open one of the sample notebooks.
 
+
+
 > Note: **Looking for automated machine learning samples?**
 > For your convenience, you can use an installation script instead of the steps below for the automated ML notebooks. Go to the [automl folder README](automl/README.md) and follow the instructions.  The script installs all  packages needed for notebooks in that folder.
 

automl/00.configuration.ipynb

@@ -1,224 +1,224 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# AutoML 00. Configuration\n",
-        "\n",
-        "In this example you will create an Azure Machine Learning `Workspace` object and initialize your notebook directory to easily reload this object from a configuration file.  Typically you will only need to run this once per notebook directory, and all other notebooks in this directory or any sub-directories will automatically use the settings you indicate here.\n",
-        "\n",
-        "\n",
-        "## Prerequisites:\n",
-        "\n",
-        "Before running this notebook, run the `automl_setup` script described in README.md.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Register Machine Learning Services Resource Provider\n",
-        "\n",
-        "Microsoft.MachineLearningServices only needs to be registed once in the subscription.\n",
-        "To register it:\n",
-        "1. Start the Azure portal.\n",
-        "2. Select your `All services` and then `Subscription`.\n",
-        "3. Select the subscription that you want to use.\n",
-        "4. Click on `Resource providers`\n",
-        "3. Click the `Register` link next to Microsoft.MachineLearningServices"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Check the Azure ML Core SDK Version to Validate Your Installation"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.core\n",
-        "\n",
-        "print(\"SDK Version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize an Azure ML Workspace\n",
-        "### What is an Azure ML Workspace and Why Do I Need One?\n",
-        "\n",
-        "An Azure ML workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an Azure ML workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, operationalization, and the monitoring of operationalized models.\n",
-        "\n",
-        "\n",
-        "### What do I Need?\n",
-        "\n",
-        "To create or access an Azure ML workspace, you will need to import the Azure ML library and specify following information:\n",
-        "* A name for your workspace. You can choose one.\n",
-        "* Your subscription id. Use the `id` value from the `az account show` command output above.\n",
-        "* The resource group name. The resource group organizes Azure resources and provides a default region for the resources in the group. The resource group will be created if it doesn't exist. Resource groups can be created and viewed in the [Azure portal](https://portal.azure.com)\n",
-        "* Supported regions include `eastus2`, `eastus`,`westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "subscription_id = \"<subscription_id>\"\n",
-        "resource_group = \"myrg\"\n",
-        "workspace_name = \"myws\"\n",
-        "workspace_region = \"eastus2\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Creating a Workspace\n",
-        "If you already have access to an Azure ML workspace you want to use, you can skip this cell.  Otherwise, this cell will create an Azure ML workspace for you in the specified subscription, provided you have the correct permissions for the given `subscription_id`.\n",
-        "\n",
-        "This will fail when:\n",
-        "1. The workspace already exists.\n",
-        "2. You do not have permission to create a workspace in the resource group.\n",
-        "3. You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription.\n",
-        "\n",
-        "If workspace creation fails for any reason other than already existing, please work with your IT administrator to provide you with the appropriate permissions or to provision the required resources.\n",
-        "\n",
-        "**Note:** Creation of a new workspace can take several minutes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Import the Workspace class and check the Azure ML SDK version.\n",
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.create(name = workspace_name,\n",
-        "                      subscription_id = subscription_id,\n",
-        "                      resource_group = resource_group, \n",
-        "                      location = workspace_region)\n",
-        "ws.get_details()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Configuring Your Local Environment\n",
-        "You can validate that you have access to the specified workspace and write a configuration file to the default configuration location, `./aml_config/config.json`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace(workspace_name = workspace_name,\n",
-        "               subscription_id = subscription_id,\n",
-        "               resource_group = resource_group)\n",
-        "\n",
-        "# Persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
-        "ws.write_config()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can then load the workspace from this config file from any notebook in the current directory."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Load workspace configuration from ./aml_config/config.json file.\n",
-        "my_workspace = Workspace.from_config()\n",
-        "my_workspace.get_details()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create a Folder to Host All Sample Projects\n",
-        "Finally, create a folder where all the sample projects will be hosted."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "sample_projects_folder = './sample_projects'\n",
-        "\n",
-        "if not os.path.isdir(sample_projects_folder):\n",
-        "    os.mkdir(sample_projects_folder)\n",
-        "    \n",
-        "print('Sample projects will be created in {}.'.format(sample_projects_folder))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Success!\n",
-        "Great, you are ready to move on to the rest of the sample notebooks."
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "savitam"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 00. Configuration\n",
+    "\n",
+    "In this example you will create an Azure Machine Learning `Workspace` object and initialize your notebook directory to easily reload this object from a configuration file.  Typically you will only need to run this once per notebook directory, and all other notebooks in this directory or any sub-directories will automatically use the settings you indicate here.\n",
+    "\n",
+    "\n",
+    "## Prerequisites:\n",
+    "\n",
+    "Before running this notebook, run the `automl_setup` script described in README.md.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Register Machine Learning Services Resource Provider\n",
+    "\n",
+    "Microsoft.MachineLearningServices only needs to be registed once in the subscription.\n",
+    "To register it:\n",
+    "1. Start the Azure portal.\n",
+    "2. Select your `All services` and then `Subscription`.\n",
+    "3. Select the subscription that you want to use.\n",
+    "4. Click on `Resource providers`\n",
+    "3. Click the `Register` link next to Microsoft.MachineLearningServices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Check the Azure ML Core SDK Version to Validate Your Installation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import azureml.core\n",
+    "\n",
+    "print(\"SDK Version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize an Azure ML Workspace\n",
+    "### What is an Azure ML Workspace and Why Do I Need One?\n",
+    "\n",
+    "An Azure ML workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an Azure ML workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, operationalization, and the monitoring of operationalized models.\n",
+    "\n",
+    "\n",
+    "### What do I Need?\n",
+    "\n",
+    "To create or access an Azure ML workspace, you will need to import the Azure ML library and specify following information:\n",
+    "* A name for your workspace. You can choose one.\n",
+    "* Your subscription id. Use the `id` value from the `az account show` command output above.\n",
+    "* The resource group name. The resource group organizes Azure resources and provides a default region for the resources in the group. The resource group will be created if it doesn't exist. Resource groups can be created and viewed in the [Azure portal](https://portal.azure.com)\n",
+    "* Supported regions include `eastus2`, `eastus`,`westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subscription_id = \"<subscription_id>\"\n",
+    "resource_group = \"myrg\"\n",
+    "workspace_name = \"myws\"\n",
+    "workspace_region = \"eastus2\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a Workspace\n",
+    "If you already have access to an Azure ML workspace you want to use, you can skip this cell.  Otherwise, this cell will create an Azure ML workspace for you in the specified subscription, provided you have the correct permissions for the given `subscription_id`.\n",
+    "\n",
+    "This will fail when:\n",
+    "1. The workspace already exists.\n",
+    "2. You do not have permission to create a workspace in the resource group.\n",
+    "3. You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription.\n",
+    "\n",
+    "If workspace creation fails for any reason other than already existing, please work with your IT administrator to provide you with the appropriate permissions or to provision the required resources.\n",
+    "\n",
+    "**Note:** Creation of a new workspace can take several minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import the Workspace class and check the Azure ML SDK version.\n",
+    "from azureml.core import Workspace\n",
+    "\n",
+    "ws = Workspace.create(name = workspace_name,\n",
+    "                      subscription_id = subscription_id,\n",
+    "                      resource_group = resource_group, \n",
+    "                      location = workspace_region)\n",
+    "ws.get_details()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configuring Your Local Environment\n",
+    "You can validate that you have access to the specified workspace and write a configuration file to the default configuration location, `./aml_config/config.json`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace\n",
+    "\n",
+    "ws = Workspace(workspace_name = workspace_name,\n",
+    "               subscription_id = subscription_id,\n",
+    "               resource_group = resource_group)\n",
+    "\n",
+    "# Persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
+    "ws.write_config()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can then load the workspace from this config file from any notebook in the current directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load workspace configuration from ./aml_config/config.json file.\n",
+    "my_workspace = Workspace.from_config()\n",
+    "my_workspace.get_details()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a Folder to Host All Sample Projects\n",
+    "Finally, create a folder where all the sample projects will be hosted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "sample_projects_folder = './sample_projects'\n",
+    "\n",
+    "if not os.path.isdir(sample_projects_folder):\n",
+    "    os.mkdir(sample_projects_folder)\n",
+    "    \n",
+    "print('Sample projects will be created in {}.'.format(sample_projects_folder))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Success!\n",
+    "Great, you are ready to move on to the rest of the sample notebooks."
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

automl/01.auto-ml-classification.ipynb

@@ -1,414 +1,414 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# AutoML 01: Classification with Local Compute\n",
-        "\n",
-        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
-        "\n",
-        "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
-        "\n",
-        "In this notebook you will learn how to:\n",
-        "1. Create an `Experiment` in an existing `Workspace`.\n",
-        "2. Configure AutoML using `AutoMLConfig`.\n",
-        "3. Train the model using local compute.\n",
-        "4. Explore the results.\n",
-        "5. Test the best fitted model.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create an Experiment\n",
-        "\n",
-        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import logging\n",
-        "import os\n",
-        "import random\n",
-        "\n",
-        "from matplotlib import pyplot as plt\n",
-        "from matplotlib.pyplot import imshow\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "from sklearn import datasets\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl.run import AutoMLRun"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "# Choose a name for the experiment and specify the project folder.\n",
-        "experiment_name = 'automl-local-classification'\n",
-        "project_folder = './sample_projects/automl-local-classification'\n",
-        "\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "\n",
-        "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace Name'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "pd.DataFrame(data = output, index = ['']).T"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Diagnostics\n",
-        "\n",
-        "Opt-in diagnostics for better experience, quality, and security of future releases."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.telemetry import set_diagnostics_collection\n",
-        "set_diagnostics_collection(send_diagnostics = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Load Training Data\n",
-        "\n",
-        "This uses scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn import datasets\n",
-        "\n",
-        "digits = datasets.load_digits()\n",
-        "\n",
-        "# Exclude the first 100 rows from training so that they can be used for test.\n",
-        "X_train = digits.data[100:,:]\n",
-        "y_train = digits.target[100:]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Configure AutoML\n",
-        "\n",
-        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
-        "\n",
-        "|Property|Description|\n",
-        "|-|-|\n",
-        "|**task**|classification or regression|\n",
-        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
-        "|**max_time_sec**|Time limit in seconds for each iteration.|\n",
-        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
-        "|**n_cross_validations**|Number of cross validation splits.|\n",
-        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-        "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
-        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             debug_log = 'automl_errors.log',\n",
-        "                             primary_metric = 'AUC_weighted',\n",
-        "                             max_time_sec = 3600,\n",
-        "                             iterations = 50,\n",
-        "                             n_cross_validations = 3,\n",
-        "                             verbosity = logging.INFO,\n",
-        "                             X = X_train, \n",
-        "                             y = y_train,\n",
-        "                             path = project_folder)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train the Models\n",
-        "\n",
-        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
-        "In this example, we specify `show_output = True` to print currently running iterations to the console."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_run = experiment.submit(automl_config, show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_run"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Optionally, you can continue an interrupted local run by calling `continue_experiment` without the `iterations` parameter, or run more iterations for a completed run by specifying the `iterations` parameter:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_run = local_run.continue_experiment(X = X_train, \n",
-        "                                          y = y_train, \n",
-        "                                          show_output = True,\n",
-        "                                          iterations = 5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_run"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explore the Results"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Widget for Monitoring Runs\n",
-        "\n",
-        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
-        "\n",
-        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.widgets import RunDetails\n",
-        "RunDetails(local_run).show() "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n",
-        "#### Retrieve All Child Runs\n",
-        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "children = list(local_run.get_children())\n",
-        "metricslist = {}\n",
-        "for run in children:\n",
-        "    properties = run.get_properties()\n",
-        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
-        "    metricslist[int(properties['iteration'])] = metrics\n",
-        "\n",
-        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
-        "rundata"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Retrieve the Best Model\n",
-        "\n",
-        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "best_run, fitted_model = local_run.get_output()\n",
-        "print(best_run)\n",
-        "print(fitted_model)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Best Model Based on Any Other Metric\n",
-        "Show the run and the model that has the smallest `log_loss` value:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "lookup_metric = \"log_loss\"\n",
-        "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
-        "print(best_run)\n",
-        "print(fitted_model)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Model from a Specific Iteration\n",
-        "Show the run and the model from the third iteration:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "iteration = 3\n",
-        "third_run, third_model = local_run.get_output(iteration = iteration)\n",
-        "print(third_run)\n",
-        "print(third_model)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Test the Best Fitted Model\n",
-        "\n",
-        "#### Load Test Data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "digits = datasets.load_digits()\n",
-        "X_test = digits.data[:10, :]\n",
-        "y_test = digits.target[:10]\n",
-        "images = digits.images[:10]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Testing Our Best Fitted Model\n",
-        "We will try to predict 2 digits and see how our model works."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Randomly select digits and test.\n",
-        "for index in np.random.choice(len(y_test), 2, replace = False):\n",
-        "    print(index)\n",
-        "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
-        "    label = y_test[index]\n",
-        "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
-        "    fig = plt.figure(1, figsize = (3,3))\n",
-        "    ax1 = fig.add_axes((0,0,.8,.8))\n",
-        "    ax1.set_title(title)\n",
-        "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
-        "    plt.show()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "savitam"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 01: Classification with Local Compute\n",
+    "\n",
+    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you will learn how to:\n",
+    "1. Create an `Experiment` in an existing `Workspace`.\n",
+    "2. Configure AutoML using `AutoMLConfig`.\n",
+    "3. Train the model using local compute.\n",
+    "4. Explore the results.\n",
+    "5. Test the best fitted model.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose a name for the experiment and specify the project folder.\n",
+    "experiment_name = 'automl-local-classification'\n",
+    "project_folder = './sample_projects/automl-local-classification'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Training Data\n",
+    "\n",
+    "This uses scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "\n",
+    "digits = datasets.load_digits()\n",
+    "\n",
+    "# Exclude the first 100 rows from training so that they can be used for test.\n",
+    "X_train = digits.data[100:,:]\n",
+    "y_train = digits.target[100:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|classification or regression|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**n_cross_validations**|Number of cross validation splits.|\n",
+    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
+    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             primary_metric = 'AUC_weighted',\n",
+    "                             iteration_timeout_minutes = 60,\n",
+    "                             iterations = 25,\n",
+    "                             n_cross_validations = 3,\n",
+    "                             verbosity = logging.INFO,\n",
+    "                             X = X_train, \n",
+    "                             y = y_train,\n",
+    "                             path = project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
+    "In this example, we specify `show_output = True` to print currently running iterations to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Optionally, you can continue an interrupted local run by calling `continue_experiment` without the `iterations` parameter, or run more iterations for a completed run by specifying the `iterations` parameter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = local_run.continue_experiment(X = X_train, \n",
+    "                                          y = y_train, \n",
+    "                                          show_output = True,\n",
+    "                                          iterations = 5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(local_run).show() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(local_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model that has the smallest `log_loss` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lookup_metric = \"log_loss\"\n",
+    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration\n",
+    "Show the run and the model from the third iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 3\n",
+    "third_run, third_model = local_run.get_output(iteration = iteration)\n",
+    "print(third_run)\n",
+    "print(third_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test the Best Fitted Model\n",
+    "\n",
+    "#### Load Test Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:10, :]\n",
+    "y_test = digits.target[:10]\n",
+    "images = digits.images[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Testing Our Best Fitted Model\n",
+    "We will try to predict 2 digits and see how our model works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Randomly select digits and test.\n",
+    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+    "    print(index)\n",
+    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+    "    label = y_test[index]\n",
+    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
+    "    fig = plt.figure(1, figsize = (3,3))\n",
+    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "    ax1.set_title(title)\n",
+    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "    plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

automl/02.auto-ml-regression.ipynb

@@ -1,415 +1,415 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# AutoML 02: Regression with Local Compute\n",
-        "\n",
-        "In this example we use the scikit-learn's [diabetes dataset](http://scikit-learn.org/stable/datasets/index.html#diabetes-dataset) to showcase how you can use AutoML for a simple regression problem.\n",
-        "\n",
-        "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
-        "\n",
-        "In this notebook you will learn how to:\n",
-        "1. Create an `Experiment` in an existing `Workspace`.\n",
-        "2. Configure AutoML using `AutoMLConfig`.\n",
-        "3. Train the model using local compute.\n",
-        "4. Explore the results.\n",
-        "5. Test the best fitted model.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create an Experiment\n",
-        "\n",
-        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import logging\n",
-        "import os\n",
-        "import random\n",
-        "\n",
-        "from matplotlib import pyplot as plt\n",
-        "from matplotlib.pyplot import imshow\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "from sklearn import datasets\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl.run import AutoMLRun"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "# Choose a name for the experiment and specify the project folder.\n",
-        "experiment_name = 'automl-local-regression'\n",
-        "project_folder = './sample_projects/automl-local-regression'\n",
-        "\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "\n",
-        "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace Name'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "pd.DataFrame(data = output, index = ['']).T"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Diagnostics\n",
-        "\n",
-        "Opt-in diagnostics for better experience, quality, and security of future releases."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.telemetry import set_diagnostics_collection\n",
-        "set_diagnostics_collection(send_diagnostics = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Load Training Data\n",
-        "This uses scikit-learn's [load_diabetes](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html) method."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Load the diabetes dataset, a well-known built-in small dataset that comes with scikit-learn.\n",
-        "from sklearn.datasets import load_diabetes\n",
-        "from sklearn.linear_model import Ridge\n",
-        "from sklearn.metrics import mean_squared_error\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "\n",
-        "X, y = load_diabetes(return_X_y = True)\n",
-        "\n",
-        "columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
-        "\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Configure AutoML\n",
-        "\n",
-        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
-        "\n",
-        "|Property|Description|\n",
-        "|-|-|\n",
-        "|**task**|classification or regression|\n",
-        "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
-        "|**max_time_sec**|Time limit in seconds for each iteration.|\n",
-        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
-        "|**n_cross_validations**|Number of cross validation splits.|\n",
-        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-        "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
-        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_config = AutoMLConfig(task = 'regression',\n",
-        "                             max_time_sec = 600,\n",
-        "                             iterations = 10,\n",
-        "                             primary_metric = 'spearman_correlation',\n",
-        "                             n_cross_validations = 5,\n",
-        "                             debug_log = 'automl.log',\n",
-        "                             verbosity = logging.INFO,\n",
-        "                             X = X_train, \n",
-        "                             y = y_train,\n",
-        "                             path = project_folder)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train the Models\n",
-        "\n",
-        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
-        "In this example, we specify `show_output = True` to print currently running iterations to the console."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_run = experiment.submit(automl_config, show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_run"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explore the Results"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Widget for Monitoring Runs\n",
-        "\n",
-        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
-        "\n",
-        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.widgets import RunDetails\n",
-        "RunDetails(local_run).show() "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n",
-        "#### Retrieve All Child Runs\n",
-        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "children = list(local_run.get_children())\n",
-        "metricslist = {}\n",
-        "for run in children:\n",
-        "    properties = run.get_properties()\n",
-        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
-        "    metricslist[int(properties['iteration'])] = metrics\n",
-        "\n",
-        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
-        "rundata"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Retrieve the Best Model\n",
-        "\n",
-        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "best_run, fitted_model = local_run.get_output()\n",
-        "print(best_run)\n",
-        "print(fitted_model)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Best Model Based on Any Other Metric\n",
-        "Show the run and the model that has the smallest `root_mean_squared_error` value (which turned out to be the same as the one with largest `spearman_correlation` value):"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "lookup_metric = \"root_mean_squared_error\"\n",
-        "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
-        "print(best_run)\n",
-        "print(fitted_model)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Model from a Specific Iteration\n",
-        "Show the run and the model from the third iteration:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "iteration = 3\n",
-        "third_run, third_model = local_run.get_output(iteration = iteration)\n",
-        "print(third_run)\n",
-        "print(third_model)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Test the Best Fitted Model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Predict on training and test set, and calculate residual values."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "y_pred_train = fitted_model.predict(X_train)\n",
-        "y_residual_train = y_train - y_pred_train\n",
-        "\n",
-        "y_pred_test = fitted_model.predict(X_test)\n",
-        "y_residual_test = y_test - y_pred_test"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%matplotlib inline\n",
-        "import matplotlib.pyplot as plt\n",
-        "import numpy as np\n",
-        "from sklearn import datasets\n",
-        "from sklearn.metrics import mean_squared_error, r2_score\n",
-        "\n",
-        "# Set up a multi-plot chart.\n",
-        "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[1, 1], 'wspace':0, 'hspace': 0})\n",
-        "f.suptitle('Regression Residual Values', fontsize = 18)\n",
-        "f.set_figheight(6)\n",
-        "f.set_figwidth(16)\n",
-        "\n",
-        "# Plot residual values of training set.\n",
-        "a0.axis([0, 360, -200, 200])\n",
-        "a0.plot(y_residual_train, 'bo', alpha = 0.5)\n",
-        "a0.plot([-10,360],[0,0], 'r-', lw = 3)\n",
-        "a0.text(16,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_train, y_pred_train))), fontsize = 12)\n",
-        "a0.text(16,140,'R2 score = {0:.2f}'.format(r2_score(y_train, y_pred_train)), fontsize = 12)\n",
-        "a0.set_xlabel('Training samples', fontsize = 12)\n",
-        "a0.set_ylabel('Residual Values', fontsize = 12)\n",
-        "\n",
-        "# Plot a histogram.\n",
-        "a0.hist(y_residual_train, orientation = 'horizontal', color = 'b', bins = 10, histtype = 'step');\n",
-        "a0.hist(y_residual_train, orientation = 'horizontal', color = 'b', alpha = 0.2, bins = 10);\n",
-        "\n",
-        "# Plot residual values of test set.\n",
-        "a1.axis([0, 90, -200, 200])\n",
-        "a1.plot(y_residual_test, 'bo', alpha = 0.5)\n",
-        "a1.plot([-10,360],[0,0], 'r-', lw = 3)\n",
-        "a1.text(5,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_test, y_pred_test))), fontsize = 12)\n",
-        "a1.text(5,140,'R2 score = {0:.2f}'.format(r2_score(y_test, y_pred_test)), fontsize = 12)\n",
-        "a1.set_xlabel('Test samples', fontsize = 12)\n",
-        "a1.set_yticklabels([])\n",
-        "\n",
-        "# Plot a histogram.\n",
-        "a1.hist(y_residual_test, orientation = 'horizontal', color = 'b', bins = 10, histtype = 'step')\n",
-        "a1.hist(y_residual_test, orientation = 'horizontal', color = 'b', alpha = 0.2, bins = 10)\n",
-        "\n",
-        "plt.show()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "savitam"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 02: Regression with Local Compute\n",
+    "\n",
+    "In this example we use the scikit-learn's [diabetes dataset](http://scikit-learn.org/stable/datasets/index.html#diabetes-dataset) to showcase how you can use AutoML for a simple regression problem.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you will learn how to:\n",
+    "1. Create an `Experiment` in an existing `Workspace`.\n",
+    "2. Configure AutoML using `AutoMLConfig`.\n",
+    "3. Train the model using local compute.\n",
+    "4. Explore the results.\n",
+    "5. Test the best fitted model.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose a name for the experiment and specify the project folder.\n",
+    "experiment_name = 'automl-local-regression'\n",
+    "project_folder = './sample_projects/automl-local-regression'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load Training Data\n",
+    "This uses scikit-learn's [load_diabetes](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html) method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the diabetes dataset, a well-known built-in small dataset that comes with scikit-learn.\n",
+    "from sklearn.datasets import load_diabetes\n",
+    "from sklearn.linear_model import Ridge\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X, y = load_diabetes(return_X_y = True)\n",
+    "\n",
+    "columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|classification or regression|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**n_cross_validations**|Number of cross validation splits.|\n",
+    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
+    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'regression',\n",
+    "                             iteration_timeout_minutes = 10,\n",
+    "                             iterations = 10,\n",
+    "                             primary_metric = 'spearman_correlation',\n",
+    "                             n_cross_validations = 5,\n",
+    "                             debug_log = 'automl.log',\n",
+    "                             verbosity = logging.INFO,\n",
+    "                             X = X_train, \n",
+    "                             y = y_train,\n",
+    "                             path = project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
+    "In this example, we specify `show_output = True` to print currently running iterations to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(local_run).show() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(local_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model that has the smallest `root_mean_squared_error` value (which turned out to be the same as the one with largest `spearman_correlation` value):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lookup_metric = \"root_mean_squared_error\"\n",
+    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration\n",
+    "Show the run and the model from the third iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 3\n",
+    "third_run, third_model = local_run.get_output(iteration = iteration)\n",
+    "print(third_run)\n",
+    "print(third_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test the Best Fitted Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Predict on training and test set, and calculate residual values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred_train = fitted_model.predict(X_train)\n",
+    "y_residual_train = y_train - y_pred_train\n",
+    "\n",
+    "y_pred_test = fitted_model.predict(X_test)\n",
+    "y_residual_test = y_test - y_pred_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import datasets\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "\n",
+    "# Set up a multi-plot chart.\n",
+    "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[1, 1], 'wspace':0, 'hspace': 0})\n",
+    "f.suptitle('Regression Residual Values', fontsize = 18)\n",
+    "f.set_figheight(6)\n",
+    "f.set_figwidth(16)\n",
+    "\n",
+    "# Plot residual values of training set.\n",
+    "a0.axis([0, 360, -200, 200])\n",
+    "a0.plot(y_residual_train, 'bo', alpha = 0.5)\n",
+    "a0.plot([-10,360],[0,0], 'r-', lw = 3)\n",
+    "a0.text(16,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_train, y_pred_train))), fontsize = 12)\n",
+    "a0.text(16,140,'R2 score = {0:.2f}'.format(r2_score(y_train, y_pred_train)), fontsize = 12)\n",
+    "a0.set_xlabel('Training samples', fontsize = 12)\n",
+    "a0.set_ylabel('Residual Values', fontsize = 12)\n",
+    "\n",
+    "# Plot a histogram.\n",
+    "a0.hist(y_residual_train, orientation = 'horizontal', color = 'b', bins = 10, histtype = 'step');\n",
+    "a0.hist(y_residual_train, orientation = 'horizontal', color = 'b', alpha = 0.2, bins = 10);\n",
+    "\n",
+    "# Plot residual values of test set.\n",
+    "a1.axis([0, 90, -200, 200])\n",
+    "a1.plot(y_residual_test, 'bo', alpha = 0.5)\n",
+    "a1.plot([-10,360],[0,0], 'r-', lw = 3)\n",
+    "a1.text(5,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_test, y_pred_test))), fontsize = 12)\n",
+    "a1.text(5,140,'R2 score = {0:.2f}'.format(r2_score(y_test, y_pred_test)), fontsize = 12)\n",
+    "a1.set_xlabel('Test samples', fontsize = 12)\n",
+    "a1.set_yticklabels([])\n",
+    "\n",
+    "# Plot a histogram.\n",
+    "a1.hist(y_residual_test, orientation = 'horizontal', color = 'b', bins = 10, histtype = 'step')\n",
+    "a1.hist(y_residual_test, orientation = 'horizontal', color = 'b', alpha = 0.2, bins = 10)\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

automl/03.auto-ml-remote-execution.ipynb

@@ -1,485 +1,485 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# AutoML 03: Remote Execution using DSVM (Ubuntu)\n",
-        "\n",
-        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
-        "\n",
-        "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
-        "\n",
-        "In this notebook you wiil learn how to:\n",
-        "1. Create an `Experiment` in an existing `Workspace`.\n",
-        "2. Attach an existing DSVM to a workspace.\n",
-        "3. Configure AutoML using `AutoMLConfig`.\n",
-        "4. Train the model using the DSVM.\n",
-        "5. Explore the results.\n",
-        "6. Test the best fitted model.\n",
-        "\n",
-        "In addition, this notebook showcases the following features:\n",
-        "- **Parallel** executions for iterations\n",
-        "- **Asynchronous** tracking of progress\n",
-        "- **Cancellation** of individual iterations or the entire run\n",
-        "- Retrieving models for any iteration or logged metric\n",
-        "- Specifying AutoML settings as `**kwargs`\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create an Experiment\n",
-        "\n",
-        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import logging\n",
-        "import os\n",
-        "import random\n",
-        "\n",
-        "from matplotlib import pyplot as plt\n",
-        "from matplotlib.pyplot import imshow\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "from sklearn import datasets\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl.run import AutoMLRun"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "# Choose a name for the run history container in the workspace.\n",
-        "experiment_name = 'automl-remote-dsvm4'\n",
-        "project_folder = './sample_projects/automl-remote-dsvm4'\n",
-        "\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "\n",
-        "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace Name'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "pd.DataFrame(data = output, index = ['']).T"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Diagnostics\n",
-        "\n",
-        "Opt-in diagnostics for better experience, quality, and security of future releases."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.telemetry import set_diagnostics_collection\n",
-        "set_diagnostics_collection(send_diagnostics = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create a Remote Linux DSVM\n",
-        "**Note:** If creation fails with a message about Marketplace purchase eligibilty, start creation of a DSVM through the [Azure portal](https://portal.azure.com), and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled this setting, you can exit the portal without actually creating the DSVM, and creation of the DSVM through the notebook should work.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import DsvmCompute\n",
-        "\n",
-        "dsvm_name = 'mydsvm'\n",
-        "try:\n",
-        "    dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
-        "    print('Found an existing DSVM.')\n",
-        "except:\n",
-        "    print('Creating a new DSVM.')\n",
-        "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
-        "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
-        "    dsvm_compute.wait_for_completion(show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create Get Data File\n",
-        "For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
-        "In this example, the `get_data()` function returns data using scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "if not os.path.exists(project_folder):\n",
-        "    os.makedirs(project_folder)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile $project_folder/get_data.py\n",
-        "\n",
-        "from sklearn import datasets\n",
-        "from scipy import sparse\n",
-        "import numpy as np\n",
-        "\n",
-        "def get_data():\n",
-        "    \n",
-        "    digits = datasets.load_digits()\n",
-        "    X_train = digits.data[100:,:]\n",
-        "    y_train = digits.target[100:]\n",
-        "\n",
-        "    return { \"X\" : X_train, \"y\" : y_train }"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Configure AutoML <a class=\"anchor\" id=\"Instantiate-AutoML-Remote-DSVM\"></a>\n",
-        "\n",
-        "You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
-        "\n",
-        "**Note:** When using Remote DSVM, you can't pass Numpy arrays directly to the fit method.\n",
-        "\n",
-        "|Property|Description|\n",
-        "|-|-|\n",
-        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
-        "|**max_time_sec**|Time limit in seconds for each iteration.|\n",
-        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
-        "|**n_cross_validations**|Number of cross validation splits.|\n",
-        "|**concurrent_iterations**|Maximum number of iterations to execute in parallel. This should be less than the number of cores on the DSVM.|"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_settings = {\n",
-        "    \"max_time_sec\": 600,\n",
-        "    \"iterations\": 20,\n",
-        "    \"n_cross_validations\": 5,\n",
-        "    \"primary_metric\": 'AUC_weighted',\n",
-        "    \"preprocess\": False,\n",
-        "    \"concurrent_iterations\": 2,\n",
-        "    \"verbosity\": logging.INFO\n",
-        "}\n",
-        "\n",
-        "automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             debug_log = 'automl_errors.log',\n",
-        "                             path = project_folder, \n",
-        "                             compute_target = dsvm_compute,\n",
-        "                             data_script = project_folder + \"/get_data.py\",\n",
-        "                             **automl_settings\n",
-        "                            )\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "**Note:** The first run on a new DSVM may take several minutes to prepare the environment."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train the Models\n",
-        "\n",
-        "Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run.\n",
-        "\n",
-        "In this example, we specify `show_output = False` to suppress console output while the run is in progress."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "remote_run = experiment.submit(automl_config, show_output = False)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explore the Results\n",
-        "\n",
-        "#### Loading Executed Runs\n",
-        "In case you need to load a previously executed run, enable the cell below and replace the `run_id` value."
-      ]
-    },
-    {
-      "cell_type": "raw",
-      "metadata": {},
-      "source": [
-        "remote_run = AutoMLRun(experiment=experiment, run_id = 'AutoML_480d3ed6-fc94-44aa-8f4e-0b945db9d3ef')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Widget for Monitoring Runs\n",
-        "\n",
-        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
-        "\n",
-        "You can click on a pipeline to see run properties and output logs.  Logs are also available on the DSVM under `/tmp/azureml_run/{iterationid}/azureml-logs`\n",
-        "\n",
-        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.widgets import RunDetails\n",
-        "RunDetails(remote_run).show() "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Wait until the run finishes.\n",
-        "remote_run.wait_for_completion(show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n",
-        "#### Retrieve All Child Runs\n",
-        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "children = list(remote_run.get_children())\n",
-        "metricslist = {}\n",
-        "for run in children:\n",
-        "    properties = run.get_properties()\n",
-        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
-        "    metricslist[int(properties['iteration'])] = metrics\n",
-        "\n",
-        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
-        "rundata"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Cancelling Runs\n",
-        "\n",
-        "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Cancel the ongoing experiment and stop scheduling new iterations.\n",
-        "# remote_run.cancel()\n",
-        "\n",
-        "# Cancel iteration 1 and move onto iteration 2.\n",
-        "# remote_run.cancel_iteration(1)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Retrieve the Best Model\n",
-        "\n",
-        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "best_run, fitted_model = remote_run.get_output()\n",
-        "print(best_run)\n",
-        "print(fitted_model)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Best Model Based on Any Other Metric\n",
-        "Show the run and the model which has the smallest `log_loss` value:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "lookup_metric = \"log_loss\"\n",
-        "best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
-        "print(best_run)\n",
-        "print(fitted_model)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Model from a Specific Iteration\n",
-        "Show the run and the model from the third iteration:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "iteration = 3\n",
-        "third_run, third_model = remote_run.get_output(iteration = iteration)\n",
-        "print(third_run)\n",
-        "print(third_model)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Test the Best Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n",
-        "\n",
-        "#### Load Test Data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "digits = datasets.load_digits()\n",
-        "X_test = digits.data[:10, :]\n",
-        "y_test = digits.target[:10]\n",
-        "images = digits.images[:10]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Test Our Best Fitted Model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Randomly select digits and test.\n",
-        "for index in np.random.choice(len(y_test), 2, replace = False):\n",
-        "    print(index)\n",
-        "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
-        "    label = y_test[index]\n",
-        "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
-        "    fig = plt.figure(1, figsize=(3,3))\n",
-        "    ax1 = fig.add_axes((0,0,.8,.8))\n",
-        "    ax1.set_title(title)\n",
-        "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
-        "    plt.show()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "savitam"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 03: Remote Execution using DSVM (Ubuntu)\n",
+    "\n",
+    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you wiil learn how to:\n",
+    "1. Create an `Experiment` in an existing `Workspace`.\n",
+    "2. Attach an existing DSVM to a workspace.\n",
+    "3. Configure AutoML using `AutoMLConfig`.\n",
+    "4. Train the model using the DSVM.\n",
+    "5. Explore the results.\n",
+    "6. Test the best fitted model.\n",
+    "\n",
+    "In addition, this notebook showcases the following features:\n",
+    "- **Parallel** executions for iterations\n",
+    "- **Asynchronous** tracking of progress\n",
+    "- **Cancellation** of individual iterations or the entire run\n",
+    "- Retrieving models for any iteration or logged metric\n",
+    "- Specifying AutoML settings as `**kwargs`\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose a name for the run history container in the workspace.\n",
+    "experiment_name = 'automl-remote-dsvm4'\n",
+    "project_folder = './sample_projects/automl-remote-dsvm4'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a Remote Linux DSVM\n",
+    "**Note:** If creation fails with a message about Marketplace purchase eligibilty, start creation of a DSVM through the [Azure portal](https://portal.azure.com), and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled this setting, you can exit the portal without actually creating the DSVM, and creation of the DSVM through the notebook should work.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import DsvmCompute\n",
+    "\n",
+    "dsvm_name = 'mydsvma'\n",
+    "try:\n",
+    "    dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
+    "    print('Found an existing DSVM.')\n",
+    "except:\n",
+    "    print('Creating a new DSVM.')\n",
+    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
+    "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
+    "    dsvm_compute.wait_for_completion(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Get Data File\n",
+    "For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
+    "In this example, the `get_data()` function returns data using scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(project_folder):\n",
+    "    os.makedirs(project_folder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile $project_folder/get_data.py\n",
+    "\n",
+    "from sklearn import datasets\n",
+    "from scipy import sparse\n",
+    "import numpy as np\n",
+    "\n",
+    "def get_data():\n",
+    "    \n",
+    "    digits = datasets.load_digits()\n",
+    "    X_train = digits.data[100:,:]\n",
+    "    y_train = digits.target[100:]\n",
+    "\n",
+    "    return { \"X\" : X_train, \"y\" : y_train }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML <a class=\"anchor\" id=\"Instantiate-AutoML-Remote-DSVM\"></a>\n",
+    "\n",
+    "You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
+    "\n",
+    "**Note:** When using Remote DSVM, you can't pass Numpy arrays directly to the fit method.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**n_cross_validations**|Number of cross validation splits.|\n",
+    "|**max_concurrent_iterations**|Maximum number of iterations to execute in parallel. This should be less than the number of cores on the DSVM.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_settings = {\n",
+    "    \"iteration_timeout_minutes\": 10,\n",
+    "    \"iterations\": 20,\n",
+    "    \"n_cross_validations\": 5,\n",
+    "    \"primary_metric\": 'AUC_weighted',\n",
+    "    \"preprocess\": False,\n",
+    "    \"max_concurrent_iterations\": 2,\n",
+    "    \"verbosity\": logging.INFO\n",
+    "}\n",
+    "\n",
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             path = project_folder, \n",
+    "                             compute_target = dsvm_compute,\n",
+    "                             data_script = project_folder + \"/get_data.py\",\n",
+    "                             **automl_settings\n",
+    "                            )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note:** The first run on a new DSVM may take several minutes to prepare the environment."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run.\n",
+    "\n",
+    "In this example, we specify `show_output = False` to suppress console output while the run is in progress."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remote_run = experiment.submit(automl_config, show_output = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results\n",
+    "\n",
+    "#### Loading Executed Runs\n",
+    "In case you need to load a previously executed run, enable the cell below and replace the `run_id` value."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "remote_run = AutoMLRun(experiment=experiment, run_id = 'AutoML_480d3ed6-fc94-44aa-8f4e-0b945db9d3ef')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "You can click on a pipeline to see run properties and output logs.  Logs are also available on the DSVM under `/tmp/azureml_run/{iterationid}/azureml-logs`\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(remote_run).show() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Wait until the run finishes.\n",
+    "remote_run.wait_for_completion(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(remote_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cancelling Runs\n",
+    "\n",
+    "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cancel the ongoing experiment and stop scheduling new iterations.\n",
+    "# remote_run.cancel()\n",
+    "\n",
+    "# Cancel iteration 1 and move onto iteration 2.\n",
+    "# remote_run.cancel_iteration(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = remote_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model which has the smallest `log_loss` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lookup_metric = \"log_loss\"\n",
+    "best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration\n",
+    "Show the run and the model from the third iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 3\n",
+    "third_run, third_model = remote_run.get_output(iteration = iteration)\n",
+    "print(third_run)\n",
+    "print(third_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test the Best Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n",
+    "\n",
+    "#### Load Test Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:10, :]\n",
+    "y_test = digits.target[:10]\n",
+    "images = digits.images[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Test Our Best Fitted Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Randomly select digits and test.\n",
+    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+    "    print(index)\n",
+    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+    "    label = y_test[index]\n",
+    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
+    "    fig = plt.figure(1, figsize=(3,3))\n",
+    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "    ax1.set_title(title)\n",
+    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "    plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

automl/04.auto-ml-remote-execution-text-data-blob-store.ipynb

@@ -1,491 +1,501 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Auto ML 04: Remote Execution with Text Data from Azure Blob Storage\n",
-        "\n",
-        "In this example we use the [Burning Man 2016 dataset](https://innovate.burningman.org/datasets-page/) to showcase how you can use AutoML to handle text data from Azure Blob Storage.\n",
-        "\n",
-        "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
-        "\n",
-        "In this notebook you will learn how to:\n",
-        "1. Create an `Experiment` in an existing `Workspace`.\n",
-        "2. Attach an existing DSVM to a workspace.\n",
-        "3. Configure AutoML using `AutoMLConfig`.\n",
-        "4. Train the model using the DSVM.\n",
-        "5. Explore the results.\n",
-        "6. Test the best fitted model.\n",
-        "\n",
-        "In addition this notebook showcases the following features\n",
-        "- **Parallel** executions for iterations\n",
-        "- **Asynchronous** tracking of progress\n",
-        "- **Cancellation** of individual iterations or the entire run\n",
-        "- Retrieving models for any iteration or logged metric\n",
-        "- Specifying AutoML settings as `**kwargs`\n",
-        "- Handling **text** data using the `preprocess` flag\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create an Experiment\n",
-        "\n",
-        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import logging\n",
-        "import os\n",
-        "import random\n",
-        "\n",
-        "from matplotlib import pyplot as plt\n",
-        "from matplotlib.pyplot import imshow\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "from sklearn import datasets\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl.run import AutoMLRun"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "# Choose a name for the run history container in the workspace.\n",
-        "experiment_name = 'automl-remote-dsvm-blobstore'\n",
-        "project_folder = './sample_projects/automl-remote-dsvm-blobstore'\n",
-        "\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "\n",
-        "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "pd.DataFrame(data=output, index=['']).T"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Diagnostics\n",
-        "\n",
-        "Opt-in diagnostics for better experience, quality, and security of future releases."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.telemetry import set_diagnostics_collection\n",
-        "set_diagnostics_collection(send_diagnostics = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Attach a Remote Linux DSVM\n",
-        "To use a remote Docker compute target:\n",
-        "1. Create a Linux DSVM in Azure, following these [quick instructions](https://docs.microsoft.com/en-us/azure/machine-learning/desktop-workbench/how-to-create-dsvm-hdi). Make sure you use the Ubuntu flavor (not CentOS). Make sure that disk space is available under `/tmp` because AutoML creates files under `/tmp/azureml_run`s. The DSVM should have more cores than the number of parallel runs that you plan to enable. It should also have at least 4GB per core.\n",
-        "2. Enter the IP address, user name and password below.\n",
-        "\n",
-        "**Note:** By default, SSH runs on port 22 and you don't need to change the port number below. If you've configured SSH to use a different port, change `dsvm_ssh_port` accordinglyaddress. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on changing SSH ports for security reasons."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import RemoteCompute\n",
-        "import time\n",
-        "\n",
-        "# Add your VM information below\n",
-        "# If a compute with the specified compute_name already exists, it will be used and the dsvm_ip_addr, dsvm_ssh_port, \n",
-        "# dsvm_username and dsvm_password will be ignored.\n",
-        "compute_name  = 'mydsvm'\n",
-        "dsvm_ip_addr  = '<<ip_addr>>'\n",
-        "dsvm_ssh_port = 22\n",
-        "dsvm_username = '<<username>>'\n",
-        "dsvm_password = '<<password>>'\n",
-        "\n",
-        "if compute_name in ws.compute_targets():\n",
-        "    print('Using existing compute.')\n",
-        "    dsvm_compute = ws.compute_targets()[compute_name]\n",
-        "else:\n",
-        "    RemoteCompute.attach(workspace=ws, name=compute_name, address=dsvm_ip_addr, username=dsvm_username, password=dsvm_password, ssh_port=dsvm_ssh_port)\n",
-        "\n",
-        "    while ws.compute_targets()[compute_name].provisioning_state == 'Creating':\n",
-        "        time.sleep(1)\n",
-        "\n",
-        "    dsvm_compute = ws.compute_targets()[compute_name]\n",
-        "    \n",
-        "    if dsvm_compute.provisioning_state == 'Failed':\n",
-        "        print('Attached failed.')\n",
-        "        print(dsvm_compute.provisioning_errors)\n",
-        "        dsvm_compute.delete()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create Get Data File\n",
-        "For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
-        "In this example, the `get_data()` function returns a [dictionary](README.md#getdata)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "if not os.path.exists(project_folder):\n",
-        "    os.makedirs(project_folder)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile $project_folder/get_data.py\n",
-        "\n",
-        "import pandas as pd\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from sklearn.preprocessing import LabelEncoder\n",
-        "\n",
-        "def get_data():\n",
-        "    # Load Burning Man 2016 data.\n",
-        "    df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
-        "                     delimiter=\"\\t\", quotechar='\"')\n",
-        "    # Get integer labels.\n",
-        "    le = LabelEncoder()\n",
-        "    le.fit(df[\"Label\"].values)\n",
-        "    y = le.transform(df[\"Label\"].values)\n",
-        "    X = df.drop([\"Label\"], axis=1)\n",
-        "\n",
-        "    X_train, _, y_train, _ = train_test_split(X, y, test_size = 0.1, random_state = 42)\n",
-        "\n",
-        "    return { \"X\" : X_train, \"y\" : y_train }"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### View data\n",
-        "\n",
-        "You can execute the `get_data()` function locally to view the training data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%run  $project_folder/get_data.py\n",
-        "data_dict = get_data()\n",
-        "df = data_dict[\"X\"]\n",
-        "y = data_dict[\"y\"]\n",
-        "pd.set_option('display.max_colwidth', 15)\n",
-        "df['Label'] = pd.Series(y, index=df.index)\n",
-        "df.head()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Configure AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
-        "\n",
-        "You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
-        "\n",
-        "**Note:** When using Remote DSVM, you can't pass Numpy arrays directly to the fit method.\n",
-        "\n",
-        "|Property|Description|\n",
-        "|-|-|\n",
-        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
-        "|**max_time_sec**|Time limit in seconds for each iteration.|\n",
-        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
-        "|**n_cross_validations**|Number of cross validation splits.|\n",
-        "|**concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of cores on the DSVM.|\n",
-        "|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.|\n",
-        "|**max_cores_per_iteration**|Indicates how many cores on the compute target would be used to train a single pipeline.<br>Default is *1*; you can set it to *-1* to use all cores.|"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_settings = {\n",
-        "    \"max_time_sec\": 3600,\n",
-        "    \"iterations\": 10,\n",
-        "    \"n_cross_validations\": 5,\n",
-        "    \"primary_metric\": 'AUC_weighted',\n",
-        "    \"preprocess\": True,\n",
-        "    \"max_cores_per_iteration\": 2\n",
-        "}\n",
-        "\n",
-        "automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             path = project_folder,\n",
-        "                             compute_target = dsvm_compute,\n",
-        "                             data_script = project_folder + \"/get_data.py\",\n",
-        "                             **automl_settings\n",
-        "                            )\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train the Models <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
-        "\n",
-        "Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "remote_run = experiment.submit(automl_config)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
-        "#### Widget for Monitoring Runs\n",
-        "\n",
-        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
-        "\n",
-        "You can click on a pipeline to see run properties and output logs.  Logs are also available on the DSVM under `/tmp/azureml_run/{iterationid}/azureml-logs`\n",
-        "\n",
-        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.widgets import RunDetails\n",
-        "RunDetails(remote_run).show() "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n",
-        "#### Retrieve All Child Runs\n",
-        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "children = list(remote_run.get_children())\n",
-        "metricslist = {}\n",
-        "for run in children:\n",
-        "    properties = run.get_properties()\n",
-        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
-        "    metricslist[int(properties['iteration'])] = metrics\n",
-        "\n",
-        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
-        "rundata"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Cancelling Runs\n",
-        "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Cancel the ongoing experiment and stop scheduling new iterations.\n",
-        "remote_run.cancel()\n",
-        "\n",
-        "# Cancel iteration 1 and move onto iteration 2.\n",
-        "# remote_run.cancel_iteration(1)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Retrieve the Best Model\n",
-        "\n",
-        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "best_run, fitted_model = remote_run.get_output()\n",
-        "print(best_run)\n",
-        "print(fitted_model)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Best Model Based on Any Other Metric\n",
-        "Show the run and the model which has the smallest `accuracy` value:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# lookup_metric = \"accuracy\"\n",
-        "# best_run, fitted_model = remote_run.get_output(metric = lookup_metric)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Model from a Specific Iteration"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "iteration = 0\n",
-        "zero_run, zero_model = remote_run.get_output(iteration = iteration)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import sklearn\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from sklearn.preprocessing import LabelEncoder\n",
-        "from pandas_ml import ConfusionMatrix\n",
-        "\n",
-        "df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
-        "                     delimiter=\"\\t\", quotechar='\"')\n",
-        "\n",
-        "# get integer labels\n",
-        "le = LabelEncoder()\n",
-        "le.fit(df[\"Label\"].values)\n",
-        "y = le.transform(df[\"Label\"].values)\n",
-        "X = df.drop([\"Label\"], axis=1)\n",
-        "\n",
-        "_, X_test, _, y_test = train_test_split(X, y, test_size=0.1, random_state=42)\n",
-        "\n",
-        "\n",
-        "ypred = fitted_model.predict(X_test.values)\n",
-        "\n",
-        "\n",
-        "ypred_strings = le.inverse_transform(ypred)\n",
-        "ytest_strings = le.inverse_transform(y_test)\n",
-        "\n",
-        "cm = ConfusionMatrix(ytest_strings, ypred_strings)\n",
-        "\n",
-        "print(cm)\n",
-        "\n",
-        "cm.plot()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "savitam"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Auto ML 04: Remote Execution with Text Data from Azure Blob Storage\n",
+    "\n",
+    "In this example we use the [Burning Man 2016 dataset](https://innovate.burningman.org/datasets-page/) to showcase how you can use AutoML to handle text data from Azure Blob Storage.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you will learn how to:\n",
+    "1. Create an `Experiment` in an existing `Workspace`.\n",
+    "2. Attach an existing DSVM to a workspace.\n",
+    "3. Configure AutoML using `AutoMLConfig`.\n",
+    "4. Train the model using the DSVM.\n",
+    "5. Explore the results.\n",
+    "6. Test the best fitted model.\n",
+    "\n",
+    "In addition this notebook showcases the following features\n",
+    "- **Parallel** executions for iterations\n",
+    "- **Asynchronous** tracking of progress\n",
+    "- **Cancellation** of individual iterations or the entire run\n",
+    "- Retrieving models for any iteration or logged metric\n",
+    "- Specifying AutoML settings as `**kwargs`\n",
+    "- Handling **text** data using the `preprocess` flag\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose a name for the run history container in the workspace.\n",
+    "experiment_name = 'automl-remote-dsvm-blobstore'\n",
+    "project_folder = './sample_projects/automl-remote-dsvm-blobstore'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data=output, index=['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Attach a Remote Linux DSVM\n",
+    "To use a remote Docker compute target:\n",
+    "1. Create a Linux DSVM in Azure, following these [quick instructions](https://docs.microsoft.com/en-us/azure/machine-learning/desktop-workbench/how-to-create-dsvm-hdi). Make sure you use the Ubuntu flavor (not CentOS). Make sure that disk space is available under `/tmp` because AutoML creates files under `/tmp/azureml_run`s. The DSVM should have more cores than the number of parallel runs that you plan to enable. It should also have at least 4GB per core.\n",
+    "2. Enter the IP address, user name and password below.\n",
+    "\n",
+    "**Note:** By default, SSH runs on port 22 and you don't need to change the port number below. If you've configured SSH to use a different port, change `dsvm_ssh_port` accordinglyaddress. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on changing SSH ports for security reasons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import RemoteCompute\n",
+    "import time\n",
+    "\n",
+    "# Add your VM information below\n",
+    "# If a compute with the specified compute_name already exists, it will be used and the dsvm_ip_addr, dsvm_ssh_port, \n",
+    "# dsvm_username and dsvm_password will be ignored.\n",
+    "compute_name  = 'mydsvmb'\n",
+    "dsvm_ip_addr  = '<<ip_addr>>'\n",
+    "dsvm_ssh_port = 22\n",
+    "dsvm_username = '<<username>>'\n",
+    "dsvm_password = '<<password>>'\n",
+    "\n",
+    "if compute_name in ws.compute_targets:\n",
+    "    print('Using existing compute.')\n",
+    "    dsvm_compute = ws.compute_targets[compute_name]\n",
+    "else:\n",
+    "    RemoteCompute.attach(workspace=ws, name=compute_name, address=dsvm_ip_addr, username=dsvm_username, password=dsvm_password, ssh_port=dsvm_ssh_port)\n",
+    "\n",
+    "    while ws.compute_targets[compute_name].provisioning_state == 'Creating':\n",
+    "        time.sleep(1)\n",
+    "\n",
+    "    dsvm_compute = ws.compute_targets[compute_name]\n",
+    "    \n",
+    "    if dsvm_compute.provisioning_state == 'Failed':\n",
+    "        print('Attached failed.')\n",
+    "        print(dsvm_compute.provisioning_errors)\n",
+    "        dsvm_compute.delete()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Get Data File\n",
+    "For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
+    "In this example, the `get_data()` function returns a [dictionary](README.md#getdata)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(project_folder):\n",
+    "    os.makedirs(project_folder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile $project_folder/get_data.py\n",
+    "\n",
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "def get_data():\n",
+    "    # Load Burning Man 2016 data.\n",
+    "    df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
+    "                     delimiter=\"\\t\", quotechar='\"')\n",
+    "    # Get integer labels.\n",
+    "    le = LabelEncoder()\n",
+    "    le.fit(df[\"Label\"].values)\n",
+    "    y = le.transform(df[\"Label\"].values)\n",
+    "    X = df.drop([\"Label\"], axis=1)\n",
+    "\n",
+    "    X_train, _, y_train, _ = train_test_split(X, y, test_size = 0.1, random_state = 42)\n",
+    "\n",
+    "    return { \"X\" : X_train, \"y\" : y_train }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### View data\n",
+    "\n",
+    "You can execute the `get_data()` function locally to view the training data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%run  $project_folder/get_data.py\n",
+    "data_dict = get_data()\n",
+    "df = data_dict[\"X\"]\n",
+    "y = data_dict[\"y\"]\n",
+    "pd.set_option('display.max_colwidth', 15)\n",
+    "df['Label'] = pd.Series(y, index=df.index)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
+    "\n",
+    "You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
+    "\n",
+    "**Note:** When using Remote DSVM, you can't pass Numpy arrays directly to the fit method.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**n_cross_validations**|Number of cross validation splits.|\n",
+    "|**max_concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of cores on the DSVM.|\n",
+    "|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.|\n",
+    "|**max_cores_per_iteration**|Indicates how many cores on the compute target would be used to train a single pipeline.<br>Default is *1*; you can set it to *-1* to use all cores.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_settings = {\n",
+    "    \"iteration_timeout_minutes\": 60,\n",
+    "    \"iterations\": 4,\n",
+    "    \"n_cross_validations\": 5,\n",
+    "    \"primary_metric\": 'AUC_weighted',\n",
+    "    \"preprocess\": True,\n",
+    "    \"max_cores_per_iteration\": 2\n",
+    "}\n",
+    "\n",
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             path = project_folder,\n",
+    "                             compute_target = dsvm_compute,\n",
+    "                             data_script = project_folder + \"/get_data.py\",\n",
+    "                             **automl_settings\n",
+    "                            )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remote_run = experiment.submit(automl_config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "You can click on a pipeline to see run properties and output logs.  Logs are also available on the DSVM under `/tmp/azureml_run/{iterationid}/azureml-logs`\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(remote_run).show() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Wait until the run finishes.\n",
+    "remote_run.wait_for_completion(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(remote_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cancelling Runs\n",
+    "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cancel the ongoing experiment and stop scheduling new iterations.\n",
+    "remote_run.cancel()\n",
+    "\n",
+    "# Cancel iteration 1 and move onto iteration 2.\n",
+    "# remote_run.cancel_iteration(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = remote_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model which has the smallest `accuracy` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lookup_metric = \"accuracy\"\n",
+    "# best_run, fitted_model = remote_run.get_output(metric = lookup_metric)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 0\n",
+    "zero_run, zero_model = remote_run.get_output(iteration = iteration)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sklearn\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from pandas_ml import ConfusionMatrix\n",
+    "\n",
+    "df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
+    "                     delimiter=\"\\t\", quotechar='\"')\n",
+    "\n",
+    "# get integer labels\n",
+    "le = LabelEncoder()\n",
+    "le.fit(df[\"Label\"].values)\n",
+    "y = le.transform(df[\"Label\"].values)\n",
+    "X = df.drop([\"Label\"], axis=1)\n",
+    "\n",
+    "_, X_test, _, y_test = train_test_split(X, y, test_size=0.1, random_state=42)\n",
+    "\n",
+    "\n",
+    "ypred = fitted_model.predict(X_test.values)\n",
+    "\n",
+    "\n",
+    "ypred_strings = le.inverse_transform(ypred)\n",
+    "ytest_strings = le.inverse_transform(y_test)\n",
+    "\n",
+    "cm = ConfusionMatrix(ytest_strings, ypred_strings)\n",
+    "\n",
+    "print(cm)\n",
+    "\n",
+    "cm.plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

automl/05.auto-ml-missing-data-Blacklist-Early-Termination.ipynb

@@ -1,381 +1,381 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# AutoML 05: Blacklisting Models, Early Termination, and Handling Missing Data\n",
-        "\n",
-        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for handling missing values in data. We also provide a stopping metric indicating a target for the primary metrics so that AutoML can terminate the run without necessarly going through all the iterations. Finally, if you want to avoid a certain pipeline, we allow you to specify a blacklist of algorithms that AutoML will ignore for this run.\n",
-        "\n",
-        "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
-        "\n",
-        "In this notebook you will learn how to:\n",
-        "1. Create an `Experiment` in an existing `Workspace`.\n",
-        "2. Configure AutoML using `AutoMLConfig`.\n",
-        "4. Train the model.\n",
-        "5. Explore the results.\n",
-        "6. Test the best fitted model.\n",
-        "\n",
-        "In addition this notebook showcases the following features\n",
-        "- **Blacklisting** certain pipelines\n",
-        "- Specifying **target metrics** to indicate stopping criteria\n",
-        "- Handling **missing data** in the input\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create an Experiment\n",
-        "\n",
-        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import logging\n",
-        "import os\n",
-        "import random\n",
-        "\n",
-        "from matplotlib import pyplot as plt\n",
-        "from matplotlib.pyplot import imshow\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "from sklearn import datasets\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl.run import AutoMLRun"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "# Choose a name for the experiment.\n",
-        "experiment_name = 'automl-local-missing-data'\n",
-        "project_folder = './sample_projects/automl-local-missing-data'\n",
-        "\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "\n",
-        "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "pd.DataFrame(data=output, index=['']).T"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Diagnostics\n",
-        "\n",
-        "Opt-in diagnostics for better experience, quality, and security of future releases."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.telemetry import set_diagnostics_collection\n",
-        "set_diagnostics_collection(send_diagnostics = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Creating missing data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from scipy import sparse\n",
-        "\n",
-        "digits = datasets.load_digits()\n",
-        "X_train = digits.data[10:,:]\n",
-        "y_train = digits.target[10:]\n",
-        "\n",
-        "# Add missing values in 75% of the lines.\n",
-        "missing_rate = 0.75\n",
-        "n_missing_samples = int(np.floor(X_train.shape[0] * missing_rate))\n",
-        "missing_samples = np.hstack((np.zeros(X_train.shape[0] - n_missing_samples, dtype=np.bool), np.ones(n_missing_samples, dtype=np.bool)))\n",
-        "rng = np.random.RandomState(0)\n",
-        "rng.shuffle(missing_samples)\n",
-        "missing_features = rng.randint(0, X_train.shape[1], n_missing_samples)\n",
-        "X_train[np.where(missing_samples)[0], missing_features] = np.nan"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "df = pd.DataFrame(data = X_train)\n",
-        "df['Label'] = pd.Series(y_train, index=df.index)\n",
-        "df.head()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Configure AutoML\n",
-        "\n",
-        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.  This includes setting `exit_score`, which should cause the run to complete before the `iterations` count is reached.\n",
-        "\n",
-        "|Property|Description|\n",
-        "|-|-|\n",
-        "|**task**|classification or regression|\n",
-        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
-        "|**max_time_sec**|Time limit in seconds for each iteration.|\n",
-        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
-        "|**n_cross_validations**|Number of cross validation splits.|\n",
-        "|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.|\n",
-        "|**exit_score**|*double* value indicating the target for *primary_metric*. <br>Once the target is surpassed the run terminates.|\n",
-        "|**blacklist_algos**|*List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run.<br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGDClassifierWrapper</i><br><i>NBWrapper</i><br><i>BernoulliNB</i><br><i>SVCWrapper</i><br><i>LinearSVMWrapper</i><br><i>KNeighborsClassifier</i><br><i>DecisionTreeClassifier</i><br><i>RandomForestClassifier</i><br><i>ExtraTreesClassifier</i><br><i>LightGBMClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet<i><br><i>GradientBoostingRegressor<i><br><i>DecisionTreeRegressor<i><br><i>KNeighborsRegressor<i><br><i>LassoLars<i><br><i>SGDRegressor<i><br><i>RandomForestRegressor<i><br><i>ExtraTreesRegressor<i>|\n",
-        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-        "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers.|\n",
-        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             debug_log = 'automl_errors.log',\n",
-        "                             primary_metric = 'AUC_weighted',\n",
-        "                             max_time_sec = 3600,\n",
-        "                             iterations = 20,\n",
-        "                             n_cross_validations = 5,\n",
-        "                             preprocess = True,\n",
-        "                             exit_score = 0.9984,\n",
-        "                             blacklist_algos = ['KNeighborsClassifier','LinearSVMWrapper'],\n",
-        "                             verbosity = logging.INFO,\n",
-        "                             X = X_train, \n",
-        "                             y = y_train,\n",
-        "                             path = project_folder)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train the Models\n",
-        "\n",
-        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
-        "In this example, we specify `show_output = True` to print currently running iterations to the console."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_run = experiment.submit(automl_config, show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explore the Results"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Widget for Monitoring Runs\n",
-        "\n",
-        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
-        "\n",
-        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.widgets import RunDetails\n",
-        "RunDetails(local_run).show() "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n",
-        "#### Retrieve All Child Runs\n",
-        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "children = list(local_run.get_children())\n",
-        "metricslist = {}\n",
-        "for run in children:\n",
-        "    properties = run.get_properties()\n",
-        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
-        "    metricslist[int(properties['iteration'])] = metrics\n",
-        "\n",
-        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
-        "rundata"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Retrieve the Best Model\n",
-        "\n",
-        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "best_run, fitted_model = local_run.get_output()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Best Model Based on Any Other Metric\n",
-        "Show the run and the model which has the smallest `accuracy` value:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# lookup_metric = \"accuracy\"\n",
-        "# best_run, fitted_model = local_run.get_output(metric = lookup_metric)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Model from a Specific Iteration\n",
-        "Show the run and the model from the third iteration:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# iteration = 3\n",
-        "# best_run, fitted_model = local_run.get_output(iteration = iteration)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Testing the best Fitted Model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "digits = datasets.load_digits()\n",
-        "X_test = digits.data[:10, :]\n",
-        "y_test = digits.target[:10]\n",
-        "images = digits.images[:10]\n",
-        "\n",
-        "# Randomly select digits and test.\n",
-        "for index in np.random.choice(len(y_test), 2, replace = False):\n",
-        "    print(index)\n",
-        "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
-        "    label = y_test[index]\n",
-        "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
-        "    fig = plt.figure(1, figsize=(3,3))\n",
-        "    ax1 = fig.add_axes((0,0,.8,.8))\n",
-        "    ax1.set_title(title)\n",
-        "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
-        "    plt.show()\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "savitam"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 05: Blacklisting Models, Early Termination, and Handling Missing Data\n",
+    "\n",
+    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for handling missing values in data. We also provide a stopping metric indicating a target for the primary metrics so that AutoML can terminate the run without necessarly going through all the iterations. Finally, if you want to avoid a certain pipeline, we allow you to specify a blacklist of algorithms that AutoML will ignore for this run.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you will learn how to:\n",
+    "1. Create an `Experiment` in an existing `Workspace`.\n",
+    "2. Configure AutoML using `AutoMLConfig`.\n",
+    "4. Train the model.\n",
+    "5. Explore the results.\n",
+    "6. Test the best fitted model.\n",
+    "\n",
+    "In addition this notebook showcases the following features\n",
+    "- **Blacklisting** certain pipelines\n",
+    "- Specifying **target metrics** to indicate stopping criteria\n",
+    "- Handling **missing data** in the input\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose a name for the experiment.\n",
+    "experiment_name = 'automl-local-missing-data'\n",
+    "project_folder = './sample_projects/automl-local-missing-data'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data=output, index=['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating missing data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy import sparse\n",
+    "\n",
+    "digits = datasets.load_digits()\n",
+    "X_train = digits.data[10:,:]\n",
+    "y_train = digits.target[10:]\n",
+    "\n",
+    "# Add missing values in 75% of the lines.\n",
+    "missing_rate = 0.75\n",
+    "n_missing_samples = int(np.floor(X_train.shape[0] * missing_rate))\n",
+    "missing_samples = np.hstack((np.zeros(X_train.shape[0] - n_missing_samples, dtype=np.bool), np.ones(n_missing_samples, dtype=np.bool)))\n",
+    "rng = np.random.RandomState(0)\n",
+    "rng.shuffle(missing_samples)\n",
+    "missing_features = rng.randint(0, X_train.shape[1], n_missing_samples)\n",
+    "X_train[np.where(missing_samples)[0], missing_features] = np.nan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(data = X_train)\n",
+    "df['Label'] = pd.Series(y_train, index=df.index)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.  This includes setting `experiment_exit_score`, which should cause the run to complete before the `iterations` count is reached.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|classification or regression|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**n_cross_validations**|Number of cross validation splits.|\n",
+    "|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.|\n",
+    "|**experiment_exit_score**|*double* value indicating the target for *primary_metric*. <br>Once the target is surpassed the run terminates.|\n",
+    "|**blacklist_models**|*List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run.<br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGD</i><br><i>MultinomialNaiveBayes</i><br><i>BernoulliNaiveBayes</i><br><i>SVM</i><br><i>LinearSVM</i><br><i>KNN</i><br><i>DecisionTree</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>GradientBoosting</i><br><i>TensorFlowDNN</i><br><i>TensorFlowLinearClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i>|\n",
+    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers.|\n",
+    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             primary_metric = 'AUC_weighted',\n",
+    "                             iteration_timeout_minutes = 60,\n",
+    "                             iterations = 20,\n",
+    "                             n_cross_validations = 5,\n",
+    "                             preprocess = True,\n",
+    "                             experiment_exit_score = 0.9984,\n",
+    "                             blacklist_models = ['KNN','LinearSVM'],\n",
+    "                             verbosity = logging.INFO,\n",
+    "                             X = X_train, \n",
+    "                             y = y_train,\n",
+    "                             path = project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
+    "In this example, we specify `show_output = True` to print currently running iterations to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(local_run).show() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(local_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model which has the smallest `accuracy` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lookup_metric = \"accuracy\"\n",
+    "# best_run, fitted_model = local_run.get_output(metric = lookup_metric)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration\n",
+    "Show the run and the model from the third iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# iteration = 3\n",
+    "# best_run, fitted_model = local_run.get_output(iteration = iteration)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing the best Fitted Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:10, :]\n",
+    "y_test = digits.target[:10]\n",
+    "images = digits.images[:10]\n",
+    "\n",
+    "# Randomly select digits and test.\n",
+    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+    "    print(index)\n",
+    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+    "    label = y_test[index]\n",
+    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
+    "    fig = plt.figure(1, figsize=(3,3))\n",
+    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "    ax1.set_title(title)\n",
+    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "    plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

automl/06.auto-ml-sparse-data-custom-cv-split.ipynb

@@ -1,384 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# AutoML 06: Custom CV Splits and Handling Sparse Data\n",
-        "\n",
-        "In this example we use the scikit-learn's [20newsgroup](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_20newsgroups.html) to showcase how you can use AutoML for handling sparse data and how to specify custom cross validations splits.\n",
-        "\n",
-        "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
-        "\n",
-        "In this notebook you will learn how to:\n",
-        "1. Create an `Experiment` in an existing `Workspace`.\n",
-        "2. Configure AutoML using `AutoMLConfig`.\n",
-        "4. Train the model.\n",
-        "5. Explore the results.\n",
-        "6. Test the best fitted model.\n",
-        "\n",
-        "In addition this notebook showcases the following features\n",
-        "- **Custom CV** splits \n",
-        "- Handling **sparse data** in the input"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create an Experiment\n",
-        "\n",
-        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import logging\n",
-        "import os\n",
-        "import random\n",
-        "\n",
-        "from matplotlib import pyplot as plt\n",
-        "from matplotlib.pyplot import imshow\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "from sklearn import datasets\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl.run import AutoMLRun"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "# choose a name for the experiment\n",
-        "experiment_name = 'automl-local-missing-data'\n",
-        "# project folder\n",
-        "project_folder = './sample_projects/automl-local-missing-data'\n",
-        "\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "\n",
-        "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "pd.DataFrame(data=output, index=['']).T"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Diagnostics\n",
-        "\n",
-        "Opt-in diagnostics for better experience, quality, and security of future releases."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.telemetry import set_diagnostics_collection\n",
-        "set_diagnostics_collection(send_diagnostics = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Creating Sparse Data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.datasets import fetch_20newsgroups\n",
-        "from sklearn.feature_extraction.text import HashingVectorizer\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "\n",
-        "remove = ('headers', 'footers', 'quotes')\n",
-        "categories = [\n",
-        "    'alt.atheism',\n",
-        "    'talk.religion.misc',\n",
-        "    'comp.graphics',\n",
-        "    'sci.space',\n",
-        "]\n",
-        "data_train = fetch_20newsgroups(subset = 'train', categories = categories,\n",
-        "                                shuffle = True, random_state = 42,\n",
-        "                                remove = remove)\n",
-        "\n",
-        "X_train, X_valid, y_train, y_valid = train_test_split(data_train.data, data_train.target, test_size = 0.33, random_state = 42)\n",
-        "\n",
-        "\n",
-        "vectorizer = HashingVectorizer(stop_words = 'english', alternate_sign = False,\n",
-        "                               n_features = 2**16)\n",
-        "X_train = vectorizer.transform(X_train)\n",
-        "X_valid = vectorizer.transform(X_valid)\n",
-        "\n",
-        "summary_df = pd.DataFrame(index = ['No of Samples', 'No of Features'])\n",
-        "summary_df['Train Set'] = [X_train.shape[0], X_train.shape[1]]\n",
-        "summary_df['Validation Set'] = [X_valid.shape[0], X_valid.shape[1]]\n",
-        "summary_df"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Configure AutoML\n",
-        "\n",
-        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
-        "\n",
-        "|Property|Description|\n",
-        "|-|-|\n",
-        "|**task**|classification or regression|\n",
-        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
-        "|**max_time_sec**|Time limit in seconds for each iteration.|\n",
-        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
-        "|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.<br>**Note:** If input data is sparse, you cannot use *True*.|\n",
-        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-        "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
-        "|**X_valid**|(sparse) array-like, shape = [n_samples, n_features] for the custom validation set.|\n",
-        "|**y_valid**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification for the custom validation set.|\n",
-        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             debug_log = 'automl_errors.log',\n",
-        "                             primary_metric = 'AUC_weighted',\n",
-        "                             max_time_sec = 3600,\n",
-        "                             iterations = 5,\n",
-        "                             preprocess = False,\n",
-        "                             verbosity = logging.INFO,\n",
-        "                             X = X_train, \n",
-        "                             y = y_train,\n",
-        "                             X_valid = X_valid, \n",
-        "                             y_valid = y_valid, \n",
-        "                             path = project_folder)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train the Models\n",
-        "\n",
-        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
-        "In this example, we specify `show_output = True` to print currently running iterations to the console."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_run = experiment.submit(automl_config, show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explore the Results"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Widget for Monitoring Runs\n",
-        "\n",
-        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
-        "\n",
-        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.widgets import RunDetails\n",
-        "RunDetails(local_run).show() "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n",
-        "#### Retrieve All Child Runs\n",
-        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "children = list(local_run.get_children())\n",
-        "metricslist = {}\n",
-        "for run in children:\n",
-        "    properties = run.get_properties()\n",
-        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
-        "    metricslist[int(properties['iteration'])] = metrics\n",
-        "    \n",
-        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
-        "rundata"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Retrieve the Best Model\n",
-        "\n",
-        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "best_run, fitted_model = local_run.get_output()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Best Model Based on Any Other Metric\n",
-        "Show the run and the model which has the smallest `accuracy` value:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# lookup_metric = \"accuracy\"\n",
-        "# best_run, fitted_model = local_run.get_output(metric = lookup_metric)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Model from a Specific Iteration\n",
-        "Show the run and the model from the third iteration:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# iteration = 3\n",
-        "# best_run, fitted_model = local_run.get_output(iteration = iteration)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Testing the Best Fitted Model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Load test data.\n",
-        "from pandas_ml import ConfusionMatrix\n",
-        "\n",
-        "data_test = fetch_20newsgroups(subset = 'test', categories = categories,\n",
-        "                               shuffle = True, random_state = 42,\n",
-        "                               remove = remove)\n",
-        "\n",
-        "X_test = vectorizer.transform(data_test.data)\n",
-        "y_test = data_test.target\n",
-        "\n",
-        "# Test our best pipeline.\n",
-        "\n",
-        "y_pred = fitted_model.predict(X_test)\n",
-        "y_pred_strings = [data_test.target_names[i] for i in y_pred]\n",
-        "y_test_strings = [data_test.target_names[i] for i in y_test]\n",
-        "\n",
-        "cm = ConfusionMatrix(y_test_strings, y_pred_strings)\n",
-        "print(cm)\n",
-        "cm.plot()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "savitam"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

automl/06.auto-ml-sparse-data-train-test-split.ipynb

@@ -0,0 +1,384 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 06: Train Test Split and Handling Sparse Data\n",
+    "\n",
+    "In this example we use the scikit-learn's [20newsgroup](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_20newsgroups.html) to showcase how you can use AutoML for handling sparse data and how to specify custom cross validations splits.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you will learn how to:\n",
+    "1. Create an `Experiment` in an existing `Workspace`.\n",
+    "2. Configure AutoML using `AutoMLConfig`.\n",
+    "4. Train the model.\n",
+    "5. Explore the results.\n",
+    "6. Test the best fitted model.\n",
+    "\n",
+    "In addition this notebook showcases the following features\n",
+    "- Explicit train test splits \n",
+    "- Handling **sparse data** in the input"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# choose a name for the experiment\n",
+    "experiment_name = 'automl-local-missing-data'\n",
+    "# project folder\n",
+    "project_folder = './sample_projects/automl-local-missing-data'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data=output, index=['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating Sparse Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_20newsgroups\n",
+    "from sklearn.feature_extraction.text import HashingVectorizer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "remove = ('headers', 'footers', 'quotes')\n",
+    "categories = [\n",
+    "    'alt.atheism',\n",
+    "    'talk.religion.misc',\n",
+    "    'comp.graphics',\n",
+    "    'sci.space',\n",
+    "]\n",
+    "data_train = fetch_20newsgroups(subset = 'train', categories = categories,\n",
+    "                                shuffle = True, random_state = 42,\n",
+    "                                remove = remove)\n",
+    "\n",
+    "X_train, X_valid, y_train, y_valid = train_test_split(data_train.data, data_train.target, test_size = 0.33, random_state = 42)\n",
+    "\n",
+    "\n",
+    "vectorizer = HashingVectorizer(stop_words = 'english', alternate_sign = False,\n",
+    "                               n_features = 2**16)\n",
+    "X_train = vectorizer.transform(X_train)\n",
+    "X_valid = vectorizer.transform(X_valid)\n",
+    "\n",
+    "summary_df = pd.DataFrame(index = ['No of Samples', 'No of Features'])\n",
+    "summary_df['Train Set'] = [X_train.shape[0], X_train.shape[1]]\n",
+    "summary_df['Validation Set'] = [X_valid.shape[0], X_valid.shape[1]]\n",
+    "summary_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|classification or regression|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.<br>**Note:** If input data is sparse, you cannot use *True*.|\n",
+    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
+    "|**X_valid**|(sparse) array-like, shape = [n_samples, n_features] for the custom validation set.|\n",
+    "|**y_valid**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification for the custom validation set.|\n",
+    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             primary_metric = 'AUC_weighted',\n",
+    "                             iteration_timeout_minutes = 60,\n",
+    "                             iterations = 5,\n",
+    "                             preprocess = False,\n",
+    "                             verbosity = logging.INFO,\n",
+    "                             X = X_train, \n",
+    "                             y = y_train,\n",
+    "                             X_valid = X_valid, \n",
+    "                             y_valid = y_valid, \n",
+    "                             path = project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
+    "In this example, we specify `show_output = True` to print currently running iterations to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(local_run).show() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(local_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "    \n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model which has the smallest `accuracy` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lookup_metric = \"accuracy\"\n",
+    "# best_run, fitted_model = local_run.get_output(metric = lookup_metric)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration\n",
+    "Show the run and the model from the third iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# iteration = 3\n",
+    "# best_run, fitted_model = local_run.get_output(iteration = iteration)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing the Best Fitted Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load test data.\n",
+    "from pandas_ml import ConfusionMatrix\n",
+    "\n",
+    "data_test = fetch_20newsgroups(subset = 'test', categories = categories,\n",
+    "                               shuffle = True, random_state = 42,\n",
+    "                               remove = remove)\n",
+    "\n",
+    "X_test = vectorizer.transform(data_test.data)\n",
+    "y_test = data_test.target\n",
+    "\n",
+    "# Test our best pipeline.\n",
+    "\n",
+    "y_pred = fitted_model.predict(X_test)\n",
+    "y_pred_strings = [data_test.target_names[i] for i in y_pred]\n",
+    "y_test_strings = [data_test.target_names[i] for i in y_test]\n",
+    "\n",
+    "cm = ConfusionMatrix(y_test_strings, y_pred_strings)\n",
+    "print(cm)\n",
+    "cm.plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

automl/07.auto-ml-exploring-previous-runs.ipynb

@@ -1,333 +1,336 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# AutoML 07: Exploring Previous Runs\n",
-        "\n",
-        "In this example we present some examples on navigating previously executed runs. We also show how you can download a fitted model for any previous run.\n",
-        "\n",
-        "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
-        "\n",
-        "In this notebook you will learn how to:\n",
-        "1. List all experiments in a workspace.\n",
-        "2. List all AutoML runs in an experiment.\n",
-        "3. Get details for an AutoML run, including settings, run widget, and all metrics.\n",
-        "4. Download a fitted pipeline for any iteration.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# List all AutoML Experiments in a Workspace"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import logging\n",
-        "import os\n",
-        "import random\n",
-        "import re\n",
-        "\n",
-        "from matplotlib import pyplot as plt\n",
-        "from matplotlib.pyplot import imshow\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "from sklearn import datasets\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.core.run import Run\n",
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl.run import AutoMLRun"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "experiment_list = Experiment.list(workspace=ws)\n",
-        "\n",
-        "summary_df = pd.DataFrame(index = ['No of Runs'])\n",
-        "pattern = re.compile('^AutoML_[^_]*$')\n",
-        "for experiment in experiment_list:\n",
-        "    all_runs = list(experiment.get_runs())\n",
-        "    automl_runs = []\n",
-        "    for run in all_runs:\n",
-        "        if(pattern.match(run.id)):\n",
-        "            automl_runs.append(run)    \n",
-        "    summary_df[experiment.name] = [len(automl_runs)]\n",
-        "    \n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "summary_df.T"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Diagnostics\n",
-        "\n",
-        "Opt-in diagnostics for better experience, quality, and security of future releases."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.telemetry import set_diagnostics_collection\n",
-        "set_diagnostics_collection(send_diagnostics = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# List AutoML runs for an experiment\n",
-        "Set `experiment_name` to any experiment name from the result of the Experiment.list cell to load the AutoML runs."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "experiment_name = 'automl-local-classification' # Replace this with any project name from previous cell.\n",
-        "\n",
-        "proj = ws.experiments()[experiment_name]\n",
-        "summary_df = pd.DataFrame(index = ['Type', 'Status', 'Primary Metric', 'Iterations', 'Compute', 'Name'])\n",
-        "pattern = re.compile('^AutoML_[^_]*$')\n",
-        "all_runs = list(proj.get_runs(properties={'azureml.runsource': 'automl'}))\n",
-        "for run in all_runs:\n",
-        "    if(pattern.match(run.id)):\n",
-        "        properties = run.get_properties()\n",
-        "        tags = run.get_tags()\n",
-        "        amlsettings = eval(properties['RawAMLSettingsString'])\n",
-        "        if 'iterations' in tags:\n",
-        "            iterations = tags['iterations']\n",
-        "        else:\n",
-        "            iterations = properties['num_iterations']\n",
-        "        summary_df[run.id] = [amlsettings['task_type'], run.get_details()['status'], properties['primary_metric'], iterations, properties['target'], amlsettings['name']]\n",
-        "    \n",
-        "from IPython.display import HTML\n",
-        "projname_html = HTML(\"<h3>{}</h3>\".format(proj.name))\n",
-        "\n",
-        "from IPython.display import display\n",
-        "display(projname_html)\n",
-        "display(summary_df.T)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Get details for an AutoML run\n",
-        "\n",
-        "Copy the project name and run id from the previous cell output to find more details on a particular run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run_id = '' # Filling your own run_id from above run ids\n",
-        "assert (run_id in summary_df.keys()),\"Run id not found! Please set run id to a value from above run ids\"\n",
-        "\n",
-        "from azureml.train.widgets import RunDetails\n",
-        "\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "ml_run = AutoMLRun(experiment = experiment, run_id = run_id)\n",
-        "\n",
-        "summary_df = pd.DataFrame(index = ['Type', 'Status', 'Primary Metric', 'Iterations', 'Compute', 'Name', 'Start Time', 'End Time'])\n",
-        "properties = ml_run.get_properties()\n",
-        "tags = ml_run.get_tags()\n",
-        "status = ml_run.get_details()\n",
-        "amlsettings = eval(properties['RawAMLSettingsString'])\n",
-        "if 'iterations' in tags:\n",
-        "    iterations = tags['iterations']\n",
-        "else:\n",
-        "    iterations = properties['num_iterations']\n",
-        "start_time = None\n",
-        "if 'startTimeUtc' in status:\n",
-        "    start_time = status['startTimeUtc']\n",
-        "end_time = None\n",
-        "if 'endTimeUtc' in status:\n",
-        "    end_time = status['endTimeUtc']\n",
-        "summary_df[ml_run.id] = [amlsettings['task_type'], status['status'], properties['primary_metric'], iterations, properties['target'], amlsettings['name'], start_time, end_time]\n",
-        "display(HTML('<h3>Runtime Details</h3>'))\n",
-        "display(summary_df)\n",
-        "\n",
-        "#settings_df = pd.DataFrame(data = amlsettings, index = [''])\n",
-        "display(HTML('<h3>AutoML Settings</h3>'))\n",
-        "display(amlsettings)\n",
-        "\n",
-        "display(HTML('<h3>Iterations</h3>'))\n",
-        "RunDetails(ml_run).show() \n",
-        "\n",
-        "children = list(ml_run.get_children())\n",
-        "metricslist = {}\n",
-        "for run in children:\n",
-        "    properties = run.get_properties()\n",
-        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
-        "    metricslist[int(properties['iteration'])] = metrics\n",
-        "\n",
-        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
-        "display(HTML('<h3>Metrics</h3>'))\n",
-        "display(rundata)\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Download fitted models"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Download the Best Model for Any Given Metric"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "metric = 'AUC_weighted' # Replace with a metric name.\n",
-        "best_run, fitted_model = ml_run.get_output(metric = metric)\n",
-        "fitted_model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Download the Model for Any Given Iteration"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "iteration = 4 # Replace with an iteration number.\n",
-        "best_run, fitted_model = ml_run.get_output(iteration = iteration)\n",
-        "fitted_model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Register fitted model for deployment\n",
-        "If neither `metric` nor `iteration` are specified in the `register_model` call, the iteration with the best primary metric is registered."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "description = 'AutoML Model'\n",
-        "tags = None\n",
-        "ml_run.register_model(description = description, tags = tags)\n",
-        "ml_run.model_id # Use this id to deploy the model as a web service in Azure."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Register the Best Model for Any Given Metric"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "metric = 'AUC_weighted' # Replace with a metric name.\n",
-        "description = 'AutoML Model'\n",
-        "tags = None\n",
-        "ml_run.register_model(description = description, tags = tags, metric = metric)\n",
-        "print(ml_run.model_id) # Use this id to deploy the model as a web service in Azure."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Register the Model for Any Given Iteration"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "iteration = 4 # Replace with an iteration number.\n",
-        "description = 'AutoML Model'\n",
-        "tags = None\n",
-        "ml_run.register_model(description = description, tags = tags, iteration = iteration)\n",
-        "print(ml_run.model_id) # Use this id to deploy the model as a web service in Azure."
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "savitam"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 07: Exploring Previous Runs\n",
+    "\n",
+    "In this example we present some examples on navigating previously executed runs. We also show how you can download a fitted model for any previous run.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you will learn how to:\n",
+    "1. List all experiments in a workspace.\n",
+    "2. List all AutoML runs in an experiment.\n",
+    "3. Get details for an AutoML run, including settings, run widget, and all metrics.\n",
+    "4. Download a fitted pipeline for any iteration.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# List all AutoML Experiments in a Workspace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "import re\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.run import Run\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "experiment_list = Experiment.list(workspace=ws)\n",
+    "\n",
+    "summary_df = pd.DataFrame(index = ['No of Runs'])\n",
+    "pattern = re.compile('^AutoML_[^_]*$')\n",
+    "for experiment in experiment_list:\n",
+    "    all_runs = list(experiment.get_runs())\n",
+    "    automl_runs = []\n",
+    "    for run in all_runs:\n",
+    "        if(pattern.match(run.id)):\n",
+    "            automl_runs.append(run)    \n",
+    "    summary_df[experiment.name] = [len(automl_runs)]\n",
+    "    \n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "summary_df.T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# List AutoML runs for an experiment\n",
+    "Set `experiment_name` to any experiment name from the result of the Experiment.list cell to load the AutoML runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment_name = 'automl-local-classification' # Replace this with any project name from previous cell.\n",
+    "\n",
+    "proj = ws.experiments[experiment_name]\n",
+    "summary_df = pd.DataFrame(index = ['Type', 'Status', 'Primary Metric', 'Iterations', 'Compute', 'Name'])\n",
+    "pattern = re.compile('^AutoML_[^_]*$')\n",
+    "all_runs = list(proj.get_runs(properties={'azureml.runsource': 'automl'}))\n",
+    "automl_runs_project = []\n",
+    "for run in all_runs:\n",
+    "    if(pattern.match(run.id)):\n",
+    "        properties = run.get_properties()\n",
+    "        tags = run.get_tags()\n",
+    "        amlsettings = eval(properties['RawAMLSettingsString'])\n",
+    "        if 'iterations' in tags:\n",
+    "            iterations = tags['iterations']\n",
+    "        else:\n",
+    "            iterations = properties['num_iterations']\n",
+    "        summary_df[run.id] = [amlsettings['task_type'], run.get_details()['status'], properties['primary_metric'], iterations, properties['target'], amlsettings['name']]\n",
+    "        if run.get_details()['status'] == 'Completed':\n",
+    "            automl_runs_project.append(run.id)\n",
+    "    \n",
+    "from IPython.display import HTML\n",
+    "projname_html = HTML(\"<h3>{}</h3>\".format(proj.name))\n",
+    "\n",
+    "from IPython.display import display\n",
+    "display(projname_html)\n",
+    "display(summary_df.T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Get details for an AutoML run\n",
+    "\n",
+    "Copy the project name and run id from the previous cell output to find more details on a particular run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_id = automl_runs_project[0]  # Replace with your own run_id from above run ids\n",
+    "assert (run_id in summary_df.keys()), \"Run id not found! Please set run id to a value from above run ids\"\n",
+    "\n",
+    "from azureml.widgets import RunDetails\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "ml_run = AutoMLRun(experiment = experiment, run_id = run_id)\n",
+    "\n",
+    "summary_df = pd.DataFrame(index = ['Type', 'Status', 'Primary Metric', 'Iterations', 'Compute', 'Name', 'Start Time', 'End Time'])\n",
+    "properties = ml_run.get_properties()\n",
+    "tags = ml_run.get_tags()\n",
+    "status = ml_run.get_details()\n",
+    "amlsettings = eval(properties['RawAMLSettingsString'])\n",
+    "if 'iterations' in tags:\n",
+    "    iterations = tags['iterations']\n",
+    "else:\n",
+    "    iterations = properties['num_iterations']\n",
+    "start_time = None\n",
+    "if 'startTimeUtc' in status:\n",
+    "    start_time = status['startTimeUtc']\n",
+    "end_time = None\n",
+    "if 'endTimeUtc' in status:\n",
+    "    end_time = status['endTimeUtc']\n",
+    "summary_df[ml_run.id] = [amlsettings['task_type'], status['status'], properties['primary_metric'], iterations, properties['target'], amlsettings['name'], start_time, end_time]\n",
+    "display(HTML('<h3>Runtime Details</h3>'))\n",
+    "display(summary_df)\n",
+    "\n",
+    "#settings_df = pd.DataFrame(data = amlsettings, index = [''])\n",
+    "display(HTML('<h3>AutoML Settings</h3>'))\n",
+    "display(amlsettings)\n",
+    "\n",
+    "display(HTML('<h3>Iterations</h3>'))\n",
+    "RunDetails(ml_run).show() \n",
+    "\n",
+    "children = list(ml_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "display(HTML('<h3>Metrics</h3>'))\n",
+    "display(rundata)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Download fitted models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download the Best Model for Any Given Metric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metric = 'AUC_weighted' # Replace with a metric name.\n",
+    "best_run, fitted_model = ml_run.get_output(metric = metric)\n",
+    "fitted_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download the Model for Any Given Iteration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 1 # Replace with an iteration number.\n",
+    "best_run, fitted_model = ml_run.get_output(iteration = iteration)\n",
+    "fitted_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Register fitted model for deployment\n",
+    "If neither `metric` nor `iteration` are specified in the `register_model` call, the iteration with the best primary metric is registered."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "description = 'AutoML Model'\n",
+    "tags = None\n",
+    "ml_run.register_model(description = description, tags = tags)\n",
+    "ml_run.model_id # Use this id to deploy the model as a web service in Azure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Register the Best Model for Any Given Metric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metric = 'AUC_weighted' # Replace with a metric name.\n",
+    "description = 'AutoML Model'\n",
+    "tags = None\n",
+    "ml_run.register_model(description = description, tags = tags, metric = metric)\n",
+    "print(ml_run.model_id) # Use this id to deploy the model as a web service in Azure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Register the Model for Any Given Iteration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 1 # Replace with an iteration number.\n",
+    "description = 'AutoML Model'\n",
+    "tags = None\n",
+    "ml_run.register_model(description = description, tags = tags, iteration = iteration)\n",
+    "print(ml_run.model_id) # Use this id to deploy the model as a web service in Azure."
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

automl/08.auto-ml-remote-execution-with-DataStore.ipynb

@@ -0,0 +1,568 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 08: Remote Execution with DataStore\n",
+    "\n",
+    "This sample accesses a data file on a remote DSVM through DataStore. Advantages of using data store are:\n",
+    "1. DataStore secures the access details.\n",
+    "2. DataStore supports read, write to blob and file store\n",
+    "3. AutoML natively supports copying data from DataStore to DSVM\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you would see\n",
+    "1. Storing data in DataStore.\n",
+    "2. get_data returning data from DataStore.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Experiment\n",
+    "\n",
+    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "import time\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.compute import DsvmCompute\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# choose a name for experiment\n",
+    "experiment_name = 'automl-remote-datastore-file'\n",
+    "# project folder\n",
+    "project_folder = './sample_projects/automl-remote-dsvm-file'\n",
+    "\n",
+    "experiment=Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data=output, index=['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a Remote Linux DSVM\n",
+    "Note: If creation fails with a message about Marketplace purchase eligibilty, go to portal.azure.com, start creating DSVM there, and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled it, you can exit without actually creating VM.\n",
+    "\n",
+    "**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you can switch to a different port (such as 5022), you can append the port number to the address. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "compute_target_name = 'mydsvmc'\n",
+    "\n",
+    "try:\n",
+    "    while ws.compute_targets[compute_target_name].provisioning_state == 'Creating':\n",
+    "        time.sleep(1)\n",
+    "        \n",
+    "    dsvm_compute = DsvmCompute(workspace=ws, name=compute_target_name)\n",
+    "    print('found existing:', dsvm_compute.name)\n",
+    "except:\n",
+    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size=\"Standard_D2_v2\")\n",
+    "    dsvm_compute = DsvmCompute.create(ws, name=compute_target_name, provisioning_configuration=dsvm_config)\n",
+    "    dsvm_compute.wait_for_completion(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Copy data file to local\n",
+    "\n",
+    "Download the data file.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mkdir data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
+    "                     delimiter=\"\\t\", quotechar='\"')\n",
+    "df.to_csv(\"data/data.tsv\", sep=\"\\t\", quotechar='\"', index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Upload data to the cloud"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now make the data accessible remotely by uploading that data from your local machine into Azure so it can be accessed for remote training. The datastore is a convenient construct associated with your workspace for you to upload/download data, and interact with it from your remote compute targets. It is backed by Azure blob storage account.\n",
+    "\n",
+    "The data.tsv files are uploaded into a directory named data at the root of the datastore."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace, Datastore\n",
+    "#blob_datastore = Datastore(ws, blob_datastore_name)\n",
+    "ds = ws.get_default_datastore()\n",
+    "print(ds.datastore_type, ds.account_name, ds.container_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ds.upload_files(\"data.tsv\")\n",
+    "ds.upload(src_dir='./data', target_path='data', overwrite=True, show_progress=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure & Run\n",
+    "\n",
+    "First let's create a DataReferenceConfigruation object to inform the system what data folder to download to the compute target.\n",
+    "The path_on_compute should be an absolute path to ensure that the data files are downloaded only once.   The get_data method should use this same path to access the data files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.runconfig import DataReferenceConfiguration\n",
+    "dr = DataReferenceConfiguration(datastore_name=ds.name, \n",
+    "                   path_on_datastore='data', \n",
+    "                   path_on_compute='/tmp/azureml_runs',\n",
+    "                   mode='download', # download files from datastore to compute target\n",
+    "                   overwrite=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.runconfig import RunConfiguration\n",
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "\n",
+    "# create a new RunConfig object\n",
+    "conda_run_config = RunConfiguration(framework=\"python\")\n",
+    "\n",
+    "# Set compute target to the Linux DSVM\n",
+    "conda_run_config.target = dsvm_compute\n",
+    "# set the data reference of the run coonfiguration\n",
+    "conda_run_config.data_references = {ds.name: dr}\n",
+    "\n",
+    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
+    "conda_run_config.environment.python.conda_dependencies = cd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Get Data File\n",
+    "For remote executions you should author a get_data.py file containing a get_data() function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
+    "\n",
+    "The *get_data()* function returns a [dictionary](README.md#getdata).\n",
+    "\n",
+    "The read_csv uses the path_on_compute value specified in the DataReferenceConfiguration call plus the path_on_datastore folder and then the actual file name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(project_folder):\n",
+    "    os.makedirs(project_folder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile $project_folder/get_data.py\n",
+    "\n",
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "import os\n",
+    "from os.path import expanduser, join, dirname\n",
+    "\n",
+    "def get_data():\n",
+    "    # Burning man 2016 data\n",
+    "    df = pd.read_csv(\"/tmp/azureml_runs/data/data.tsv\", delimiter=\"\\t\", quotechar='\"')\n",
+    "    # get integer labels\n",
+    "    le = LabelEncoder()\n",
+    "    le.fit(df[\"Label\"].values)\n",
+    "    y = le.transform(df[\"Label\"].values)\n",
+    "    X = df.drop([\"Label\"], axis=1)\n",
+    "\n",
+    "    X_train, _, y_train, _ = train_test_split(X, y, test_size=0.1, random_state=42)\n",
+    "\n",
+    "    return { \"X\" : X_train.values, \"y\" : y_train }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
+    "\n",
+    "You can specify automl_settings as **kwargs** as well. Also note that you can use the get_data() symantic for local excutions too. \n",
+    "\n",
+    "<i>Note: For Remote DSVM and Batch AI you cannot pass Numpy arrays directly to AutoMLConfig.</i>\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**primary_metric**|This is the metric that you want to optimize.<br> Classification supports the following primary metrics <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration|\n",
+    "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data|\n",
+    "|**n_cross_validations**|Number of cross validation splits|\n",
+    "|**max_concurrent_iterations**|Max number of iterations that would be executed in parallel.  This should be less than the number of cores on the DSVM\n",
+    "|**preprocess**| *True/False* <br>Setting this to *True* enables Auto ML to perform preprocessing <br>on the input to handle *missing data*, and perform some common *feature extraction*|\n",
+    "|**max_cores_per_iteration**| Indicates how many cores on the compute target would be used to train a single pipeline.<br> Default is *1*, you can set it to *-1* to use all cores|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_settings = {\n",
+    "    \"iteration_timeout_minutes\": 60,\n",
+    "    \"iterations\": 4,\n",
+    "    \"n_cross_validations\": 5,\n",
+    "    \"primary_metric\": 'AUC_weighted',\n",
+    "    \"preprocess\": True,\n",
+    "    \"max_cores_per_iteration\": 1,\n",
+    "    \"verbosity\": logging.INFO\n",
+    "}\n",
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             path=project_folder,\n",
+    "                             run_configuration=conda_run_config,\n",
+    "                             #compute_target = dsvm_compute,\n",
+    "                             data_script = project_folder + \"/get_data.py\",\n",
+    "                             **automl_settings\n",
+    "                            )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the Models <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
+    "\n",
+    "For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets/models even when the experiment is running to retreive the best model up to that point. Once you are satisfied with the model you can cancel a particular iteration or the whole run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remote_run = experiment.submit(automl_config, show_output=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
+    "#### Widget for monitoring runs\n",
+    "\n",
+    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "You can click on a pipeline to see run properties and output logs. Logs are also available on the DSVM under /tmp/azureml_run/{iterationid}/azureml-logs\n",
+    "\n",
+    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(remote_run).show() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Wait until the run finishes.\n",
+    "remote_run.wait_for_completion(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use sdk methods to fetch all the child runs and see individual metrics that we log. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(remote_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Canceling Runs\n",
+    "You can cancel ongoing remote runs using the *cancel()* and *cancel_iteration()* functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cancel the ongoing experiment and stop scheduling new iterations\n",
+    "# remote_run.cancel()\n",
+    "\n",
+    "# Cancel iteration 1 and move onto iteration 2\n",
+    "# remote_run.cancel_iteration(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The *get_output* method returns the best run and the fitted model. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = remote_run.get_output()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model based on any other metric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lookup_metric = \"accuracy\"\n",
+    "# best_run, fitted_model = remote_run.get_output(metric=lookup_metric)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a specific iteration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# iteration = 1\n",
+    "# best_run, fitted_model = remote_run.get_output(iteration=iteration)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing the Best Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sklearn\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from pandas_ml import ConfusionMatrix\n",
+    "\n",
+    "df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
+    "                     delimiter=\"\\t\", quotechar='\"')\n",
+    "\n",
+    "# get integer labels\n",
+    "le = LabelEncoder()\n",
+    "le.fit(df[\"Label\"].values)\n",
+    "y = le.transform(df[\"Label\"].values)\n",
+    "X = df.drop([\"Label\"], axis=1)\n",
+    "\n",
+    "_, X_test, _, y_test = train_test_split(X, y, test_size=0.1, random_state=42)\n",
+    "\n",
+    "ypred = fitted_model.predict(X_test.values)\n",
+    "\n",
+    "ypred_strings = le.inverse_transform(ypred)\n",
+    "ytest_strings = le.inverse_transform(y_test)\n",
+    "\n",
+    "cm = ConfusionMatrix(ytest_strings, ypred_strings)\n",
+    "\n",
+    "print(cm)\n",
+    "\n",
+    "cm.plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

automl/08.auto-ml-remote-execution-with-text-file-on-DSVM.ipynb

@@ -1,550 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# AutoML 08: Remote Execution with DataStore\n",
-        "\n",
-        "This sample accesses a data file on a remote DSVM through DataStore. Advantages of using data store are:\n",
-        "1. DataStore secures the access details.\n",
-        "2. DataStore supports read, write to blob and file store\n",
-        "3. AutoML natively supports copying data from DataStore to DSVM\n",
-        "\n",
-        "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
-        "\n",
-        "In this notebook you would see\n",
-        "1. Storing data in DataStore.\n",
-        "2. get_data returning data from DataStore.\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create Experiment\n",
-        "\n",
-        "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import logging\n",
-        "import os\n",
-        "import random\n",
-        "\n",
-        "from matplotlib import pyplot as plt\n",
-        "from matplotlib.pyplot import imshow\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "from sklearn import datasets\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl.run import AutoMLRun"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "# choose a name for experiment\n",
-        "experiment_name = 'automl-remote-datastore-file'\n",
-        "# project folder\n",
-        "project_folder = './sample_projects/automl-remote-dsvm-file'\n",
-        "\n",
-        "experiment=Experiment(ws, experiment_name)\n",
-        "\n",
-        "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "pd.DataFrame(data=output, index=['']).T"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Diagnostics\n",
-        "\n",
-        "Opt-in diagnostics for better experience, quality, and security of future releases"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.telemetry import set_diagnostics_collection\n",
-        "set_diagnostics_collection(send_diagnostics=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create a Remote Linux DSVM\n",
-        "Note: If creation fails with a message about Marketplace purchase eligibilty, go to portal.azure.com, start creating DSVM there, and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled it, you can exit without actually creating VM.\n",
-        "\n",
-        "**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you can switch to a different port (such as 5022), you can append the port number to the address. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on this."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import DsvmCompute\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "compute_target_name = 'mydsvm'\n",
-        "\n",
-        "try:\n",
-        "    dsvm_compute = DsvmCompute(workspace=ws, name=compute_target_name)\n",
-        "    print('found existing:', dsvm_compute.name)\n",
-        "except ComputeTargetException:\n",
-        "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size=\"Standard_D2_v2\")\n",
-        "    dsvm_compute = DsvmCompute.create(ws, name=compute_target_name, provisioning_configuration=dsvm_config)\n",
-        "    dsvm_compute.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Copy data file to local\n",
-        "\n",
-        "Download the data file.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "mkdir data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
-        "                     delimiter=\"\\t\", quotechar='\"')\n",
-        "df.to_csv(\"data/data.tsv\", sep=\"\\t\", quotechar='\"', index=False)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Upload data to the cloud"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now make the data accessible remotely by uploading that data from your local machine into Azure so it can be accessed for remote training. The datastore is a convenient construct associated with your workspace for you to upload/download data, and interact with it from your remote compute targets. It is backed by Azure blob storage account.\n",
-        "\n",
-        "The data.tsv files are uploaded into a directory named data at the root of the datastore."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace, Datastore\n",
-        "#blob_datastore = Datastore(ws, blob_datastore_name)\n",
-        "ds = ws.get_default_datastore()\n",
-        "print(ds.datastore_type, ds.account_name, ds.container_name)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# ds.upload_files(\"data.tsv\")\n",
-        "ds.upload(src_dir='./data', target_path='data', overwrite=True, show_progress=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Configure & Run\n",
-        "\n",
-        "First let's create a DataReferenceConfigruation object to inform the system what data folder to download to the copmute target."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.runconfig import DataReferenceConfiguration\n",
-        "dr = DataReferenceConfiguration(datastore_name=ds.name, \n",
-        "                   path_on_datastore='data', \n",
-        "                   mode='download', # download files from datastore to compute target\n",
-        "                   overwrite=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "\n",
-        "# create a new RunConfig object\n",
-        "conda_run_config = RunConfiguration(framework=\"python\")\n",
-        "\n",
-        "# Set compute target to the Linux DSVM\n",
-        "conda_run_config.target = dsvm_compute.name\n",
-        "# set the data reference of the run coonfiguration\n",
-        "conda_run_config.data_references = {ds.name: dr}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create Get Data File\n",
-        "For remote executions you should author a get_data.py file containing a get_data() function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
-        "\n",
-        "The *get_data()* function returns a [dictionary](README.md#getdata)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "if not os.path.exists(project_folder):\n",
-        "    os.makedirs(project_folder)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile $project_folder/get_data.py\n",
-        "\n",
-        "import pandas as pd\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from sklearn.preprocessing import LabelEncoder\n",
-        "import os\n",
-        "from os.path import expanduser, join, dirname\n",
-        "\n",
-        "def get_data():\n",
-        "    # Burning man 2016 data\n",
-        "    df = pd.read_csv(join(dirname(os.path.realpath(__file__)),\n",
-        "                          os.environ[\"AZUREML_DATAREFERENCE_workspacefilestore\"],\n",
-        "                          \"data.tsv\"), delimiter=\"\\t\", quotechar='\"')\n",
-        "    # get integer labels\n",
-        "    le = LabelEncoder()\n",
-        "    le.fit(df[\"Label\"].values)\n",
-        "    y = le.transform(df[\"Label\"].values)\n",
-        "    X = df.drop([\"Label\"], axis=1)\n",
-        "\n",
-        "    X_train, _, y_train, _ = train_test_split(X, y, test_size=0.1, random_state=42)\n",
-        "\n",
-        "    return { \"X\" : X_train.values, \"y\" : y_train }"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
-        "\n",
-        "You can specify automl_settings as **kwargs** as well. Also note that you can use the get_data() symantic for local excutions too. \n",
-        "\n",
-        "<i>Note: For Remote DSVM and Batch AI you cannot pass Numpy arrays directly to AutoMLConfig.</i>\n",
-        "\n",
-        "|Property|Description|\n",
-        "|-|-|\n",
-        "|**primary_metric**|This is the metric that you want to optimize.<br> Classification supports the following primary metrics <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
-        "|**max_time_sec**|Time limit in seconds for each iteration|\n",
-        "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data|\n",
-        "|**n_cross_validations**|Number of cross validation splits|\n",
-        "|**concurrent_iterations**|Max number of iterations that would be executed in parallel.  This should be less than the number of cores on the DSVM\n",
-        "|**preprocess**| *True/False* <br>Setting this to *True* enables Auto ML to perform preprocessing <br>on the input to handle *missing data*, and perform some common *feature extraction*|\n",
-        "|**max_cores_per_iteration**| Indicates how many cores on the compute target would be used to train a single pipeline.<br> Default is *1*, you can set it to *-1* to use all cores|"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_settings = {\n",
-        "    \"max_time_sec\": 3600,\n",
-        "    \"iterations\": 10,\n",
-        "    \"n_cross_validations\": 5,\n",
-        "    \"primary_metric\": 'AUC_weighted',\n",
-        "    \"preprocess\": True,\n",
-        "    \"max_cores_per_iteration\": 2,\n",
-        "    \"verbosity\": logging.INFO\n",
-        "}\n",
-        "automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             debug_log = 'automl_errors.log',\n",
-        "                             path=project_folder,\n",
-        "                             run_configuration=conda_run_config,\n",
-        "                             #compute_target = dsvm_compute,\n",
-        "                             data_script = project_folder + \"/get_data.py\",\n",
-        "                             **automl_settings\n",
-        "                            )"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Training the Models <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
-        "\n",
-        "For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets/models even when the experiment is running to retreive the best model up to that point. Once you are satisfied with the model you can cancel a particular iteration or the whole run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "remote_run = experiment.submit(automl_config, show_output=False)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
-        "#### Widget for monitoring runs\n",
-        "\n",
-        "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
-        "\n",
-        "You can click on a pipeline to see run properties and output logs. Logs are also available on the DSVM under /tmp/azureml_run/{iterationid}/azureml-logs\n",
-        "\n",
-        "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.widgets import RunDetails\n",
-        "RunDetails(remote_run).show() "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n",
-        "#### Retrieve All Child Runs\n",
-        "You can also use sdk methods to fetch all the child runs and see individual metrics that we log. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "children = list(remote_run.get_children())\n",
-        "metricslist = {}\n",
-        "for run in children:\n",
-        "    properties = run.get_properties()\n",
-        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
-        "    metricslist[int(properties['iteration'])] = metrics\n",
-        "\n",
-        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
-        "rundata"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Canceling Runs\n",
-        "You can cancel ongoing remote runs using the *cancel()* and *cancel_iteration()* functions"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Cancel the ongoing experiment and stop scheduling new iterations\n",
-        "remote_run.cancel()\n",
-        "\n",
-        "# Cancel iteration 1 and move onto iteration 2\n",
-        "# remote_run.cancel_iteration(1)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Retrieve the Best Model\n",
-        "\n",
-        "Below we select the best pipeline from our iterations. The *get_output* method returns the best run and the fitted model. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "best_run, fitted_model = remote_run.get_output()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Best Model based on any other metric"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# lookup_metric = \"accuracy\"\n",
-        "# best_run, fitted_model = remote_run.get_output(metric=lookup_metric)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Model from a specific iteration"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# iteration = 1\n",
-        "# best_run, fitted_model = remote_run.get_output(iteration=iteration)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Testing the Best Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import sklearn\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from sklearn.preprocessing import LabelEncoder\n",
-        "from pandas_ml import ConfusionMatrix\n",
-        "\n",
-        "df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
-        "                     delimiter=\"\\t\", quotechar='\"')\n",
-        "\n",
-        "# get integer labels\n",
-        "le = LabelEncoder()\n",
-        "le.fit(df[\"Label\"].values)\n",
-        "y = le.transform(df[\"Label\"].values)\n",
-        "X = df.drop([\"Label\"], axis=1)\n",
-        "\n",
-        "_, X_test, _, y_test = train_test_split(X, y, test_size=0.1, random_state=42)\n",
-        "\n",
-        "ypred = fitted_model.predict(X_test.values)\n",
-        "\n",
-        "ypred_strings = le.inverse_transform(ypred)\n",
-        "ytest_strings = le.inverse_transform(y_test)\n",
-        "\n",
-        "cm = ConfusionMatrix(ytest_strings, ypred_strings)\n",
-        "\n",
-        "print(cm)\n",
-        "\n",
-        "cm.plot()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "savitam"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

automl/09.auto-ml-classification-with-deployment.ipynb

@@ -1,500 +1,501 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# AutoML 09: Classification with Deployment\n",
-        "\n",
-        "In this example we use the scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) to showcase how you can use AutoML for a simple classification problem and deploy it to an Azure Container Instance (ACI).\n",
-        "\n",
-        "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
-        "\n",
-        "In this notebook you will learn how to:\n",
-        "1. Create an experiment using an existing workspace.\n",
-        "2. Configure AutoML using `AutoMLConfig`.\n",
-        "3. Train the model using local compute.\n",
-        "4. Explore the results.\n",
-        "5. Register the model.\n",
-        "6. Create a container image.\n",
-        "7. Create an Azure Container Instance (ACI) service.\n",
-        "8. Test the ACI service.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create an Experiment\n",
-        "\n",
-        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import json\n",
-        "import logging\n",
-        "import os\n",
-        "import random\n",
-        "\n",
-        "from matplotlib import pyplot as plt\n",
-        "from matplotlib.pyplot import imshow\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "from sklearn import datasets\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl.run import AutoMLRun"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "# choose a name for experiment\n",
-        "experiment_name = 'automl-local-classification'\n",
-        "# project folder\n",
-        "project_folder = './sample_projects/automl-local-classification'\n",
-        "\n",
-        "experiment=Experiment(ws, experiment_name)\n",
-        "\n",
-        "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "pd.DataFrame(data=output, index=['']).T"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Diagnostics\n",
-        "\n",
-        "Opt-in diagnostics for better experience, quality, and security of future releases."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.telemetry import set_diagnostics_collection\n",
-        "set_diagnostics_collection(send_diagnostics = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Configure AutoML\n",
-        "\n",
-        "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
-        "\n",
-        "|Property|Description|\n",
-        "|-|-|\n",
-        "|**task**|classification or regression|\n",
-        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
-        "|**max_time_sec**|Time limit in seconds for each iteration.|\n",
-        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
-        "|**n_cross_validations**|Number of cross validation splits.|\n",
-        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-        "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
-        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "digits = datasets.load_digits()\n",
-        "X_train = digits.data[10:,:]\n",
-        "y_train = digits.target[10:]\n",
-        "\n",
-        "automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             name = experiment_name,\n",
-        "                             debug_log = 'automl_errors.log',\n",
-        "                             primary_metric = 'AUC_weighted',\n",
-        "                             max_time_sec = 1200,\n",
-        "                             iterations = 10,\n",
-        "                             n_cross_validations = 2,\n",
-        "                             verbosity = logging.INFO,\n",
-        "                             X = X_train, \n",
-        "                             y = y_train,\n",
-        "                             path = project_folder)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train the Models\n",
-        "\n",
-        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
-        "In this example, we specify `show_output = True` to print currently running iterations to the console."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_run = experiment.submit(automl_config, show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Retrieve the Best Model\n",
-        "\n",
-        "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "best_run, fitted_model = local_run.get_output()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Register the Fitted Model for Deployment\n",
-        "If neither `metric` nor `iteration` are specified in the `register_model` call, the iteration with the best primary metric is registered."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "description = 'AutoML Model'\n",
-        "tags = None\n",
-        "model = local_run.register_model(description = description, tags = tags)\n",
-        "local_run.model_id # This will be written to the script file later in the notebook."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create Scoring Script"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile score.py\n",
-        "import pickle\n",
-        "import json\n",
-        "import numpy\n",
-        "from sklearn.externals import joblib\n",
-        "from azureml.core.model import Model\n",
-        "\n",
-        "\n",
-        "def init():\n",
-        "    global model\n",
-        "    model_path = Model.get_model_path(model_name = '<<modelid>>') # this name is model.id of model that we want to deploy\n",
-        "    # deserialize the model file back into a sklearn model\n",
-        "    model = joblib.load(model_path)\n",
-        "\n",
-        "def run(rawdata):\n",
-        "    try:\n",
-        "        data = json.loads(rawdata)['data']\n",
-        "        data = numpy.array(data)\n",
-        "        result = model.predict(data)\n",
-        "    except Exception as e:\n",
-        "        result = str(e)\n",
-        "        return json.dumps({\"error\": result})\n",
-        "    return json.dumps({\"result\":result.tolist()})"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create a YAML File for the Environment"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To ensure the fit results are consistent with the training results, the SDK dependency versions need to be the same as the environment that trains the model. Details about retrieving the versions can be found in notebook [12.auto-ml-retrieve-the-training-sdk-versions](12.auto-ml-retrieve-the-training-sdk-versions.ipynb)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "experiment_name = 'automl-local-classification'\n",
-        "\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "ml_run = AutoMLRun(experiment = experiment, run_id = local_run.id)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dependencies = ml_run.get_run_sdk_dependencies(iteration = 7)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
-        "    print('{}\\t{}'.format(p, dependencies[p]))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile myenv.yml\n",
-        "name: myenv\n",
-        "channels:\n",
-        "  - defaults\n",
-        "dependencies:\n",
-        "  - pip:\n",
-        "    - numpy==1.14.2\n",
-        "    - scikit-learn==0.19.2\n",
-        "    - azureml-sdk[notebooks,automl]==<<azureml-version>>"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Substitute the actual version number in the environment file.\n",
-        "\n",
-        "conda_env_file_name = 'myenv.yml'\n",
-        "\n",
-        "with open(conda_env_file_name, 'r') as cefr:\n",
-        "    content = cefr.read()\n",
-        "\n",
-        "with open(conda_env_file_name, 'w') as cefw:\n",
-        "    cefw.write(content.replace('<<azureml-version>>', dependencies['azureml-sdk']))\n",
-        "\n",
-        "# Substitute the actual model id in the script file.\n",
-        "\n",
-        "script_file_name = 'score.py'\n",
-        "\n",
-        "with open(script_file_name, 'r') as cefr:\n",
-        "    content = cefr.read()\n",
-        "\n",
-        "with open(script_file_name, 'w') as cefw:\n",
-        "    cefw.write(content.replace('<<modelid>>', local_run.model_id))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create a Container Image"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.image import Image, ContainerImage\n",
-        "\n",
-        "image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
-        "                                 execution_script = script_file_name,\n",
-        "                                 conda_file = conda_env_file_name,\n",
-        "                                 tags = {'area': \"digits\", 'type': \"automl_classification\"},\n",
-        "                                 description = \"Image for automl classification sample\")\n",
-        "\n",
-        "image = Image.create(name = \"automlsampleimage\",\n",
-        "                     # this is the model object \n",
-        "                     models = [model],\n",
-        "                     image_config = image_config, \n",
-        "                     workspace = ws)\n",
-        "\n",
-        "image.wait_for_creation(show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Deploy the Image as a Web Service on Azure Container Instance"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import AciWebservice\n",
-        "\n",
-        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
-        "                                               memory_gb = 1, \n",
-        "                                               tags = {'area': \"digits\", 'type': \"automl_classification\"}, \n",
-        "                                               description = 'sample service for Automl Classification')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import Webservice\n",
-        "\n",
-        "aci_service_name = 'automl-sample-01'\n",
-        "print(aci_service_name)\n",
-        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
-        "                                           image = image,\n",
-        "                                           name = aci_service_name,\n",
-        "                                           workspace = ws)\n",
-        "aci_service.wait_for_deployment(True)\n",
-        "print(aci_service.state)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Delete a Web Service"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#aci_service.delete()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Get Logs from a Deployed Web Service"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#aci_service.get_logs()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Test a Web Service"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#Randomly select digits and test\n",
-        "digits = datasets.load_digits()\n",
-        "X_test = digits.data[:10, :]\n",
-        "y_test = digits.target[:10]\n",
-        "images = digits.images[:10]\n",
-        "\n",
-        "for index in np.random.choice(len(y_test), 3, replace = False):\n",
-        "    print(index)\n",
-        "    test_sample = json.dumps({'data':X_test[index:index + 1].tolist()})\n",
-        "    predicted = aci_service.run(input_data = test_sample)\n",
-        "    label = y_test[index]\n",
-        "    predictedDict = json.loads(predicted)\n",
-        "    title = \"Label value = %d  Predicted value = %s \" % ( label,predictedDict['result'][0])\n",
-        "    fig = plt.figure(1, figsize = (3,3))\n",
-        "    ax1 = fig.add_axes((0,0,.8,.8))\n",
-        "    ax1.set_title(title)\n",
-        "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
-        "    plt.show()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "savitam"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 09: Classification with Deployment\n",
+    "\n",
+    "In this example we use the scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) to showcase how you can use AutoML for a simple classification problem and deploy it to an Azure Container Instance (ACI).\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you will learn how to:\n",
+    "1. Create an experiment using an existing workspace.\n",
+    "2. Configure AutoML using `AutoMLConfig`.\n",
+    "3. Train the model using local compute.\n",
+    "4. Explore the results.\n",
+    "5. Register the model.\n",
+    "6. Create a container image.\n",
+    "7. Create an Azure Container Instance (ACI) service.\n",
+    "8. Test the ACI service.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# choose a name for experiment\n",
+    "experiment_name = 'automl-local-classification'\n",
+    "# project folder\n",
+    "project_folder = './sample_projects/automl-local-classification'\n",
+    "\n",
+    "experiment=Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data=output, index=['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|classification or regression|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**n_cross_validations**|Number of cross validation splits.|\n",
+    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
+    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_train = digits.data[10:,:]\n",
+    "y_train = digits.target[10:]\n",
+    "\n",
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             name = experiment_name,\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             primary_metric = 'AUC_weighted',\n",
+    "                             iteration_timeout_minutes = 20,\n",
+    "                             iterations = 10,\n",
+    "                             n_cross_validations = 2,\n",
+    "                             verbosity = logging.INFO,\n",
+    "                             X = X_train, \n",
+    "                             y = y_train,\n",
+    "                             path = project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
+    "In this example, we specify `show_output = True` to print currently running iterations to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Register the Fitted Model for Deployment\n",
+    "If neither `metric` nor `iteration` are specified in the `register_model` call, the iteration with the best primary metric is registered."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "description = 'AutoML Model'\n",
+    "tags = None\n",
+    "model = local_run.register_model(description = description, tags = tags)\n",
+    "local_run.model_id # This will be written to the script file later in the notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create Scoring Script"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile score.py\n",
+    "import pickle\n",
+    "import json\n",
+    "import numpy\n",
+    "import azureml.train.automl\n",
+    "from sklearn.externals import joblib\n",
+    "from azureml.core.model import Model\n",
+    "\n",
+    "\n",
+    "def init():\n",
+    "    global model\n",
+    "    model_path = Model.get_model_path(model_name = '<<modelid>>') # this name is model.id of model that we want to deploy\n",
+    "    # deserialize the model file back into a sklearn model\n",
+    "    model = joblib.load(model_path)\n",
+    "\n",
+    "def run(rawdata):\n",
+    "    try:\n",
+    "        data = json.loads(rawdata)['data']\n",
+    "        data = numpy.array(data)\n",
+    "        result = model.predict(data)\n",
+    "    except Exception as e:\n",
+    "        result = str(e)\n",
+    "        return json.dumps({\"error\": result})\n",
+    "    return json.dumps({\"result\":result.tolist()})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a YAML File for the Environment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To ensure the fit results are consistent with the training results, the SDK dependency versions need to be the same as the environment that trains the model. Details about retrieving the versions can be found in notebook [12.auto-ml-retrieve-the-training-sdk-versions](12.auto-ml-retrieve-the-training-sdk-versions.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment_name = 'automl-local-classification'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "ml_run = AutoMLRun(experiment = experiment, run_id = local_run.id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dependencies = ml_run.get_run_sdk_dependencies(iteration = 7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
+    "    print('{}\\t{}'.format(p, dependencies[p]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "\n",
+    "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-sdk[automl]'])\n",
+    "\n",
+    "conda_env_file_name = 'myenv.yml'\n",
+    "myenv.save_to_file('.', conda_env_file_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Substitute the actual version number in the environment file.\n",
+    "# This is not strictly needed in this notebook because the model should have been generated using the current SDK version.\n",
+    "# However, we include this in case this code is used on an experiment from a previous SDK version.\n",
+    "\n",
+    "with open(conda_env_file_name, 'r') as cefr:\n",
+    "    content = cefr.read()\n",
+    "\n",
+    "with open(conda_env_file_name, 'w') as cefw:\n",
+    "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
+    "\n",
+    "# Substitute the actual model id in the script file.\n",
+    "\n",
+    "script_file_name = 'score.py'\n",
+    "\n",
+    "with open(script_file_name, 'r') as cefr:\n",
+    "    content = cefr.read()\n",
+    "\n",
+    "with open(script_file_name, 'w') as cefw:\n",
+    "    cefw.write(content.replace('<<modelid>>', local_run.model_id))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a Container Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.image import Image, ContainerImage\n",
+    "\n",
+    "image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
+    "                                 execution_script = script_file_name,\n",
+    "                                 conda_file = conda_env_file_name,\n",
+    "                                 tags = {'area': \"digits\", 'type': \"automl_classification\"},\n",
+    "                                 description = \"Image for automl classification sample\")\n",
+    "\n",
+    "image = Image.create(name = \"automlsampleimage\",\n",
+    "                     # this is the model object \n",
+    "                     models = [model],\n",
+    "                     image_config = image_config, \n",
+    "                     workspace = ws)\n",
+    "\n",
+    "image.wait_for_creation(show_output = True)\n",
+    "\n",
+    "if image.creation_state == 'Failed':\n",
+    "    print(\"Image build log at: \" + image.image_build_log_uri)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deploy the Image as a Web Service on Azure Container Instance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.webservice import AciWebservice\n",
+    "\n",
+    "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
+    "                                               memory_gb = 1, \n",
+    "                                               tags = {'area': \"digits\", 'type': \"automl_classification\"}, \n",
+    "                                               description = 'sample service for Automl Classification')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.webservice import Webservice\n",
+    "\n",
+    "aci_service_name = 'automl-sample-01'\n",
+    "print(aci_service_name)\n",
+    "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
+    "                                           image = image,\n",
+    "                                           name = aci_service_name,\n",
+    "                                           workspace = ws)\n",
+    "aci_service.wait_for_deployment(True)\n",
+    "print(aci_service.state)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Delete a Web Service"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#aci_service.delete()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get Logs from a Deployed Web Service"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#aci_service.get_logs()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test a Web Service"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Randomly select digits and test\n",
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:10, :]\n",
+    "y_test = digits.target[:10]\n",
+    "images = digits.images[:10]\n",
+    "\n",
+    "for index in np.random.choice(len(y_test), 3, replace = False):\n",
+    "    print(index)\n",
+    "    test_sample = json.dumps({'data':X_test[index:index + 1].tolist()})\n",
+    "    predicted = aci_service.run(input_data = test_sample)\n",
+    "    label = y_test[index]\n",
+    "    predictedDict = json.loads(predicted)\n",
+    "    title = \"Label value = %d  Predicted value = %s \" % ( label,predictedDict['result'][0])\n",
+    "    fig = plt.figure(1, figsize = (3,3))\n",
+    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "    ax1.set_title(title)\n",
+    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "    plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

automl/10.auto-ml-multi-output-example.ipynb

@@ -1,294 +1,294 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# AutoML 10: Multi-output\n",
-        "\n",
-        "This notebook shows how to use AutoML to train multi-output problems by leveraging the correlation between the outputs using indicator vectors.\n",
-        "\n",
-        "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import logging\n",
-        "import os\n",
-        "import random\n",
-        "\n",
-        "from matplotlib import pyplot as plt\n",
-        "from matplotlib.pyplot import imshow\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "from sklearn import datasets\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl.run import AutoMLRun"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Diagnostics\n",
-        "\n",
-        "Opt-in diagnostics for better experience, quality, and security of future releases."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.telemetry import set_diagnostics_collection\n",
-        "set_diagnostics_collection(send_diagnostics = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Transformer Functions\n",
-        "The transformations of inputs `X` and `y` are happening as follows, e.g. `y = {y_1, y_2}`, then `X` becomes\n",
-        "    \n",
-        "`X 1 0`\n",
-        "     \n",
-        "`X 0 1`\n",
-        "\n",
-        "and `y` becomes,\n",
-        "\n",
-        "`y_1`\n",
-        "\n",
-        "`y_2`"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from scipy import sparse\n",
-        "from scipy import linalg\n",
-        "\n",
-        "#Transformer functions\n",
-        "def multi_output_transform_x_y(X, y):\n",
-        "    X_new = multi_output_transformer_x(X, y.shape[1])\n",
-        "    y_new = multi_output_transform_y(y)\n",
-        "    return X_new, y_new\n",
-        "\n",
-        "def multi_output_transformer_x(X, number_of_columns_y):\n",
-        "    indicator_vecs = linalg.block_diag(*([np.ones((X.shape[0], 1))] * number_of_columns_y))\n",
-        "    if sparse.issparse(X):\n",
-        "        X_new = sparse.vstack(np.tile(X, number_of_columns_y))\n",
-        "        indicator_vecs = sparse.coo_matrix(indicator_vecs)\n",
-        "        X_new = sparse.hstack((X_new, indicator_vecs))\n",
-        "    else:\n",
-        "        X_new = np.tile(X, (number_of_columns_y, 1))\n",
-        "        X_new = np.hstack((X_new, indicator_vecs))\n",
-        "    return X_new\n",
-        "\n",
-        "def multi_output_transform_y(y):\n",
-        "    return y.reshape(-1, order=\"F\")\n",
-        "\n",
-        "def multi_output_inverse_transform_y(y, number_of_columns_y):\n",
-        "    return y.reshape((-1, number_of_columns_y), order = \"F\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## AutoML Experiment Setup"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "# Choose a name for the experiment and specify the project folder.\n",
-        "experiment_name = 'automl-local-multi-output'\n",
-        "project_folder = './sample_projects/automl-local-multi-output'\n",
-        "\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "\n",
-        "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "pd.DataFrame(data = output, index = ['']).T"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create a Random Dataset for Test Purposes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "rng = np.random.RandomState(1)\n",
-        "X_train = np.sort(200 * rng.rand(600, 1) - 100, axis = 0)\n",
-        "y_train = np.array([np.pi * np.sin(X_train).ravel(), np.pi * np.cos(X_train).ravel()]).T\n",
-        "y_train += (0.5 - rng.rand(*y_train.shape))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Perform X and y transformation using the transformer function."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "X_train_transformed, y_train_transformed = multi_output_transform_x_y(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Configure AutoML using the transformed results."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_config = AutoMLConfig(task = 'regression',\n",
-        "                             debug_log = 'automl_errors_multi.log',\n",
-        "                             primary_metric = 'r2_score',\n",
-        "                             iterations = 10,\n",
-        "                             n_cross_validations = 2,\n",
-        "                             verbosity = logging.INFO,\n",
-        "                             X = X_train_transformed,\n",
-        "                             y = y_train_transformed,\n",
-        "                             path = project_folder)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Fit the Transformed Data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_run = experiment.submit(automl_config, show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Get the best fit model.\n",
-        "best_run, fitted_model = local_run.get_output()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Generate random data set for predicting.\n",
-        "X_test = np.sort(200 * rng.rand(200, 1) - 100, axis = 0)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Transform predict data.\n",
-        "X_test_transformed = multi_output_transformer_x(X_test, y_train.shape[1])\n",
-        "\n",
-        "# Predict and inverse transform the prediction.\n",
-        "y_predict = fitted_model.predict(X_test_transformed)\n",
-        "y_predict = multi_output_inverse_transform_y(y_predict, y_train.shape[1])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(y_predict)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "savitam"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 10: Multi-output\n",
+    "\n",
+    "This notebook shows how to use AutoML to train multi-output problems by leveraging the correlation between the outputs using indicator vectors.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Transformer Functions\n",
+    "The transformations of inputs `X` and `y` are happening as follows, e.g. `y = {y_1, y_2}`, then `X` becomes\n",
+    "    \n",
+    "`X 1 0`\n",
+    "     \n",
+    "`X 0 1`\n",
+    "\n",
+    "and `y` becomes,\n",
+    "\n",
+    "`y_1`\n",
+    "\n",
+    "`y_2`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy import sparse\n",
+    "from scipy import linalg\n",
+    "\n",
+    "#Transformer functions\n",
+    "def multi_output_transform_x_y(X, y):\n",
+    "    X_new = multi_output_transformer_x(X, y.shape[1])\n",
+    "    y_new = multi_output_transform_y(y)\n",
+    "    return X_new, y_new\n",
+    "\n",
+    "def multi_output_transformer_x(X, number_of_columns_y):\n",
+    "    indicator_vecs = linalg.block_diag(*([np.ones((X.shape[0], 1))] * number_of_columns_y))\n",
+    "    if sparse.issparse(X):\n",
+    "        X_new = sparse.vstack(np.tile(X, number_of_columns_y))\n",
+    "        indicator_vecs = sparse.coo_matrix(indicator_vecs)\n",
+    "        X_new = sparse.hstack((X_new, indicator_vecs))\n",
+    "    else:\n",
+    "        X_new = np.tile(X, (number_of_columns_y, 1))\n",
+    "        X_new = np.hstack((X_new, indicator_vecs))\n",
+    "    return X_new\n",
+    "\n",
+    "def multi_output_transform_y(y):\n",
+    "    return y.reshape(-1, order=\"F\")\n",
+    "\n",
+    "def multi_output_inverse_transform_y(y, number_of_columns_y):\n",
+    "    return y.reshape((-1, number_of_columns_y), order = \"F\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## AutoML Experiment Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose a name for the experiment and specify the project folder.\n",
+    "experiment_name = 'automl-local-multi-output'\n",
+    "project_folder = './sample_projects/automl-local-multi-output'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a Random Dataset for Test Purposes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rng = np.random.RandomState(1)\n",
+    "X_train = np.sort(200 * rng.rand(600, 1) - 100, axis = 0)\n",
+    "y_train = np.array([np.pi * np.sin(X_train).ravel(), np.pi * np.cos(X_train).ravel()]).T\n",
+    "y_train += (0.5 - rng.rand(*y_train.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Perform X and y transformation using the transformer function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train_transformed, y_train_transformed = multi_output_transform_x_y(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Configure AutoML using the transformed results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'regression',\n",
+    "                             debug_log = 'automl_errors_multi.log',\n",
+    "                             primary_metric = 'r2_score',\n",
+    "                             iterations = 10,\n",
+    "                             n_cross_validations = 2,\n",
+    "                             verbosity = logging.INFO,\n",
+    "                             X = X_train_transformed,\n",
+    "                             y = y_train_transformed,\n",
+    "                             path = project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit the Transformed Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the best fit model.\n",
+    "best_run, fitted_model = local_run.get_output()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate random data set for predicting.\n",
+    "X_test = np.sort(200 * rng.rand(200, 1) - 100, axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Transform predict data.\n",
+    "X_test_transformed = multi_output_transformer_x(X_test, y_train.shape[1])\n",
+    "\n",
+    "# Predict and inverse transform the prediction.\n",
+    "y_predict = fitted_model.predict(X_test_transformed)\n",
+    "y_predict = multi_output_inverse_transform_y(y_predict, y_train.shape[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(y_predict)"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

automl/11.auto-ml-sample-weight.ipynb

@@ -1,251 +1,251 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# AutoML 11: Sample Weight\n",
-        "\n",
-        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use sample weight with AutoML. Sample weight is used where some sample values are more important than others.\n",
-        "\n",
-        "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
-        "\n",
-        "In this notebook you will learn how to configure AutoML to use `sample_weight` and you will see the difference sample weight makes to the test results.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create an Experiment\n",
-        "\n",
-        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import logging\n",
-        "import os\n",
-        "import random\n",
-        "\n",
-        "from matplotlib import pyplot as plt\n",
-        "from matplotlib.pyplot import imshow\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "from sklearn import datasets\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl.run import AutoMLRun"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "# Choose names for the regular and the sample weight experiments.\n",
-        "experiment_name = 'non_sample_weight_experiment'\n",
-        "sample_weight_experiment_name = 'sample_weight_experiment'\n",
-        "\n",
-        "project_folder = './sample_projects/automl-local-classification'\n",
-        "\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "sample_weight_experiment=Experiment(ws, sample_weight_experiment_name)\n",
-        "\n",
-        "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace Name'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "pd.DataFrame(data = output, index = ['']).T"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Diagnostics\n",
-        "\n",
-        "Opt-in diagnostics for better experience, quality, and security of future releases."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.telemetry import set_diagnostics_collection\n",
-        "set_diagnostics_collection(send_diagnostics = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Configure AutoML\n",
-        "\n",
-        "Instantiate two `AutoMLConfig` objects. One will be used with `sample_weight` and one without."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "digits = datasets.load_digits()\n",
-        "X_train = digits.data[100:,:]\n",
-        "y_train = digits.target[100:]\n",
-        "\n",
-        "# The example makes the sample weight 0.9 for the digit 4 and 0.1 for all other digits.\n",
-        "# This makes the model more likely to classify as 4 if the image it not clear.\n",
-        "sample_weight = np.array([(0.9 if x == 4 else 0.01) for x in y_train])\n",
-        "\n",
-        "automl_classifier = AutoMLConfig(task = 'classification',\n",
-        "                                 debug_log = 'automl_errors.log',\n",
-        "                                 primary_metric = 'AUC_weighted',\n",
-        "                                 max_time_sec = 3600,\n",
-        "                                 iterations = 10,\n",
-        "                                 n_cross_validations = 2,\n",
-        "                                 verbosity = logging.INFO,\n",
-        "                                 X = X_train, \n",
-        "                                 y = y_train,\n",
-        "                                 path = project_folder)\n",
-        "\n",
-        "automl_sample_weight = AutoMLConfig(task = 'classification',\n",
-        "                                    debug_log = 'automl_errors.log',\n",
-        "                                    primary_metric = 'AUC_weighted',\n",
-        "                                    max_time_sec = 3600,\n",
-        "                                    iterations = 10,\n",
-        "                                    n_cross_validations = 2,\n",
-        "                                    verbosity = logging.INFO,\n",
-        "                                    X = X_train, \n",
-        "                                    y = y_train,\n",
-        "                                    sample_weight = sample_weight,\n",
-        "                                    path = project_folder)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train the Models\n",
-        "\n",
-        "Call the `submit` method on the experiment objects and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
-        "In this example, we specify `show_output = True` to print currently running iterations to the console."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_run = experiment.submit(automl_classifier, show_output = True)\n",
-        "sample_weight_run = sample_weight_experiment.submit(automl_sample_weight, show_output = True)\n",
-        "\n",
-        "best_run, fitted_model = local_run.get_output()\n",
-        "best_run_sample_weight, fitted_model_sample_weight = sample_weight_run.get_output()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Test the Best Fitted Model\n",
-        "\n",
-        "#### Load Test Data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "digits = datasets.load_digits()\n",
-        "X_test = digits.data[:100, :]\n",
-        "y_test = digits.target[:100]\n",
-        "images = digits.images[:100]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Compare the Models\n",
-        "The prediction from the sample weight model is more likely to correctly predict 4's.  However, it is also more likely to predict 4 for some images that are not labelled as 4."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Randomly select digits and test.\n",
-        "for index in range(0,len(y_test)):\n",
-        "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
-        "    predicted_sample_weight = fitted_model_sample_weight.predict(X_test[index:index + 1])[0]\n",
-        "    label = y_test[index]\n",
-        "    if predicted == 4 or predicted_sample_weight == 4 or label == 4:\n",
-        "        title = \"Label value = %d  Predicted value = %d Prediced with sample weight = %d\" % (label, predicted, predicted_sample_weight)\n",
-        "        fig = plt.figure(1, figsize=(3,3))\n",
-        "        ax1 = fig.add_axes((0,0,.8,.8))\n",
-        "        ax1.set_title(title)\n",
-        "        plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
-        "        plt.show()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "savitam"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.5"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 11: Sample Weight\n",
+    "\n",
+    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use sample weight with AutoML. Sample weight is used where some sample values are more important than others.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you will learn how to configure AutoML to use `sample_weight` and you will see the difference sample weight makes to the test results.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose names for the regular and the sample weight experiments.\n",
+    "experiment_name = 'non_sample_weight_experiment'\n",
+    "sample_weight_experiment_name = 'sample_weight_experiment'\n",
+    "\n",
+    "project_folder = './sample_projects/automl-local-classification'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "sample_weight_experiment=Experiment(ws, sample_weight_experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "Instantiate two `AutoMLConfig` objects. One will be used with `sample_weight` and one without."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_train = digits.data[100:,:]\n",
+    "y_train = digits.target[100:]\n",
+    "\n",
+    "# The example makes the sample weight 0.9 for the digit 4 and 0.1 for all other digits.\n",
+    "# This makes the model more likely to classify as 4 if the image it not clear.\n",
+    "sample_weight = np.array([(0.9 if x == 4 else 0.01) for x in y_train])\n",
+    "\n",
+    "automl_classifier = AutoMLConfig(task = 'classification',\n",
+    "                                 debug_log = 'automl_errors.log',\n",
+    "                                 primary_metric = 'AUC_weighted',\n",
+    "                                 iteration_timeout_minutes = 60,\n",
+    "                                 iterations = 10,\n",
+    "                                 n_cross_validations = 2,\n",
+    "                                 verbosity = logging.INFO,\n",
+    "                                 X = X_train, \n",
+    "                                 y = y_train,\n",
+    "                                 path = project_folder)\n",
+    "\n",
+    "automl_sample_weight = AutoMLConfig(task = 'classification',\n",
+    "                                    debug_log = 'automl_errors.log',\n",
+    "                                    primary_metric = 'AUC_weighted',\n",
+    "                                    iteration_timeout_minutes = 60,\n",
+    "                                    iterations = 10,\n",
+    "                                    n_cross_validations = 2,\n",
+    "                                    verbosity = logging.INFO,\n",
+    "                                    X = X_train, \n",
+    "                                    y = y_train,\n",
+    "                                    sample_weight = sample_weight,\n",
+    "                                    path = project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models\n",
+    "\n",
+    "Call the `submit` method on the experiment objects and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
+    "In this example, we specify `show_output = True` to print currently running iterations to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_classifier, show_output = True)\n",
+    "sample_weight_run = sample_weight_experiment.submit(automl_sample_weight, show_output = True)\n",
+    "\n",
+    "best_run, fitted_model = local_run.get_output()\n",
+    "best_run_sample_weight, fitted_model_sample_weight = sample_weight_run.get_output()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test the Best Fitted Model\n",
+    "\n",
+    "#### Load Test Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:100, :]\n",
+    "y_test = digits.target[:100]\n",
+    "images = digits.images[:100]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Compare the Models\n",
+    "The prediction from the sample weight model is more likely to correctly predict 4's.  However, it is also more likely to predict 4 for some images that are not labelled as 4."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Randomly select digits and test.\n",
+    "for index in range(0,len(y_test)):\n",
+    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+    "    predicted_sample_weight = fitted_model_sample_weight.predict(X_test[index:index + 1])[0]\n",
+    "    label = y_test[index]\n",
+    "    if predicted == 4 or predicted_sample_weight == 4 or label == 4:\n",
+    "        title = \"Label value = %d  Predicted value = %d Prediced with sample weight = %d\" % (label, predicted, predicted_sample_weight)\n",
+    "        fig = plt.figure(1, figsize=(3,3))\n",
+    "        ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "        ax1.set_title(title)\n",
+    "        plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "        plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

automl/12.auto-ml-retrieve-the-training-sdk-versions.ipynb

@@ -1,251 +1,227 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# AutoML 12: Retrieving Training SDK Versions\n",
-        "\n",
-        "This example shows how to find the SDK versions used for an experiment.\n",
-        "\n",
-        "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import logging\n",
-        "import os\n",
-        "import random\n",
-        "\n",
-        "from matplotlib import pyplot as plt\n",
-        "from matplotlib.pyplot import imshow\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "from sklearn import datasets\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl.run import AutoMLRun\n",
-        "from azureml.train.automl.utilities import get_sdk_dependencies"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Diagnostics\n",
-        "\n",
-        "Opt-in diagnostics for better experience, quality, and security of future releases."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.telemetry import set_diagnostics_collection\n",
-        "set_diagnostics_collection(send_diagnostics = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Retrieve the SDK versions in the current environment"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To retrieve the SDK versions in the current environment, run `get_sdk_dependencies`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "get_sdk_dependencies()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Train models using AutoML"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "# Choose a name for the experiment and specify the project folder.\n",
-        "experiment_name = 'automl-local-classification'\n",
-        "project_folder = './sample_projects/automl-local-classification'\n",
-        "\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "\n",
-        "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "pd.DataFrame(data=output, index=['']).T"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "digits = datasets.load_digits()\n",
-        "X_train = digits.data[10:,:]\n",
-        "y_train = digits.target[10:]\n",
-        "\n",
-        "automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             debug_log = 'automl_errors.log',\n",
-        "                             primary_metric = 'AUC_weighted',\n",
-        "                             iterations = 3,\n",
-        "                             n_cross_validations = 2,\n",
-        "                             verbosity = logging.INFO,\n",
-        "                             X = X_train, \n",
-        "                             y = y_train,\n",
-        "                             path = project_folder)\n",
-        "\n",
-        "local_run = experiment.submit(automl_config, show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Retrieve the SDK versions from RunHistory"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To get the SDK versions from RunHistory, first the run id needs to be recorded. This can either be done by copying it from the output message or by retrieving it after each run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Use a run id copied from an output message.\n",
-        "#run_id = 'AutoML_c0585b1f-a0e6-490b-84c7-3a099468b28e'\n",
-        "\n",
-        "# Retrieve the run id from a run.\n",
-        "run_id = local_run.id\n",
-        "print(run_id)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Initialize a new `AutoMLRun` object."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "experiment_name = 'automl-local-classification'\n",
-        "\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "ml_run = AutoMLRun(experiment = experiment, run_id = run_id)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Get parent training SDK versions."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ml_run.get_run_sdk_dependencies()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Get the traning SDK versions of a specific run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ml_run.get_run_sdk_dependencies(iteration = 2)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "savitam"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 12: Retrieving Training SDK Versions\n",
+    "\n",
+    "This example shows how to find the SDK versions used for an experiment.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train models using AutoML"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose a name for the experiment and specify the project folder.\n",
+    "experiment_name = 'automl-local-classification'\n",
+    "project_folder = './sample_projects/automl-local-classification'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data=output, index=['']).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_train = digits.data[10:,:]\n",
+    "y_train = digits.target[10:]\n",
+    "\n",
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             primary_metric = 'AUC_weighted',\n",
+    "                             iterations = 3,\n",
+    "                             n_cross_validations = 2,\n",
+    "                             verbosity = logging.INFO,\n",
+    "                             X = X_train, \n",
+    "                             y = y_train,\n",
+    "                             path = project_folder)\n",
+    "\n",
+    "local_run = experiment.submit(automl_config, show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Retrieve the SDK versions from RunHistory"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get the SDK versions from RunHistory, first the run id needs to be recorded. This can either be done by copying it from the output message or by retrieving it after each run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use a run id copied from an output message.\n",
+    "#run_id = 'AutoML_c0585b1f-a0e6-490b-84c7-3a099468b28e'\n",
+    "\n",
+    "# Retrieve the run id from a run.\n",
+    "run_id = local_run.id\n",
+    "print(run_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initialize a new `AutoMLRun` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment_name = 'automl-local-classification'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "ml_run = AutoMLRun(experiment = experiment, run_id = run_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get parent training SDK versions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ml_run.get_run_sdk_dependencies()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the traning SDK versions of a specific run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ml_run.get_run_sdk_dependencies(iteration = 2)"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

automl/13.auto-ml-dataprep.ipynb

@@ -1,558 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# AutoML 13: Prepare Data using `azureml.dataprep`\n",
-        "In this example we showcase how you can use the `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone; full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
-        "\n",
-        "Make sure you have executed the [setup](00.configuration.ipynb) before running this notebook.\n",
-        "\n",
-        "In this notebook you will learn how to:\n",
-        "1. Define data loading and preparation steps in a `Dataflow` using `azureml.dataprep`.\n",
-        "2. Pass the `Dataflow` to AutoML for a local run.\n",
-        "3. Pass the `Dataflow` to AutoML for a remote run."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Install `azureml.dataprep` SDK"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "!pip install azureml-dataprep"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Diagnostics\n",
-        "\n",
-        "Opt-in diagnostics for better experience, quality, and security of future releases."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.telemetry import set_diagnostics_collection\n",
-        "set_diagnostics_collection(send_diagnostics = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create an Experiment\n",
-        "\n",
-        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import logging\n",
-        "import os\n",
-        "\n",
-        "import pandas as pd\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core.compute import DsvmCompute\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.core.runconfig import CondaDependencies\n",
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "from azureml.core.workspace import Workspace\n",
-        "import azureml.dataprep as dprep\n",
-        "from azureml.train.automl import AutoMLConfig"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        " \n",
-        "# choose a name for experiment\n",
-        "experiment_name = 'automl-dataprep-classification'\n",
-        "# project folder\n",
-        "project_folder = './sample_projects/automl-dataprep-classification'\n",
-        " \n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        " \n",
-        "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace Name'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "pd.DataFrame(data = output, index = ['']).T"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Loading Data using DataPrep"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# You can use `smart_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
-        "# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
-        "simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
-        "X = dprep.smart_read_file(simple_example_data_root + 'X.csv').skip(1)  # Remove the header row.\n",
-        "\n",
-        "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
-        "# and convert column types manually.\n",
-        "# Here we read a comma delimited file and convert all columns to integers.\n",
-        "y = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Review the Data Preparation Result\n",
-        "\n",
-        "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "X.skip(1).head(5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Configure AutoML\n",
-        "\n",
-        "This creates a general AutoML settings object applicable for both local and remote runs."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_settings = {\n",
-        "    \"max_time_sec\" : 600,\n",
-        "    \"iterations\" : 2,\n",
-        "    \"primary_metric\" : 'AUC_weighted',\n",
-        "    \"preprocess\" : False,\n",
-        "    \"verbosity\" : logging.INFO,\n",
-        "    \"n_cross_validations\": 3\n",
-        "}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Local Run"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Pass Data with `Dataflow` Objects\n",
-        "\n",
-        "The `Dataflow` objects captured above can be passed to the `submit` method for a local run. AutoML will retrieve the results from the `Dataflow` for model training."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             debug_log = 'automl_errors.log',\n",
-        "                             X = X,\n",
-        "                             y = y,\n",
-        "                             **automl_settings)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "local_run = experiment.submit(automl_config, show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Remote Run"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create or Attach a Remote Linux DSVM"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dsvm_name = 'mydsvm'\n",
-        "try:\n",
-        "    dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
-        "    print('Found existing DVSM.')\n",
-        "except:\n",
-        "    print('Creating a new DSVM.')\n",
-        "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
-        "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
-        "    dsvm_compute.wait_for_completion(show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Update Conda Dependency file to have AutoML and DataPrep SDK\n",
-        "\n",
-        "Currently the AutoML and DataPrep SDKs are not installed with the Azure ML SDK by default. To circumvent this limitation, we update the conda dependency file to add these dependencies."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "cd = CondaDependencies()\n",
-        "cd.add_pip_package(pip_package='azureml-dataprep')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create a `RunConfiguration` with DSVM name"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run_config = RunConfiguration(conda_dependencies=cd)\n",
-        "run_config.target = dsvm_compute\n",
-        "run_config.auto_prepare_environment = True"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Pass Data with `Dataflow` Objects\n",
-        "\n",
-        "The `Dataflow` objects captured above can also be passed to the `submit` method for a remote run. AutoML will serialize the `Dataflow` object and send it to the remote compute target. The `Dataflow` will not be evaluated locally."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             debug_log = 'automl_errors.log',\n",
-        "                             path = project_folder,\n",
-        "                             run_configuration = run_config,\n",
-        "                             X = X,\n",
-        "                             y = y,\n",
-        "                             **automl_settings)\n",
-        "remote_run = experiment.submit(automl_config, show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explore the Results"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Widget for Monitoring Runs\n",
-        "\n",
-        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
-        "\n",
-        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.widgets import RunDetails\n",
-        "RunDetails(local_run).show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Retrieve All Child Runs\n",
-        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "children = list(local_run.get_children())\n",
-        "metricslist = {}\n",
-        "for run in children:\n",
-        "    properties = run.get_properties()\n",
-        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
-        "    metricslist[int(properties['iteration'])] = metrics\n",
-        "    \n",
-        "import pandas as pd\n",
-        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
-        "rundata"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Retrieve the Best Model\n",
-        "\n",
-        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "best_run, fitted_model = local_run.get_output()\n",
-        "print(best_run)\n",
-        "print(fitted_model)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Best Model Based on Any Other Metric\n",
-        "Show the run and the model that has the smallest `log_loss` value:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "lookup_metric = \"log_loss\"\n",
-        "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
-        "print(best_run)\n",
-        "print(fitted_model)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Model from a Specific Iteration\n",
-        "Show the run and the model from the first iteration:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "iteration = 0\n",
-        "best_run, fitted_model = local_run.get_output(iteration = iteration)\n",
-        "print(best_run)\n",
-        "print(fitted_model)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Test the Best Fitted Model\n",
-        "\n",
-        "#### Load Test Data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn import datasets\n",
-        "\n",
-        "digits = datasets.load_digits()\n",
-        "X_test = digits.data[:10, :]\n",
-        "y_test = digits.target[:10]\n",
-        "images = digits.images[:10]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Testing Our Best Fitted Model\n",
-        "We will try to predict 2 digits and see how our model works."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#Randomly select digits and test\n",
-        "from matplotlib import pyplot as plt\n",
-        "from matplotlib.pyplot import imshow\n",
-        "import random\n",
-        "import numpy as np\n",
-        "\n",
-        "for index in np.random.choice(len(y_test), 2, replace = False):\n",
-        "    print(index)\n",
-        "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
-        "    label = y_test[index]\n",
-        "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
-        "    fig = plt.figure(1, figsize=(3,3))\n",
-        "    ax1 = fig.add_axes((0,0,.8,.8))\n",
-        "    ax1.set_title(title)\n",
-        "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
-        "    plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Appendix"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Capture the `Dataflow` Objects for Later Use in AutoML\n",
-        "\n",
-        "`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object can be branched at any point for further usage."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# sklearn.digits.data + target\n",
-        "digits_complete = dprep.smart_read_file('https://dprepdata.blob.core.windows.net/automl-notebook-data/digits-complete.csv')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "`digits_complete` (sourced from `sklearn.datasets.load_digits()`) is forked into `dflow_X` to capture all the feature columns and `dflow_y` to capture the label column."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "digits_complete.to_pandas_dataframe().shape\n",
-        "labels_column = 'Column64'\n",
-        "dflow_X = digits_complete.drop_columns(columns = [labels_column])\n",
-        "dflow_y = digits_complete.keep_columns(columns = [labels_column])"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "savitam"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.5"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

automl/13a.auto-ml-dataprep.ipynb

@@ -0,0 +1,446 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 13: Prepare Data using `azureml.dataprep` for Local Execution\n",
+    "In this example we showcase how you can use the `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone; full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
+    "\n",
+    "Make sure you have executed the [setup](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you will learn how to:\n",
+    "1. Define data loading and preparation steps in a `Dataflow` using `azureml.dataprep`.\n",
+    "2. Pass the `Dataflow` to AutoML for a local run.\n",
+    "3. Pass the `Dataflow` to AutoML for a remote run."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "import azureml.dataprep as dprep\n",
+    "from azureml.train.automl import AutoMLConfig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    " \n",
+    "# choose a name for experiment\n",
+    "experiment_name = 'automl-dataprep-local'\n",
+    "# project folder\n",
+    "project_folder = './sample_projects/automl-dataprep-local'\n",
+    " \n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    " \n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading Data using DataPrep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
+    "# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
+    "simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
+    "X = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1)  # Remove the header row.\n",
+    "\n",
+    "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
+    "# and convert column types manually.\n",
+    "# Here we read a comma delimited file and convert all columns to integers.\n",
+    "y = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Review the Data Preparation Result\n",
+    "\n",
+    "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X.skip(1).head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "This creates a general AutoML settings object applicable for both local and remote runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_settings = {\n",
+    "    \"iteration_timeout_minutes\" : 10,\n",
+    "    \"iterations\" : 2,\n",
+    "    \"primary_metric\" : 'AUC_weighted',\n",
+    "    \"preprocess\" : False,\n",
+    "    \"verbosity\" : logging.INFO,\n",
+    "    \"n_cross_validations\": 3\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Local Run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pass Data with `Dataflow` Objects\n",
+    "\n",
+    "The `Dataflow` objects captured above can be passed to the `submit` method for a local run. AutoML will retrieve the results from the `Dataflow` for model training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             X = X,\n",
+    "                             y = y,\n",
+    "                             **automl_settings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(local_run).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(local_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "    \n",
+    "import pandas as pd\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model that has the smallest `log_loss` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lookup_metric = \"log_loss\"\n",
+    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration\n",
+    "Show the run and the model from the first iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 0\n",
+    "best_run, fitted_model = local_run.get_output(iteration = iteration)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test the Best Fitted Model\n",
+    "\n",
+    "#### Load Test Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "\n",
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:10, :]\n",
+    "y_test = digits.target[:10]\n",
+    "images = digits.images[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Testing Our Best Fitted Model\n",
+    "We will try to predict 2 digits and see how our model works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Randomly select digits and test\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import random\n",
+    "import numpy as np\n",
+    "\n",
+    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+    "    print(index)\n",
+    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+    "    label = y_test[index]\n",
+    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
+    "    fig = plt.figure(1, figsize=(3,3))\n",
+    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "    ax1.set_title(title)\n",
+    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Appendix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Capture the `Dataflow` Objects for Later Use in AutoML\n",
+    "\n",
+    "`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object can be branched at any point for further usage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sklearn.digits.data + target\n",
+    "digits_complete = dprep.auto_read_file('https://dprepdata.blob.core.windows.net/automl-notebook-data/digits-complete.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`digits_complete` (sourced from `sklearn.datasets.load_digits()`) is forked into `dflow_X` to capture all the feature columns and `dflow_y` to capture the label column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits_complete.to_pandas_dataframe().shape\n",
+    "labels_column = 'Column64'\n",
+    "dflow_X = digits_complete.drop_columns(columns = [labels_column])\n",
+    "dflow_y = digits_complete.keep_columns(columns = [labels_column])"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

automl/13b.auto-ml-dataprep-remote-execution.ipynb

@@ -0,0 +1,495 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 13: Prepare Data using `azureml.dataprep` for Remote Execution (DSVM)\n",
+    "In this example we showcase how you can use the `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone; full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
+    "\n",
+    "Make sure you have executed the [setup](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you will learn how to:\n",
+    "1. Define data loading and preparation steps in a `Dataflow` using `azureml.dataprep`.\n",
+    "2. Pass the `Dataflow` to AutoML for a local run.\n",
+    "3. Pass the `Dataflow` to AutoML for a remote run."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import time\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.compute import DsvmCompute\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "import azureml.dataprep as dprep\n",
+    "from azureml.train.automl import AutoMLConfig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    " \n",
+    "# choose a name for experiment\n",
+    "experiment_name = 'automl-dataprep-remote-dsvm'\n",
+    "# project folder\n",
+    "project_folder = './sample_projects/automl-dataprep-remote-dsvm'\n",
+    " \n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    " \n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading Data using DataPrep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
+    "# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
+    "simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
+    "X = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1)  # Remove the header row.\n",
+    "\n",
+    "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
+    "# and convert column types manually.\n",
+    "# Here we read a comma delimited file and convert all columns to integers.\n",
+    "y = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Review the Data Preparation Result\n",
+    "\n",
+    "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X.skip(1).head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "This creates a general AutoML settings object applicable for both local and remote runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_settings = {\n",
+    "    \"iteration_timeout_minutes\" : 10,\n",
+    "    \"iterations\" : 2,\n",
+    "    \"primary_metric\" : 'AUC_weighted',\n",
+    "    \"preprocess\" : False,\n",
+    "    \"verbosity\" : logging.INFO,\n",
+    "    \"n_cross_validations\": 3\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Remote Run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create or Attach a Remote Linux DSVM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dsvm_name = 'mydsvmd'\n",
+    "\n",
+    "try:\n",
+    "    while ws.compute_targets[dsvm_name].provisioning_state == 'Creating':\n",
+    "        time.sleep(1)\n",
+    "        \n",
+    "    dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
+    "    print('Found existing DVSM.')\n",
+    "except:\n",
+    "    print('Creating a new DSVM.')\n",
+    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
+    "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
+    "    dsvm_compute.wait_for_completion(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.runconfig import RunConfiguration\n",
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "\n",
+    "conda_run_config = RunConfiguration(framework=\"python\")\n",
+    "\n",
+    "conda_run_config.target = dsvm_compute\n",
+    "\n",
+    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
+    "conda_run_config.environment.python.conda_dependencies = cd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pass Data with `Dataflow` Objects\n",
+    "\n",
+    "The `Dataflow` objects captured above can also be passed to the `submit` method for a remote run. AutoML will serialize the `Dataflow` object and send it to the remote compute target. The `Dataflow` will not be evaluated locally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             path = project_folder,\n",
+    "                             run_configuration=conda_run_config,\n",
+    "                             X = X,\n",
+    "                             y = y,\n",
+    "                             **automl_settings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remote_run = experiment.submit(automl_config, show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(remote_run).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(remote_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "    \n",
+    "import pandas as pd\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = remote_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model that has the smallest `log_loss` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lookup_metric = \"log_loss\"\n",
+    "best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration\n",
+    "Show the run and the model from the first iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 0\n",
+    "best_run, fitted_model = remote_run.get_output(iteration = iteration)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test the Best Fitted Model\n",
+    "\n",
+    "#### Load Test Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "\n",
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:10, :]\n",
+    "y_test = digits.target[:10]\n",
+    "images = digits.images[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Testing Our Best Fitted Model\n",
+    "We will try to predict 2 digits and see how our model works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Randomly select digits and test\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import random\n",
+    "import numpy as np\n",
+    "\n",
+    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+    "    print(index)\n",
+    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+    "    label = y_test[index]\n",
+    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
+    "    fig = plt.figure(1, figsize=(3,3))\n",
+    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "    ax1.set_title(title)\n",
+    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Appendix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Capture the `Dataflow` Objects for Later Use in AutoML\n",
+    "\n",
+    "`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object can be branched at any point for further usage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sklearn.digits.data + target\n",
+    "digits_complete = dprep.auto_read_file('https://dprepdata.blob.core.windows.net/automl-notebook-data/digits-complete.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`digits_complete` (sourced from `sklearn.datasets.load_digits()`) is forked into `dflow_X` to capture all the feature columns and `dflow_y` to capture the label column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits_complete.to_pandas_dataframe().shape\n",
+    "labels_column = 'Column64'\n",
+    "dflow_X = digits_complete.drop_columns(columns = [labels_column])\n",
+    "dflow_y = digits_complete.keep_columns(columns = [labels_column])"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

automl/14.auto-ml-model-explanation.ipynb

@@ -0,0 +1,374 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 14: Explain classification model and visualize the explanation\n",
+    "\n",
+    "In this example we use the sklearn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) to showcase how you can use the AutoML Classifier for a simple classification problem.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you would see\n",
+    "1. Creating an Experiment in an existing Workspace\n",
+    "2. Instantiating AutoMLConfig\n",
+    "3. Training the Model using local compute and explain the model\n",
+    "4. Visualization model's feature importance in widget\n",
+    "5. Explore best model's explanation\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Install AzureML Explainer SDK "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install azureml_sdk[explain]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Experiment\n",
+    "\n",
+    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "import pandas as pd\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# choose a name for experiment\n",
+    "experiment_name = 'automl-local-classification'\n",
+    "# project folder\n",
+    "project_folder = './sample_projects/automl-local-classification-model-explanation'\n",
+    "\n",
+    "experiment=Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Iris Data Set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "\n",
+    "iris = datasets.load_iris()\n",
+    "y = iris.target\n",
+    "X = iris.data\n",
+    "\n",
+    "features = iris.feature_names\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
+    "                                                    y,\n",
+    "                                                    test_size=0.1,\n",
+    "                                                    random_state=100,\n",
+    "                                                    stratify=y)\n",
+    "\n",
+    "X_train = pd.DataFrame(X_train, columns=features)\n",
+    "X_test = pd.DataFrame(X_test, columns=features)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiate Auto ML Config\n",
+    "\n",
+    "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|classification or regression|\n",
+    "|**primary_metric**|This is the metric that you want to optimize.<br> Classification supports the following primary metrics <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
+    "|**max_time_sec**|Time limit in minutes for each iterations|\n",
+    "|**iterations**|Number of iterations. In each iteration Auto ML trains the data with a specific pipeline|\n",
+    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
+    "|**X_valid**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y_valid**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]|\n",
+    "|**model_explainability**|Indicate to explain each trained pipeline or not |\n",
+    "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             primary_metric = 'AUC_weighted',\n",
+    "                             max_time_sec = 12000,\n",
+    "                             iterations = 10,\n",
+    "                             verbosity = logging.INFO,\n",
+    "                             X = X_train, \n",
+    "                             y = y_train,\n",
+    "                             X_valid = X_test,\n",
+    "                             y_valid = y_test,\n",
+    "                             model_explainability=True,\n",
+    "                             path=project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the Model\n",
+    "\n",
+    "You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
+    "You will see the currently running iterations printing to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exploring the results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Widget for monitoring runs\n",
+    "\n",
+    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(local_run).show() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "child_run = next(local_run.get_children())\n",
+    "RunDetails(child_run).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The *get_output* method on automl_classifier returns the best run and the fitted model for the last *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Best Model 's explanation\n",
+    "\n",
+    "Retrieve the explanation from the best_run. And explanation information includes:\n",
+    "\n",
+    "1.\tshap_values: The explanation information generated by shap lib\n",
+    "2.\texpected_values: The expected value of the model applied to set of X_train data.\n",
+    "3.\toverall_summary: The model level feature importance values sorted in descending order\n",
+    "4.\toverall_imp: The feature names sorted in the same order as in overall_summary\n",
+    "5.\tper_class_summary: The class level feature importance values sorted in descending order. Only available for the classification case\n",
+    "6.\tper_class_imp: The feature names sorted in the same order as in per_class_summary. Only available for the classification case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.train.automl.automlexplainer import retrieve_model_explanation\n",
+    "\n",
+    "shap_values, expected_values, overall_summary, overall_imp, per_class_summary, per_class_imp = \\\n",
+    "    retrieve_model_explanation(best_run)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(overall_summary)\n",
+    "print(overall_imp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(per_class_summary)\n",
+    "print(per_class_imp)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Beside retrieve the existed model explanation information, explain the model with different train/test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.train.automl.automlexplainer import explain_model\n",
+    "\n",
+    "shap_values, expected_values, overall_summary, overall_imp, per_class_summary, per_class_imp = \\\n",
+    "    explain_model(fitted_model, X_train, X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(overall_summary)\n",
+    "print(overall_imp)"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "xif"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

automl/15a.auto-ml-classification-ensemble.ipynb

@@ -0,0 +1,423 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 15a: Classification with ensembling on local compute\n",
+    "\n",
+    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) to showcase how you can use AutoML for a simple classification problem.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you will learn how to:\n",
+    "1. Create an `Experiment` in an existing `Workspace`.\n",
+    "2. Configure AutoML using `AutoMLConfig` which enables an extra ensembling iteration.\n",
+    "3. Train the model using local compute.\n",
+    "4. Explore the results.\n",
+    "5. Test the best fitted model.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose a name for the experiment and specify the project folder.\n",
+    "experiment_name = 'automl-local-classification'\n",
+    "project_folder = './sample_projects/automl-local-classification'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Training Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "\n",
+    "digits = datasets.load_digits()\n",
+    "\n",
+    "# Exclude the first 50 rows from training so that they can be used for test.\n",
+    "X_train = digits.data[150:,:]\n",
+    "y_train = digits.target[150:]\n",
+    "X_valid = digits.data[50:150]\n",
+    "y_valid = digits.target[50:150]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|classification or regression|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**n_cross_validations**|Number of cross validation splits.|\n",
+    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
+    "|**X_valid**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y_valid**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
+    "|**enable_ensembling**|Flag to enable an ensembling iteration after all the other iterations complete.|\n",
+    "|**ensemble_iterations**|Number of iterations during which we choose a fitted pipeline to be part of the final ensemble.|\n",
+    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'classification.log',\n",
+    "                             primary_metric = 'AUC_weighted',\n",
+    "                             iteration_timeout_minutes = 60,\n",
+    "                             iterations = 10,\n",
+    "                             verbosity = logging.INFO,\n",
+    "                             X = X_train, \n",
+    "                             y = y_train,\n",
+    "                             X_valid = X_valid,\n",
+    "                             y_valid = y_valid,\n",
+    "                             enable_ensembling = True,\n",
+    "                             ensemble_iterations = 5,\n",
+    "                             path = project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Model\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
+    "In this example, we specify `show_output = True` to print currently running iterations to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Optionally, you can continue an interrupted local run by calling `continue_experiment` without the `iterations` parameter, or run more iterations for a completed run by specifying the `iterations` parameter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = local_run.continue_experiment(X = X_train, \n",
+    "                                          y = y_train,\n",
+    "                                          X_valid = X_valid,\n",
+    "                                          y_valid = y_valid,\n",
+    "                                          show_output = True,\n",
+    "                                          iterations = 5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(local_run).show() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(local_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model that has the smallest `log_loss` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lookup_metric = \"log_loss\"\n",
+    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration\n",
+    "Show the run and the model from the third iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 3\n",
+    "third_run, third_model = local_run.get_output(iteration = iteration)\n",
+    "print(third_run)\n",
+    "print(third_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test the Best Fitted Model\n",
+    "\n",
+    "#### Load Test Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:10, :]\n",
+    "y_test = digits.target[:10]\n",
+    "images = digits.images[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Testing Our Best Pipeline\n",
+    "We will try to predict 2 digits and see how our model works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Randomly select digits and test.\n",
+    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+    "    print(index)\n",
+    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+    "    label = y_test[index]\n",
+    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
+    "    fig = plt.figure(1, figsize = (3,3))\n",
+    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "    ax1.set_title(title)\n",
+    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "    plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "ratanase"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

automl/15b.auto-ml-regression-ensemble.ipynb

@@ -0,0 +1,449 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 15b: Regression with ensembling on remote compute\n",
+    "\n",
+    "In this example we use the scikit-learn's [diabetes dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html) to showcase how you can use AutoML for a simple regression problem.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you will learn how to:\n",
+    "1. Create an `Experiment` in an existing `Workspace`.\n",
+    "2. Configure AutoML using `AutoMLConfig`which enables an extra ensembling iteration.\n",
+    "3. Train the model using remote compute.\n",
+    "4. Explore the results.\n",
+    "5. Test the best fitted model.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose a name for the experiment and specify the project folder.\n",
+    "experiment_name = 'automl-local-regression'\n",
+    "project_folder = './sample_projects/automl-local-regression'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a Remote Linux DSVM\n",
+    "**Note:** If creation fails with a message about Marketplace purchase eligibilty, start creation of a DSVM through the [Azure portal](https://portal.azure.com), and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled this setting, you can exit the portal without actually creating the DSVM, and creation of the DSVM through the notebook should work."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import DsvmCompute\n",
+    "\n",
+    "dsvm_name = 'mydsvm'\n",
+    "try:\n",
+    "    dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
+    "    print('Found an existing DSVM.')\n",
+    "except:\n",
+    "    print('Creating a new DSVM.')\n",
+    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
+    "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
+    "    dsvm_compute.wait_for_completion(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Get Data File\n",
+    "For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
+    "In this example, the `get_data()` function returns data using scikit-learn's `diabetes` dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(project_folder):\n",
+    "    os.makedirs(project_folder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile $project_folder/get_data.py\n",
+    "\n",
+    "# Load the diabetes dataset, a well-known built-in small dataset that comes with scikit-learn.\n",
+    "from sklearn.datasets import load_diabetes\n",
+    "from sklearn.linear_model import Ridge\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "def get_data():\n",
+    "    X, y = load_diabetes(return_X_y = True)\n",
+    "\n",
+    "    columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
+    "\n",
+    "    X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
+    "    X_valid, X_test, y_valid, y_test = train_test_split(X_temp, y_temp, test_size = 0.5, random_state = 0)\n",
+    "    return { \"X\" : X_train, \"y\" : y_train, \"X_valid\": X_valid, \"y_valid\": y_valid }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|classification or regression|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**enable_ensembling**|Flag to enable an ensembling iteration after all the other iterations complete.|\n",
+    "|**ensemble_iterations**|Number of iterations during which we choose a fitted pipeline to be part of the final ensemble.|\n",
+    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'regression',\n",
+    "                             iteration_timeout_minutes = 10,\n",
+    "                             iterations = 20,\n",
+    "                             primary_metric = 'spearman_correlation',\n",
+    "                             debug_log = 'regression.log',\n",
+    "                             verbosity = logging.INFO,\n",
+    "                             compute_target = dsvm_compute,\n",
+    "                             data_script = project_folder + \"/get_data.py\",\n",
+    "                             enable_ensembling = True,\n",
+    "                             ensemble_iterations = 5,\n",
+    "                             path = project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Model\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
+    "In this example, we specify `show_output = True` to print currently running iterations to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(local_run).show() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(local_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model that has the smallest `root_mean_squared_error` value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lookup_metric = \"root_mean_squared_error\"\n",
+    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test the Best Model (Ensemble)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Predict on training and test set, and calculate residual values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_diabetes\n",
+    "from sklearn.linear_model import Ridge\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X, y = load_diabetes(return_X_y = True)\n",
+    "\n",
+    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
+    "X_valid, X_test, y_valid, y_test = train_test_split(X_temp, y_temp, test_size = 0.5, random_state = 0)\n",
+    "\n",
+    "\n",
+    "y_pred_train = fitted_model.predict(X_train)\n",
+    "y_residual_train = y_train - y_pred_train\n",
+    "\n",
+    "y_pred_test = fitted_model.predict(X_test)\n",
+    "y_residual_test = y_test - y_pred_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import datasets\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "\n",
+    "# Set up a multi-plot chart.\n",
+    "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[1, 1], 'wspace':0, 'hspace': 0})\n",
+    "f.suptitle('Regression Residual Values', fontsize = 18)\n",
+    "f.set_figheight(6)\n",
+    "f.set_figwidth(16)\n",
+    "\n",
+    "# Plot residual values of training set.\n",
+    "a0.axis([0, 360, -200, 200])\n",
+    "a0.plot(y_residual_train, 'bo', alpha = 0.5)\n",
+    "a0.plot([-10,360],[0,0], 'r-', lw = 3)\n",
+    "a0.text(16,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_train, y_pred_train))), fontsize = 12)\n",
+    "a0.text(16,140,'R2 score = {0:.2f}'.format(r2_score(y_train, y_pred_train)), fontsize = 12)\n",
+    "a0.set_xlabel('Training samples', fontsize = 12)\n",
+    "a0.set_ylabel('Residual Values', fontsize = 12)\n",
+    "\n",
+    "# Plot a histogram.\n",
+    "a0.hist(y_residual_train, orientation = 'horizontal', color = 'b', bins = 10, histtype = 'step');\n",
+    "a0.hist(y_residual_train, orientation = 'horizontal', color = 'b', alpha = 0.2, bins = 10);\n",
+    "\n",
+    "# Plot residual values of test set.\n",
+    "a1.axis([0, 90, -200, 200])\n",
+    "a1.plot(y_residual_test, 'bo', alpha = 0.5)\n",
+    "a1.plot([-10,360],[0,0], 'r-', lw = 3)\n",
+    "a1.text(5,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_test, y_pred_test))), fontsize = 12)\n",
+    "a1.text(5,140,'R2 score = {0:.2f}'.format(r2_score(y_test, y_pred_test)), fontsize = 12)\n",
+    "a1.set_xlabel('Test samples', fontsize = 12)\n",
+    "a1.set_yticklabels([])\n",
+    "\n",
+    "# Plot a histogram.\n",
+    "a1.hist(y_residual_test, orientation = 'horizontal', color = 'b', bins = 10, histtype = 'step')\n",
+    "a1.hist(y_residual_test, orientation = 'horizontal', color = 'b', alpha = 0.2, bins = 10)\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "ratanase"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

automl/README.md

@@ -1,24 +1,52 @@
 # Table of Contents
-1. [Auto ML Introduction](#introduction)
-2. [Running samples in a Local Conda environment](#localconda)
-3. [Auto ML SDK Sample Notebooks](#samples)
-4. [Documentation](#documentation)
-5. [Running using python command](#pythoncommand)
-6. [Troubleshooting](#troubleshooting)
+1. [Automated ML Introduction](#introduction)
+1. [Running samples in Azure Notebooks](#jupyter)
+1. [Running samples in a Local Conda environment](#localconda)
+1. [Automated ML SDK Sample Notebooks](#samples)
+1. [Documentation](#documentation)
+1. [Running using python command](#pythoncommand)
+1. [Troubleshooting](#troubleshooting)
+
+<a name="introduction"></a>
+# Automated ML introduction 
+Automated machine learning (automated ML) builds high quality machine learning models for you by automating model and hyperparameter selection. Bring a labelled dataset that you want to build a model for, automated ML will give you a high quality machine learning model that you can use for predictions.
 
-# Auto ML Introduction <a name="introduction"></a>
-AutoML builds high quality Machine Learning models for you by automating model and hyperparameter selection. Bring a labelled dataset that you want to build a model for, AutoML will give you a high quality machine learning model that you can use for predictions.
 
 If you are new to Data Science, AutoML will help you get jumpstarted by simplifying machine learning model building. It abstracts you from needing to perform model selection, hyperparameter selection and in one step creates a high quality trained model for you to use.
 
 If you are an experienced data scientist, AutoML will help increase your productivity by intelligently performing the model and hyperparameter selection for your training and generates high quality models much quicker than manually specifying several combinations of the parameters and running training jobs. AutoML provides visibility and access to all the training jobs and the performance characteristics of the models to help you further tune the pipeline if you desire.
 
+ <a name="jupyter"></a>
+## Running samples in Azure Notebooks - Jupyter based notebooks in the Azure cloud 
 
-# Running samples in a Local Conda environment <a name="localconda"></a>
+1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
+[Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks.
+1. Follow the instructions in the [../00.configuration](00.configuration.ipynb) notebook to create and connect to a workspace.
+1. Open one of the sample notebooks.
+    
+    **Make sure the Azure Notebook kernel is set to `Python 3.6`** when you open a notebook. 
+    
+    ![set kernel to Python 3.6](../images/python36.png)
 
-You can run these notebooks in Azure Notebooks without any extra installation.  To run these notebook on your own notebook server, use these installation instructions.
+<a name="localconda"></a>
+## Running samples in a Local Conda environment 
+
+To run these notebook on your own notebook server, use these installation instructions.
+
+The instructions below will install everything you need and then start a Jupyter notebook.  To start your Jupyter notebook manually, use:
+
+```
+conda activate azure_automl
+jupyter notebook
+```
+
+or on Mac:
+
+```
+source activate azure_automl
+jupyter notebook
+```
 
-It is best if you create a new conda environment locally to try this SDK, so it doesn't mess up with your existing Python environment. 
 
 ### 1. Install mini-conda from [here](https://conda.io/miniconda.html), choose Python 3.7 or higher. 
 - **Note**: if you already have conda installed, you can keep using it but it should be version 4.4.10 or later (as shown by: conda -V).  If you have a previous version installed, you can update it using the command: conda update conda.
@@ -29,7 +57,7 @@ There's no need to install mini-conda specifically.
 
 ### 3. Setup a new conda environment
 The **automl/automl_setup** script creates a new conda environment, installs the necessary packages, configures the widget and starts a jupyter notebook.
-It takes the conda environment name as an optional parameter.  The default conda environment name is azure_automl.  The exact command depends on the operating system.  It can take about 30 minutes to execute.
+It takes the conda environment name as an optional parameter.  The default conda environment name is azure_automl.  The exact command depends on the operating system.  It can take about 10 minutes to execute.
 ## Windows
 Start a conda command windows, cd to the **automl** folder where the sample notebooks were extracted and then run: 
 ```
@@ -48,19 +76,19 @@ bash automl_setup_mac.sh
 cd to the **automl** folder where the sample notebooks were extracted and then run: 
 
 ```
-automl_setup_linux.sh
+bash automl_setup_linux.sh
 ```
 
 ### 4. Running configuration.ipynb
 - Before running any samples you next need to run the configuration notebook. Click on 00.configuration.ipynb notebook
-- Please make sure you use the Python [conda env:azure_automl] kernel when running this notebook.
 - Execute the cells in the notebook to Register Machine Learning Services Resource Provider and create a workspace. (*instructions in notebook*)
 
 ### 5. Running Samples
 - Please make sure you use the Python [conda env:azure_automl] kernel when trying the sample Notebooks.
 - Follow the instructions in the individual notebooks to explore various features in AutoML
 
-# Auto ML SDK Sample Notebooks <a name="samples"></a>
+<a name="samples"></a>
+# Automated ML SDK Sample Notebooks 
 - [00.configuration.ipynb](00.configuration.ipynb)
     - Register Machine Learning Services Resource Provider
     - Create new Azure ML Workspace
@@ -87,7 +115,7 @@ automl_setup_linux.sh
 
 - [03b.auto-ml-remote-batchai.ipynb](03b.auto-ml-remote-batchai.ipynb)
     - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
-    - Example of using Auto ML for classification using a remote Batch AI compute for training
+    - Example of using automated ML for classification using a remote Batch AI compute for training
     - Parallel execution of iterations
     - Async tracking of progress
     - Cancelling individual iterations or entire run
@@ -106,7 +134,7 @@ automl_setup_linux.sh
     - Specify a target metrics to indicate stopping criteria
     - Handling Missing Data in the input
 
-- [06.auto-ml-sparse-data-custom-cv-split.ipynb](06.auto-ml-sparse-data-custom-cv-split.ipynb)
+- [06.auto-ml-sparse-data-train-test-split.ipynb](06.auto-ml-sparse-data-train-test-split.ipynb)
     - Dataset: Scikit learn's [20newsgroup](http://scikit-learn.org/stable/datasets/twenty_newsgroups.html) 
     - Handle sparse datasets
     - Specify custom train and validation set
@@ -115,11 +143,11 @@ automl_setup_linux.sh
     - List all projects for the workspace
     - List all AutoML Runs for a given project
     - Get details for a AutoML Run. (Automl settings, run widget & all metrics)
-    - Downlaod fitted pipeline for any iteration
+    - Download fitted pipeline for any iteration
 
-- [08.auto-ml-remote-execution-with-text-file-on-DSVM](08.auto-ml-remote-execution-with-text-file-on-DSVM.ipynb)
+- [08.auto-ml-remote-execution-with-DataStore.ipynb](08.auto-ml-remote-execution-with-DataStore.ipynb)
     - Dataset: scikit learn's [digit dataset](https://innovate.burningman.org/datasets-page/)
-    - Download the data and store it in the DSVM to improve performance.
+    - Download the data and store it in DataStore.
 
 - [09.auto-ml-classification-with-deployment.ipynb](09.auto-ml-classification-with-deployment.ipynb)
     - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
@@ -143,34 +171,76 @@ automl_setup_linux.sh
 - [13.auto-ml-dataprep.ipynb](13.auto-ml-dataprep.ipynb)
     - Using DataPrep for reading data
 
-- [14a.auto-ml-classification-ensemble.ipynb](14a.auto-ml-classification-ensemble.ipynb)
-    - Classification with ensembling
+- [14.auto-ml-model-explanation.ipynb](14.auto-ml-model-explanation.ipynb)
+    - Dataset: sklearn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html)
+    - Explaining the AutoML classification pipeline
+    - Visualizing feature importance in widget
+    
+- [15a.auto-ml-classification-ensemble.ipynb](15a.auto-ml-classification-ensemble.ipynb)
+    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
+    - Enables an extra iteration for generating an Ensemble of models
+    - Uses local compute for training
 
-- [14b.auto-ml-regression-ensemble.ipynb](14b.auto-ml-regression-ensemble.ipynb)
-    - Regression with ensembling
+- [15b.auto-ml-regression-ensemble.ipynb](15b.auto-ml-regression-ensemble.ipynb)
+    - Dataset: scikit learn's [diabetes dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html)
+    - Enables an extra iteration for generating an Ensemble of models
+    - Uses remote Linux DSVM for training
 
-# Documentation <a name="documentation"></a>
+<a name="documentation"></a>
+# Documentation 
 ## Table of Contents
-1. [Auto ML Settings ](#automlsettings)
-2. [Cross validation split options](#cvsplits)
-3. [Get Data Syntax](#getdata)
-4. [Data pre-processing and featurization](#preprocessing)
+1. [Automated ML Settings ](#automlsettings)
+1. [Cross validation split options](#cvsplits)
+1. [Get Data Syntax](#getdata)
+1. [Data pre-processing and featurization](#preprocessing)
+
+<a name="automlsettings"></a>
+## Automated ML Settings 
 
-## Auto ML Settings <a name="automlsettings"></a>
 |Property|Description|Default|
 |-|-|-|
 |**primary_metric**|This is the metric that you want to optimize.<br><br> Classification supports the following primary metrics <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i><br><br> Regression supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i><br><i>normalized_root_mean_squared_log_error</i>| Classification: accuracy <br><br> Regression: spearman_correlation
-|**max_time_sec**|Time limit in seconds for each iteration|None|
+|**iteration_timeout_minutes**|Time limit in minutes for each iteration|None|
 |**iterations**|Number of iterations. In each iteration trains the data with a specific pipeline.  To get the best result, use at least 100. |100|
 |**n_cross_validations**|Number of cross validation splits|None|
 |**validation_size**|Size of validation set as percentage of all training samples|None|
-|**concurrent_iterations**|Max number of iterations that would be executed in parallel|1|
+|**max_concurrent_iterations**|Max number of iterations that would be executed in parallel|1|
 |**preprocess**|*True/False* <br>Setting this to *True* enables preprocessing <br>on the input to handle missing data, and perform some common feature extraction<br>*Note: If input data is Sparse you cannot use preprocess=True*|False|
 |**max_cores_per_iteration**| Indicates how many cores on the compute target would be used to train a single pipeline.<br> You can set it to *-1* to use all cores|1|
-|**exit_score**|*double* value indicating the target for *primary_metric*. <br> Once the target is surpassed the run terminates|None|
-|**blacklist_algos**|*Array* of *strings* indicating pipelines to ignore for Auto ML.<br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGDClassifierWrapper</i><br><i>NBWrapper</i><br><i>BernoulliNB</i><br><i>SVCWrapper</i><br><i>LinearSVMWrapper</i><br><i>KNeighborsClassifier</i><br><i>DecisionTreeClassifier</i><br><i>RandomForestClassifier</i><br><i>ExtraTreesClassifier</i><br><i>gradient boosting</i><br><i>LightGBMClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoostingRegressor</i><br><i>DecisionTreeRegressor</i><br><i>KNeighborsRegressor</i><br><i>LassoLars</i><br><i>SGDRegressor</i><br><i>RandomForestRegressor</i><br><i>ExtraTreesRegressor</i>|None|
+|**experiment_exit_score**|*double* value indicating the target for *primary_metric*. <br> Once the target is surpassed the run terminates|None|
+|**blacklist_models**|*Array* of *strings* indicating models to ignore for Auto ML from the list of models.|None|
+|**whilelist_models**|*Array* of *strings* use only models listed for Auto ML from the list of models..|None|
+ <a name="cvsplits"></a>
+## List of models for white list/blacklist
+**Classification**
+<br><i>LogisticRegression</i>
+<br><i>SGD</i>
+<br><i>MultinomialNaiveBayes</i>
+<br><i>BernoulliNaiveBayes</i>
+<br><i>SVM</i>
+<br><i>LinearSVM</i>
+<br><i>KNN</i>
+<br><i>DecisionTree</i>
+<br><i>RandomForest</i>
+<br><i>ExtremeRandomTrees</i>
+<br><i>LightGBM</i>
+<br><i>GradientBoosting</i>
+<br><i>TensorFlowDNN</i>
+<br><i>TensorFlowLinearClassifier</i>
+<br><br>**Regression**
+<br><i>ElasticNet</i>
+<br><i>GradientBoosting</i>
+<br><i>DecisionTree</i>
+<br><i>KNN</i>
+<br><i>LassoLars</i>
+<br><i>SGD</i>
+<br><i>RandomForest</i>
+<br><i>ExtremeRandomTrees</i>
+<br><i>LightGBM</i>
+<br><i>TensorFlowLinearRegressor</i>
+<br><i>TensorFlowDNN</i>
 
-## Cross validation split options <a name="cvsplits"></a>
+## Cross validation split options
 ### K-Folds Cross Validation
 Use *n_cross_validations* setting to specify the number of cross validations. The training data set will be randomly split into *n_cross_validations* folds of equal size. During each cross validation round, one of the folds will be used for validation of the model trained on the remaining folds. This process repeats for *n_cross_validations* rounds until each fold is used once as validation set. Finally, the average scores accross all *n_cross_validations* rounds will be reported, and the corresponding model will be retrained on the whole training data set.
 
@@ -180,7 +250,8 @@ Use *validation_size* to specify the percentage of the training data set that sh
 ### Custom train and validation set
 You can specify seperate train and validation set either through the get_data() or directly to the fit method.
 
-## get_data() syntax <a name="getdata"></a>
+<a name="getdata"></a>
+## get_data() syntax 
 The *get_data()* function can be used to return a dictionary with these values:
 
 |Key|Type|Dependency|Mutually Exclusive with|Description|
@@ -196,21 +267,23 @@ The *get_data()* function can be used to return a dictionary with these values:
 |columns|Array of strings|data_train||*Optional* Whitelist of columns to use for features|
 |cv_splits_indices|Array of integers|data_train||*Optional* List of indexes to split the data for cross validation|
 
-## Data pre-processing and featurization <a name="preprocessing"></a>
-If you use "preprocess=True", the following data preprocessing steps are performed automatically for you:
-### 1. Dropping high cardinality or no variance features
-- Features with no useful information are dropped from training and validation sets. These include features with all values missing, same value across all rows or with extremely high cardinality (e.g., hashes, IDs or GUIDs).
-### 2. Missing value imputation
-- For numerical features, missing values are imputed with average of values in the column.
-- For categorical features, missing values are imputed with most frequent value.
-### 3. Generating additional features
-- For DateTime features: Year, Month, Day, Day of week, Day of year, Quarter, Week of the year, Hour, Minute, Second.
-- For Text features: Term frequency based on bi-grams and tri-grams, Count vectorizer.
-### 4. Transformations and encodings
-- Numeric features with very few unique values are transformed into categorical features.
-- Depending on cardinality of categorical features label encoding or (hashing) one-hot encoding is performed.
+<a name="preprocessing"></a>
+## Data pre-processing and featurization 
+If you use `preprocess=True`, the following data preprocessing steps are performed automatically for you:
 
-# Running using python command <a name="pythoncommand"></a>
+1. Dropping high cardinality or no variance features
+    - Features with no useful information are dropped from training and validation sets. These include features with all values missing, same value across all rows or with extremely high cardinality (e.g., hashes, IDs or GUIDs).
+2. Missing value imputation
+    - For numerical features, missing values are imputed with average of values in the column.
+    - For categorical features, missing values are imputed with most frequent value.
+3. Generating additional features
+    - For DateTime features: Year, Month, Day, Day of week, Day of year, Quarter, Week of the year, Hour, Minute, Second.
+    - For Text features: Term frequency based on bi-grams and tri-grams, Count vectorizer.
+4. Transformations and encodings
+    - Numeric features with very few unique values are transformed into categorical features.
+
+<a name="pythoncommand"></a>
+# Running using python command 
 Jupyter notebook provides a File / Download as / Python (.py) option for saving the notebook as a Python file.
 You can then run this file using the python command.
 However, on Windows the file needs to be modified before it can be run.
@@ -220,13 +293,14 @@ The following condition must be added to the main code in the file:
 
 The main code of the file must be indented so that it is under this condition.
 
-# Troubleshooting <a name="troubleshooting"></a>
+<a name="troubleshooting"></a>
+# Troubleshooting 
 ## Iterations fail and the log contains "MemoryError"
 This can be caused by insufficient memory on the DSVM.  AutoML loads all training data into memory.  So, the available memory should be more than the training data size.
-If you are using a remote DSVM, memory is needed for each concurrent iteration.  The concurrent_iterations setting specifies the maximum concurrent iterations.  For example, if the training data size is 8Gb and concurrent_iterations is set to 10, the minimum memory required is at least 80Gb.
-To resolve this issue, allocate a DSVM with more memory or reduce the value specified for concurrent_iterations.
+If you are using a remote DSVM, memory is needed for each concurrent iteration.  The max_concurrent_iterations setting specifies the maximum concurrent iterations.  For example, if the training data size is 8Gb and max_concurrent_iterations is set to 10, the minimum memory required is at least 80Gb.
+To resolve this issue, allocate a DSVM with more memory or reduce the value specified for max_concurrent_iterations.
 
 ## Iterations show as "Not Responding" in the RunDetails widget.
-This can be caused by too many concurrent iterations for a remote DSVM.  Each concurrent iteration usually takes 100% of a core when it is running.  Some iterations can use multiple cores.  So, the concurrent_iterations setting should always be less than the number of cores of the DSVM.
-To resolve this issue, try reducing the value specified for the concurrent_iterations setting.
+This can be caused by too many concurrent iterations for a remote DSVM.  Each concurrent iteration usually takes 100% of a core when it is running.  Some iterations can use multiple cores.  So, the max_concurrent_iterations setting should always be less than the number of cores of the DSVM.
+To resolve this issue, try reducing the value specified for the max_concurrent_iterations setting.
 

automl/automl_env.yml

@@ -4,16 +4,28 @@ dependencies:
   # Currently Azure ML only supports 3.5.2 and later.
 - python=3.6
 - nb_conda
-- matplotlib
-- numpy>=1.11.0,<1.16.0
+- matplotlib==2.1.0
+- numpy>=1.11.0,<1.15.0
+- cython
+- urllib3<1.24
 - scipy>=0.19.0,<0.20.0
 - scikit-learn>=0.18.0,<=0.19.1
 - pandas>=0.22.0,<0.23.0
 
+# Required for azuremlftk
+- dill
+- pyodbc
+- statsmodels 
+- numexpr  
+- keras
+- distributed>=1.21.5,<1.24
+  
 - pip:
-    # Required packages for AzureML execution, history, and data preparation.
-  - --extra-index-url https://pypi.python.org/simple
-  - azureml-sdk[automl]
-  - azureml-train-widgets
+
+  # Required for azuremlftk
+  - https://azuremlpackages.blob.core.windows.net/forecasting/azuremlftk-0.1.18313.5a1-py3-none-any.whl
+
+  # Required packages for AzureML execution, history, and data preparation.
+  - azureml-sdk[automl,notebooks]
   - pandas_ml
   

automl/automl_env_mac.yml

@@ -0,0 +1,31 @@
+name: azure_automl
+dependencies:
+  # The python interpreter version.
+  # Currently Azure ML only supports 3.5.2 and later.
+- python=3.6
+- nb_conda
+- matplotlib==2.1.0
+- numpy>=1.15.3
+- cython
+- urllib3<1.24
+- scipy>=0.19.0,<0.20.0
+- scikit-learn>=0.18.0,<=0.19.1
+- pandas>=0.22.0,<0.23.0
+
+# Required for azuremlftk
+- dill
+- pyodbc
+- statsmodels 
+- numexpr  
+- keras
+- distributed>=1.21.5,<1.24
+  
+- pip:
+
+  # Required for azuremlftk
+  - https://azuremlpackages.blob.core.windows.net/forecasting/azuremlftk-0.1.18313.5a1-py3-none-any.whl
+    # Required packages for AzureML execution, history, and data preparation.
+  - azureml-sdk[automl,notebooks]
+  - pandas_ml
+  
+

automl/automl_setup.cmd

@@ -1,16 +1,21 @@
 @echo off
 set conda_env_name=%1
+set automl_env_file=%2
+set PIP_NO_WARN_SCRIPT_LOCATION=0
 
 IF "%conda_env_name%"=="" SET conda_env_name="azure_automl"
+IF "%automl_env_file%"=="" SET automl_env_file="automl_env.yml"
+
+IF NOT EXIST %automl_env_file% GOTO YmlMissing
 
 call conda activate %conda_env_name% 2>nul:
 
 if not errorlevel 1 (
   echo Upgrading azureml-sdk[automl] in existing conda environment %conda_env_name%
-  call pip install --upgrade azureml-sdk[automl]
+  call pip install --upgrade azureml-sdk[automl,notebooks]
   if errorlevel 1 goto ErrorExit
 ) else (
-  call conda env create -f automl_env.yml -n %conda_env_name%
+  call conda env create -f %automl_env_file% -n %conda_env_name%
 )
 
 call conda activate %conda_env_name% 2>nul:
@@ -18,10 +23,12 @@ if errorlevel 1 goto ErrorExit
 
 call pip install psutil
 
-call jupyter nbextension install --py azureml.train.widgets
+call python -m ipykernel install --user --name %conda_env_name% --display-name "Python (%conda_env_name%)"
+
+call jupyter nbextension install --py azureml.widgets --user
 if errorlevel 1 goto ErrorExit
 
-call jupyter nbextension enable --py azureml.train.widgets
+call jupyter nbextension enable --py azureml.widgets --user
 if errorlevel 1 goto ErrorExit
 
 echo.
@@ -36,6 +43,9 @@ jupyter notebook --log-level=50
 
 goto End
 
+:YmlMissing
+echo File %automl_env_file% not found.
+
 :ErrorExit
 echo Install failed
 

automl/automl_setup_linux.sh

@@ -1,21 +1,34 @@
 #!/bin/bash
 
 CONDA_ENV_NAME=$1
+AUTOML_ENV_FILE=$2
+PIP_NO_WARN_SCRIPT_LOCATION=0
 
 if [ "$CONDA_ENV_NAME" == "" ]
 then
   CONDA_ENV_NAME="azure_automl"
 fi
 
+if [ "$AUTOML_ENV_FILE" == "" ]
+then
+  AUTOML_ENV_FILE="automl_env.yml"
+fi
+
+if [ ! -f $AUTOML_ENV_FILE ]; then
+    echo "File $AUTOML_ENV_FILE not found"
+    exit 1
+fi
+
 if source activate $CONDA_ENV_NAME 2> /dev/null
 then
    echo "Upgrading azureml-sdk[automl] in existing conda environment" $CONDA_ENV_NAME
-   pip install --upgrade azureml-sdk[automl]
+   pip install --upgrade azureml-sdk[automl,notebooks]
 else
-   conda env create -f automl_env.yml -n $CONDA_ENV_NAME &&
+   conda env create -f $AUTOML_ENV_FILE -n $CONDA_ENV_NAME &&
    source activate $CONDA_ENV_NAME &&
-   jupyter nbextension install --py azureml.train.widgets --user &&
-   jupyter nbextension enable --py azureml.train.widgets --user &&
+   python -m ipykernel install --user --name $CONDA_ENV_NAME --display-name "Python ($CONDA_ENV_NAME)" &&
+   jupyter nbextension install --py azureml.widgets --user &&
+   jupyter nbextension enable --py azureml.widgets --user &&
    echo "" &&
    echo "" &&
    echo "***************************************" &&

automl/automl_setup_mac.sh

@@ -1,22 +1,36 @@
 #!/bin/bash
 
 CONDA_ENV_NAME=$1
+AUTOML_ENV_FILE=$2
+PIP_NO_WARN_SCRIPT_LOCATION=0
 
 if [ "$CONDA_ENV_NAME" == "" ]
 then
   CONDA_ENV_NAME="azure_automl"
 fi
 
+if [ "$AUTOML_ENV_FILE" == "" ]
+then
+  AUTOML_ENV_FILE="automl_env_mac.yml"
+fi
+
+if [ ! -f $AUTOML_ENV_FILE ]; then
+    echo "File $AUTOML_ENV_FILE not found"
+    exit 1
+fi
+
 if source activate $CONDA_ENV_NAME 2> /dev/null
 then
    echo "Upgrading azureml-sdk[automl] in existing conda environment" $CONDA_ENV_NAME
-   pip install --upgrade azureml-sdk[automl]
+   pip install --upgrade azureml-sdk[automl,notebooks]
 else
-   conda env create -f automl_env.yml -n $CONDA_ENV_NAME &&
+   conda env create -f $AUTOML_ENV_FILE -n $CONDA_ENV_NAME &&
    source activate $CONDA_ENV_NAME &&
    conda install lightgbm -c conda-forge -y &&
-   jupyter nbextension install --py azureml.train.widgets --user &&
-   jupyter nbextension enable --py azureml.train.widgets --user &&
+   python -m ipykernel install --user --name $CONDA_ENV_NAME --display-name "Python ($CONDA_ENV_NAME)" &&
+   jupyter nbextension install --py azureml.widgets --user &&
+   jupyter nbextension enable --py azureml.widgets --user &&
+   pip install numpy==1.15.3
    echo "" &&
    echo "" &&
    echo "***************************************" &&
@@ -34,3 +48,4 @@ then
 fi
 
 
+

databricks/automl_adb_readme.md

@@ -0,0 +1,49 @@
+**PREVIEW capability** 
+
+Automated ML now supports Azure Databricks as a local compute to perform training (**public preview**). Azure Databricks is a managed Spark offering on Azure and customers already use it for advanced analytics. It provides a collaborative Notebook based environment with CPU or GPU based compute cluster. 
+- Customers who use Azure Databricks for advanced analytics can now use the same cluster to run automated machine learning experiments. 
+- You can keep the data within the same cluster. 
+- You can leverage the local worker nodes with autoscale and auto termination capabilities. 
+- You can use multiple cores of your Azure Databricks cluster to perform simultenous training. 
+- You can further tune the model generated by automated machine learning if you chose to. 
+- Every run (including the best run) is available as a pipeline. 
+- The model from the pipeline can be registered in Azure ML SDK workspace and then deployed to Azure managed compute (ACI or AKS) using the Azure Machine learning SDK.
+
+**Create Azure Databricks Cluster:**
+
+Select New Cluster and fill in following detail:
+ - Cluster name: _yourclustername_
+ - Cluster Mode: Any. **High Concurrency** preferred
+ - Databricks Runtime: Any 4.x runtime.
+ - Python version: **3**
+ - Workers: 2 or higher.  
+ - Max. number of **concurrent iterations** in Automated ML settings is **<=** to the number of **worker nodes** in your Databricks cluster.
+ - Worker node VM types: **Memory optimized VM** preferred. 
+ - Uncheck _Enable Autoscaling_
+
+
+It will take few minutes to create the cluster. Please ensure that the cluster state is running before proceeding further.
+
+**Install Azure ML with Automated ML SDK on your Azure Databricks cluster**
+
+- Select Import library
+
+- Source: Upload Python Egg or PyPI
+
+- PyPi Name (_with_ Automated ML capability): **azureml-sdk[automl_databricks]**
+
+- PyPi Name (_without_ Automated ML capability): **azureml-sdk[databricks]**
+
+- Click Install Library
+
+- Do not select _Attach automatically to all clusters_. In case you have selected earlier then you can go to your Home folder and deselect it.
+
+- Select the check box _Attach_ next to your cluster name
+
+(More details on how to attach and detach libs are here - [https://docs.databricks.com/user-guide/libraries.html#attach-a-library-to-a-cluster](https://docs.databricks.com/user-guide/libraries.html#attach-a-library-to-a-cluster) )
+
+- Ensure that there are no errors until Status changes to _Attached_. It may take a couple of minutes.
+
+**Note** - If you have the old build the please deselect it from cluster’s installed libs > move to trash. Install the new build and restart the cluster. And if still there is an issue then detach and reattach your cluster.
+
+**Now you can run the Automated ML sample notebook on your Azure Databricks cluster. Please let us know your feedback.**

onnx/README.md

@@ -1,14 +1,14 @@
 # ONNX on Azure Machine Learning
 
-These tutorials show how to create and deploy [ONNX](http://onnx.ai) models using Azure Machine Learning and the [ONNX Runtime](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-build-deploy-onnx). 
-Once deployed as web services, you can ping the models with your own images to be analyzed!
+These tutorials show how to create and deploy [ONNX](http://onnx.ai) models in Azure Machine Learning environments using [ONNX Runtime](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-build-deploy-onnx) for inference. Once deployed as a web service, you can ping the model with your own set of images to be analyzed!
 
 ## Tutorials
-- [Obtain ONNX model from ONNX Model Zoo and deploy - ResNet50](https://github.com/Azure/MachineLearningNotebooks/blob/master/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb)
+- [Obtain ONNX model from ONNX Model Zoo and deploy with ONNX Runtime inference - Handwritten Digit Classification (MNIST)](https://github.com/Azure/MachineLearningNotebooks/blob/master/onnx/onnx-inference-mnist-deploy.ipynb)
+- [Obtain ONNX model from ONNX Model Zoo and deploy with ONNX Runtime inference - Facial Expression Recognition (Emotion FER+)](https://github.com/Azure/MachineLearningNotebooks/blob/master/onnx/onnx-inference-facial-emotion-recognition-deploy.ipynb)
+- [Obtain ONNX model from ONNX Model Zoo and deploy with ONNX Runtime inference - Image Recognition (ResNet50)](https://github.com/Azure/MachineLearningNotebooks/blob/master/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb)
 - [Convert ONNX model from CoreML and deploy - TinyYOLO](https://github.com/Azure/MachineLearningNotebooks/blob/master/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb)
 - [Train ONNX model in PyTorch and deploy - MNIST](https://github.com/Azure/MachineLearningNotebooks/blob/master/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb)
-- [Handwritten Digit Classification (MNIST) using ONNX Runtime on AzureML](https://github.com/Azure/MachineLearningNotebooks/blob/master/onnx/onnx-inference-mnist.ipynb)
-- [Facial Expression Recognition using ONNX Runtime on AzureML](https://github.com/Azure/MachineLearningNotebooks/blob/master/onnx/onnx-inference-emotion-recognition.ipynb)
+
 
 ## Documentation
 - [ONNX Runtime Python API Documentation](http://aka.ms/onnxruntime-python)
@@ -21,7 +21,8 @@ Once deployed as web services, you can ping the models with your own images to b
 
 
 ## License
-
 Copyright (c) Microsoft Corporation. All rights reserved.  
 Licensed under the MIT License.
 
+## Acknowledgements
+These tutorials were developed by Vinitra Swamy and Prasanth Pulavarthi of the Microsoft AI Frameworks team and adapted for presentation at Microsoft Ignite 2018.

onnx/onnx-convert-aml-deploy-tinyyolo.ipynb

@@ -1,431 +1,435 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# YOLO Real-time Object Detection using ONNX on AzureML\n",
-        "\n",
-        "This example shows how to convert the TinyYOLO model from CoreML to ONNX and operationalize it as a web service using Azure Machine Learning services and the ONNX Runtime.\n",
-        "\n",
-        "## What is ONNX\n",
-        "ONNX is an open format for representing machine learning and deep learning models. ONNX enables open and interoperable AI by enabling data scientists and developers to use the tools of their choice without worrying about lock-in and flexibility to deploy to a variety of platforms. ONNX is developed and supported by a community of partners including Microsoft, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai).\n",
-        "\n",
-        "## YOLO Details\n",
-        "You Only Look Once (YOLO) is a state-of-the-art, real-time object detection system. For more information about YOLO, please visit the [YOLO website](https://pjreddie.com/darknet/yolo/)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "\n",
-        "To make the best use of your time, make sure you have done the following:\n",
-        "\n",
-        "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
-        "* Go through the [00.configuration.ipynb](../00.configuration.ipynb) notebook to:\n",
-        "    * install the AML SDK\n",
-        "    * create a workspace and its configuration file (config.json)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Check core SDK version number\n",
-        "import azureml.core\n",
-        "\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Install necessary packages\n",
-        "\n",
-        "You'll need to run the following commands to use this tutorial:\n",
-        "\n",
-        "```sh\n",
-        "pip install coremltools\n",
-        "pip install onnxmltools\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Convert model to ONNX\n",
-        "\n",
-        "First we download the CoreML model. We use the CoreML model listed at https://coreml.store/tinyyolo. This may take a few minutes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "!wget https://s3-us-west-2.amazonaws.com/coreml-models/TinyYOLO.mlmodel"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Then we use ONNXMLTools to convert the model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import onnxmltools\n",
-        "import coremltools\n",
-        "\n",
-        "# Load a CoreML model\n",
-        "coreml_model = coremltools.utils.load_spec('TinyYOLO.mlmodel')\n",
-        "\n",
-        "# Convert from CoreML into ONNX\n",
-        "onnx_model = onnxmltools.convert_coreml(coreml_model, 'TinyYOLOv2')\n",
-        "\n",
-        "# Save ONNX model\n",
-        "onnxmltools.utils.save_model(onnx_model, 'tinyyolov2.onnx')\n",
-        "\n",
-        "import os\n",
-        "print(os.path.getsize('tinyyolov2.onnx'))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Deploying as a web service with Azure ML\n",
-        "\n",
-        "### Load Azure ML workspace\n",
-        "\n",
-        "We begin by instantiating a workspace object from the existing workspace created earlier in the configuration notebook."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.location, ws.resource_group, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Registering your model with Azure ML\n",
-        "\n",
-        "Now we upload the model and register it in the workspace."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.model import Model\n",
-        "\n",
-        "model = Model.register(model_path = \"tinyyolov2.onnx\",\n",
-        "                       model_name = \"tinyyolov2\",\n",
-        "                       tags = {\"onnx\": \"demo\"},\n",
-        "                       description = \"TinyYOLO\",\n",
-        "                       workspace = ws)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Displaying your registered models\n",
-        "\n",
-        "You can optionally list out all the models that you have registered in this workspace."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "models = ws.models()\n",
-        "for m in models:\n",
-        "    print(\"Name:\", m.name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Write scoring file\n",
-        "\n",
-        "We are now going to deploy our ONNX model on Azure ML using the ONNX Runtime. We begin by writing a score.py file that will be invoked by the web service call. The `init()` function is called once when the container is started so we load the model using the ONNX Runtime into a global session object."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile score.py\n",
-        "import json\n",
-        "import time\n",
-        "import sys\n",
-        "import os\n",
-        "from azureml.core.model import Model\n",
-        "import numpy as np    # we're going to use numpy to process input and output data\n",
-        "import onnxruntime    # to inference ONNX models, we use the ONNX Runtime\n",
-        "\n",
-        "def init():\n",
-        "    global session\n",
-        "    model = Model.get_model_path(model_name = 'tinyyolov2')\n",
-        "    session = onnxruntime.InferenceSession(model)\n",
-        "\n",
-        "def preprocess(input_data_json):\n",
-        "    # convert the JSON data into the tensor input\n",
-        "    return np.array(json.loads(input_data_json)['data']).astype('float32')\n",
-        "\n",
-        "def postprocess(result):\n",
-        "    return np.array(result).tolist()\n",
-        "\n",
-        "def run(input_data_json):\n",
-        "    try:\n",
-        "        start = time.time()   # start timer\n",
-        "        input_data = preprocess(input_data_json)\n",
-        "        input_name = session.get_inputs()[0].name  # get the id of the first input of the model   \n",
-        "        result = session.run([], {input_name: input_data})\n",
-        "        end = time.time()     # stop timer\n",
-        "        return {\"result\": postprocess(result),\n",
-        "                \"time\": end - start}\n",
-        "    except Exception as e:\n",
-        "        result = str(e)\n",
-        "        return {\"error\": result}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create container image\n",
-        "First we create a YAML file that specifies which dependencies we would like to see in our container."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies \n",
-        "\n",
-        "myenv = CondaDependencies.create(pip_packages=[\"numpy\",\"onnxruntime\"])\n",
-        "\n",
-        "with open(\"myenv.yml\",\"w\") as f:\n",
-        "    f.write(myenv.serialize_to_string())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Then we have Azure ML create the container. This step will likely take a few minutes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.image import ContainerImage\n",
-        "\n",
-        "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
-        "                                                  runtime = \"python\",\n",
-        "                                                  conda_file = \"myenv.yml\",\n",
-        "                                                  description = \"TinyYOLO ONNX Demo\",\n",
-        "                                                  tags = {\"demo\": \"onnx\"}\n",
-        "                                                 )\n",
-        "\n",
-        "\n",
-        "image = ContainerImage.create(name = \"onnxyolo\",\n",
-        "                              models = [model],\n",
-        "                              image_config = image_config,\n",
-        "                              workspace = ws)\n",
-        "\n",
-        "image.wait_for_creation(show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In case you need to debug your code, the next line of code accesses the log file."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(image.image_build_log_uri)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We're all set! Let's get our model chugging.\n",
-        "\n",
-        "### Deploy the container image"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import AciWebservice\n",
-        "\n",
-        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
-        "                                               memory_gb = 1, \n",
-        "                                               tags = {'demo': 'onnx'}, \n",
-        "                                               description = 'web service for TinyYOLO ONNX model')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The following cell will likely take a few minutes to run as well."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import Webservice\n",
-        "from random import randint\n",
-        "\n",
-        "aci_service_name = 'onnx-tinyyolo'+str(randint(0,100))\n",
-        "print(\"Service\", aci_service_name)\n",
-        "\n",
-        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
-        "                                           image = image,\n",
-        "                                           name = aci_service_name,\n",
-        "                                           workspace = ws)\n",
-        "\n",
-        "aci_service.wait_for_deployment(True)\n",
-        "print(aci_service.state)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In case the deployment fails, you can check the logs. Make sure to delete your aci_service before trying again."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "if aci_service.state != 'Healthy':\n",
-        "    # run this command for debugging.\n",
-        "    print(aci_service.get_logs())\n",
-        "    aci_service.delete()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Success!\n",
-        "\n",
-        "If you've made it this far, you've deployed a working web service that does object detection using an ONNX model. You can get the URL for the webservice with the code below."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(aci_service.scoring_uri)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "When you are eventually done using the web service, remember to delete it."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#aci_service.delete()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "onnx"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.5.6"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# YOLO Real-time Object Detection using ONNX on AzureML\n",
+    "\n",
+    "This example shows how to convert the TinyYOLO model from CoreML to ONNX and operationalize it as a web service using Azure Machine Learning services and the ONNX Runtime.\n",
+    "\n",
+    "## What is ONNX\n",
+    "ONNX is an open format for representing machine learning and deep learning models. ONNX enables open and interoperable AI by enabling data scientists and developers to use the tools of their choice without worrying about lock-in and flexibility to deploy to a variety of platforms. ONNX is developed and supported by a community of partners including Microsoft, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai).\n",
+    "\n",
+    "## YOLO Details\n",
+    "You Only Look Once (YOLO) is a state-of-the-art, real-time object detection system. For more information about YOLO, please visit the [YOLO website](https://pjreddie.com/darknet/yolo/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "\n",
+    "To make the best use of your time, make sure you have done the following:\n",
+    "\n",
+    "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
+    "* Go through the [00.configuration.ipynb](../00.configuration.ipynb) notebook to:\n",
+    "    * install the AML SDK\n",
+    "    * create a workspace and its configuration file (config.json)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check core SDK version number\n",
+    "import azureml.core\n",
+    "\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Install necessary packages\n",
+    "\n",
+    "You'll need to run the following commands to use this tutorial:\n",
+    "\n",
+    "```sh\n",
+    "pip install onnxmltools\n",
+    "pip install coremltools  # use this on Linux and Mac\n",
+    "pip install git+https://github.com/apple/coremltools  # use this on Windows\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Convert model to ONNX\n",
+    "\n",
+    "First we download the CoreML model. We use the CoreML model listed at https://coreml.store/tinyyolo. This may take a few minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import urllib.request\n",
+    "\n",
+    "onnx_model_url = \"https://s3-us-west-2.amazonaws.com/coreml-models/TinyYOLO.mlmodel\"\n",
+    "urllib.request.urlretrieve(onnx_model_url, filename=\"TinyYOLO.mlmodel\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we use ONNXMLTools to convert the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import onnxmltools\n",
+    "import coremltools\n",
+    "\n",
+    "# Load a CoreML model\n",
+    "coreml_model = coremltools.utils.load_spec('TinyYOLO.mlmodel')\n",
+    "\n",
+    "# Convert from CoreML into ONNX\n",
+    "onnx_model = onnxmltools.convert_coreml(coreml_model, 'TinyYOLOv2')\n",
+    "\n",
+    "# Save ONNX model\n",
+    "onnxmltools.utils.save_model(onnx_model, 'tinyyolov2.onnx')\n",
+    "\n",
+    "import os\n",
+    "print(os.path.getsize('tinyyolov2.onnx'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deploying as a web service with Azure ML\n",
+    "\n",
+    "### Load Azure ML workspace\n",
+    "\n",
+    "We begin by instantiating a workspace object from the existing workspace created earlier in the configuration notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.location, ws.resource_group, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Registering your model with Azure ML\n",
+    "\n",
+    "Now we upload the model and register it in the workspace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.model import Model\n",
+    "\n",
+    "model = Model.register(model_path = \"tinyyolov2.onnx\",\n",
+    "                       model_name = \"tinyyolov2\",\n",
+    "                       tags = {\"onnx\": \"demo\"},\n",
+    "                       description = \"TinyYOLO\",\n",
+    "                       workspace = ws)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Displaying your registered models\n",
+    "\n",
+    "You can optionally list out all the models that you have registered in this workspace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models = ws.models\n",
+    "for name, m in models.items():\n",
+    "    print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Write scoring file\n",
+    "\n",
+    "We are now going to deploy our ONNX model on Azure ML using the ONNX Runtime. We begin by writing a score.py file that will be invoked by the web service call. The `init()` function is called once when the container is started so we load the model using the ONNX Runtime into a global session object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile score.py\n",
+    "import json\n",
+    "import time\n",
+    "import sys\n",
+    "import os\n",
+    "from azureml.core.model import Model\n",
+    "import numpy as np    # we're going to use numpy to process input and output data\n",
+    "import onnxruntime    # to inference ONNX models, we use the ONNX Runtime\n",
+    "\n",
+    "def init():\n",
+    "    global session\n",
+    "    model = Model.get_model_path(model_name = 'tinyyolov2')\n",
+    "    session = onnxruntime.InferenceSession(model)\n",
+    "\n",
+    "def preprocess(input_data_json):\n",
+    "    # convert the JSON data into the tensor input\n",
+    "    return np.array(json.loads(input_data_json)['data']).astype('float32')\n",
+    "\n",
+    "def postprocess(result):\n",
+    "    return np.array(result).tolist()\n",
+    "\n",
+    "def run(input_data_json):\n",
+    "    try:\n",
+    "        start = time.time()   # start timer\n",
+    "        input_data = preprocess(input_data_json)\n",
+    "        input_name = session.get_inputs()[0].name  # get the id of the first input of the model   \n",
+    "        result = session.run([], {input_name: input_data})\n",
+    "        end = time.time()     # stop timer\n",
+    "        return {\"result\": postprocess(result),\n",
+    "                \"time\": end - start}\n",
+    "    except Exception as e:\n",
+    "        result = str(e)\n",
+    "        return {\"error\": result}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create container image\n",
+    "First we create a YAML file that specifies which dependencies we would like to see in our container."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.conda_dependencies import CondaDependencies \n",
+    "\n",
+    "myenv = CondaDependencies.create(pip_packages=[\"numpy\",\"onnxruntime\",\"azureml-core\"])\n",
+    "\n",
+    "with open(\"myenv.yml\",\"w\") as f:\n",
+    "    f.write(myenv.serialize_to_string())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we have Azure ML create the container. This step will likely take a few minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.image import ContainerImage\n",
+    "\n",
+    "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
+    "                                                  runtime = \"python\",\n",
+    "                                                  conda_file = \"myenv.yml\",\n",
+    "                                                  description = \"TinyYOLO ONNX Demo\",\n",
+    "                                                  tags = {\"demo\": \"onnx\"}\n",
+    "                                                 )\n",
+    "\n",
+    "\n",
+    "image = ContainerImage.create(name = \"onnxyolo\",\n",
+    "                              models = [model],\n",
+    "                              image_config = image_config,\n",
+    "                              workspace = ws)\n",
+    "\n",
+    "image.wait_for_creation(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In case you need to debug your code, the next line of code accesses the log file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(image.image_build_log_uri)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We're all set! Let's get our model chugging.\n",
+    "\n",
+    "### Deploy the container image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.webservice import AciWebservice\n",
+    "\n",
+    "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
+    "                                               memory_gb = 1, \n",
+    "                                               tags = {'demo': 'onnx'}, \n",
+    "                                               description = 'web service for TinyYOLO ONNX model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following cell will likely take a few minutes to run as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.webservice import Webservice\n",
+    "from random import randint\n",
+    "\n",
+    "aci_service_name = 'onnx-tinyyolo'+str(randint(0,100))\n",
+    "print(\"Service\", aci_service_name)\n",
+    "\n",
+    "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
+    "                                           image = image,\n",
+    "                                           name = aci_service_name,\n",
+    "                                           workspace = ws)\n",
+    "\n",
+    "aci_service.wait_for_deployment(True)\n",
+    "print(aci_service.state)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In case the deployment fails, you can check the logs. Make sure to delete your aci_service before trying again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if aci_service.state != 'Healthy':\n",
+    "    # run this command for debugging.\n",
+    "    print(aci_service.get_logs())\n",
+    "    aci_service.delete()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Success!\n",
+    "\n",
+    "If you've made it this far, you've deployed a working web service that does object detection using an ONNX model. You can get the URL for the webservice with the code below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(aci_service.scoring_uri)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you are eventually done using the web service, remember to delete it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#aci_service.delete()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "onnx"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

onnx/onnx-inference-emotion-recognition.ipynb

@@ -1,812 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Facial Expression Recognition (Emotion FER+) using ONNX Runtime on Azure ML\n",
-        "\n",
-        "This example shows how to deploy an image classification neural network using the Facial Expression Recognition ([FER](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data)) dataset and Open Neural Network eXchange format ([ONNX](http://aka.ms/onnxdocarticle)) on the Azure Machine Learning platform. This tutorial will show you how to deploy a FER+ model from the [ONNX model zoo](https://github.com/onnx/models), use it to make predictions using ONNX Runtime Inference, and deploy it as a web service in Azure.\n",
-        "\n",
-        "Throughout this tutorial, we will be referring to ONNX, a neural network exchange format used to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools (CNTK, PyTorch, Caffe, MXNet, TensorFlow) and choose the combination that is best for them. ONNX is developed and supported by a community of partners including Microsoft AI, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai) and [open source files](https://github.com/onnx).\n",
-        "\n",
-        "[ONNX Runtime](https://aka.ms/onnxruntime-python) is the runtime engine that enables evaluation of trained machine learning (Traditional ML and Deep Learning) models with high performance and low resource utilization. We use the CPU version of ONNX Runtime in this tutorial, but will soon be releasing an additional tutorial for deploying this model using ONNX Runtime GPU.\n",
-        "\n",
-        "#### Tutorial Objectives:\n",
-        "\n",
-        "1. Describe the FER+ dataset and pretrained Convolutional Neural Net ONNX model for Emotion Recognition, stored in the ONNX model zoo.\n",
-        "2. Deploy and run the pretrained FER+ ONNX model on an Azure Machine Learning instance\n",
-        "3. Predict labels for test set data points in the cloud using ONNX Runtime and Azure ML"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "\n",
-        "### 1. Install Azure ML SDK and create a new workspace\n",
-        "Please follow [Azure ML configuration notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) to set up your environment.\n",
-        "\n",
-        "### 2. Install additional packages needed for this Notebook\n",
-        "You need to install the popular plotting library `matplotlib`, the image manipulation library `opencv`, and the `onnx` library in the conda environment where Azure Maching Learning SDK is installed.\n",
-        "\n",
-        "```sh\n",
-        "(myenv) $ pip install matplotlib onnx opencv-python\n",
-        "```\n",
-        "\n",
-        "**Debugging tip**: Make sure that to activate your virtual environment (myenv) before you re-launch this notebook using the `jupyter notebook` comand. Choose the respective Python kernel for your new virtual environment using the `Kernel > Change Kernel` menu above. If you have completed the steps correctly, the upper right corner of your screen should state `Python [conda env:myenv]` instead of `Python [default]`.\n",
-        "\n",
-        "### 3. Download sample data and pre-trained ONNX model from ONNX Model Zoo.\n",
-        "\n",
-        "In the following lines of code, we download [the trained ONNX Emotion FER+ model and corresponding test data](https://github.com/onnx/models/tree/master/emotion_ferplus) and place them in the same folder as this tutorial notebook. For more information about the FER+ dataset, please visit Microsoft Researcher Emad Barsoum's [FER+ source data repository](https://github.com/ebarsoum/FERPlus)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# urllib is a built-in Python library to download files from URLs\n",
-        "\n",
-        "# Objective: retrieve the latest version of the ONNX Emotion FER+ model files from the\n",
-        "# ONNX Model Zoo and save it in the same folder as this tutorial\n",
-        "\n",
-        "import urllib.request\n",
-        "\n",
-        "onnx_model_url = \"https://www.cntk.ai/OnnxModels/emotion_ferplus/opset_7/emotion_ferplus.tar.gz\"\n",
-        "\n",
-        "urllib.request.urlretrieve(onnx_model_url, filename=\"emotion_ferplus.tar.gz\")\n",
-        "\n",
-        "# the ! magic command tells our jupyter notebook kernel to run the following line of \n",
-        "# code from the command line instead of the notebook kernel\n",
-        "\n",
-        "# We use tar and xvcf to unzip the files we just retrieved from the ONNX model zoo\n",
-        "\n",
-        "!tar xvzf emotion_ferplus.tar.gz"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Deploy a VM with your ONNX model in the Cloud\n",
-        "\n",
-        "### Load Azure ML workspace\n",
-        "\n",
-        "We begin by instantiating a workspace object from the existing workspace created earlier in the configuration notebook."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Check core SDK version number\n",
-        "import azureml.core\n",
-        "\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.location, ws.resource_group, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Registering your model with Azure ML"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model_dir = \"emotion_ferplus\" # replace this with the location of your model files\n",
-        "\n",
-        "# leave as is if it's in the same folder as this notebook"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.model import Model\n",
-        "\n",
-        "model = Model.register(model_path = model_dir + \"/\" + \"model.onnx\",\n",
-        "                       model_name = \"onnx_emotion\",\n",
-        "                       tags = {\"onnx\": \"demo\"},\n",
-        "                       description = \"FER+ emotion recognition CNN from ONNX Model Zoo\",\n",
-        "                       workspace = ws)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Optional: Displaying your registered models\n",
-        "\n",
-        "This step is not required, so feel free to skip it."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "models = ws.models()\n",
-        "for name, m in models.items():\n",
-        "    print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### ONNX FER+ Model Methodology\n",
-        "\n",
-        "The image classification model we are using is pre-trained using Microsoft's deep learning cognitive toolkit, [CNTK](https://github.com/Microsoft/CNTK), from the [ONNX model zoo](http://github.com/onnx/models). The model zoo has many other models that can be deployed on cloud providers like AzureML without any additional training. To ensure that our cloud deployed model works, we use testing data from the well-known FER+ data set, provided as part of the [trained Emotion Recognition model](https://github.com/onnx/models/tree/master/emotion_ferplus) in the ONNX model zoo.\n",
-        "\n",
-        "The original Facial Emotion Recognition (FER) Dataset was released in 2013 by Pierre-Luc Carrier and Aaron Courville as part of a [Kaggle Competition](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data), but some of the labels are not entirely appropriate for the expression. In the FER+ Dataset, each photo was evaluated by at least 10 croud sourced reviewers, creating a more accurate basis for ground truth. \n",
-        "\n",
-        "You can see the difference of label quality in the sample model input below. The FER labels are the first word below each image, and the FER+ labels are the second word below each image.\n",
-        "\n",
-        "![](https://raw.githubusercontent.com/Microsoft/FERPlus/master/FER+vsFER.png)\n",
-        "\n",
-        "***Input: Photos of cropped faces from FER+ Dataset***\n",
-        "\n",
-        "***Task: Classify each facial image into its appropriate emotions in the emotion table***\n",
-        "\n",
-        "```    emotion_table = {'neutral':0, 'happiness':1, 'surprise':2, 'sadness':3, 'anger':4, 'disgust':5, 'fear':6, 'contempt':7} ```\n",
-        "\n",
-        "***Output: Emotion prediction for input image***\n",
-        "\n",
-        "\n",
-        "Remember, once the application is deployed in Azure ML, you can use your own images as input for the model to classify."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# for images and plots in this notebook\n",
-        "import matplotlib.pyplot as plt  \n",
-        "from IPython.display import Image\n",
-        "\n",
-        "# display images inline\n",
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Model Description\n",
-        "\n",
-        "The FER+ model from the ONNX Model Zoo is summarized by the graphic below. You can see the entire workflow of our pre-trained model in the following image from Barsoum et. al's paper [\"Training Deep Networks for Facial Expression Recognition\n",
-        "with Crowd-Sourced Label Distribution\"](https://arxiv.org/pdf/1608.01041.pdf), with our (64 x 64) input images and our output probabilities for each of the labels."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![](https://raw.githubusercontent.com/vinitra/FERPlus/master/emotion_model_img.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Specify our Score and Environment Files"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We are now going to deploy our ONNX Model on AML with inference in ONNX Runtime. We begin by writing a score.py file, which will help us run the model in our Azure ML virtual machine (VM), and then specify our environment by writing a yml file. You will also notice that we import the onnxruntime library to do runtime inference on our ONNX models (passing in input and evaluating out model's predicted output). More information on the API and commands can be found in the [ONNX Runtime documentation](https://aka.ms/onnxruntime).\n",
-        "\n",
-        "### Write Score File\n",
-        "\n",
-        "A score file is what tells our Azure cloud service what to do. After initializing our model using azureml.core.model, we start an ONNX Runtime inference session to evaluate the data passed in on our function calls."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile score.py\n",
-        "import json\n",
-        "import numpy as np\n",
-        "import onnxruntime\n",
-        "import sys\n",
-        "import os\n",
-        "from azureml.core.model import Model\n",
-        "import time\n",
-        "\n",
-        "def init():\n",
-        "    global session, input_name, output_name\n",
-        "    model = Model.get_model_path(model_name = 'onnx_emotion')\n",
-        "    session = onnxruntime.InferenceSession(model, None)\n",
-        "    input_name = session.get_inputs()[0].name\n",
-        "    output_name = session.get_outputs()[0].name \n",
-        "    \n",
-        "def run(input_data):\n",
-        "    '''Purpose: evaluate test input in Azure Cloud using onnxruntime.\n",
-        "        We will call the run function later from our Jupyter Notebook \n",
-        "        so our azure service can evaluate our model input in the cloud. '''\n",
-        "\n",
-        "    try:\n",
-        "        # load in our data, convert to readable format\n",
-        "        data = np.array(json.loads(input_data)['data']).astype('float32')\n",
-        "        \n",
-        "        start = time.time()\n",
-        "        r = session.run([output_name], {input_name : data})\n",
-        "        end = time.time()\n",
-        "        \n",
-        "        result = emotion_map(postprocess(r[0]))\n",
-        "        \n",
-        "        result_dict = {\"result\": result,\n",
-        "                      \"time_in_sec\": [end - start]}\n",
-        "    except Exception as e:\n",
-        "        result_dict = {\"error\": str(e)}\n",
-        "    \n",
-        "    return json.dumps(result_dict)\n",
-        "\n",
-        "def emotion_map(classes, N=1):\n",
-        "    \"\"\"Take the most probable labels (output of postprocess) and returns the \n",
-        "    top N emotional labels that fit the picture.\"\"\"\n",
-        "    \n",
-        "    emotion_table = {'neutral':0, 'happiness':1, 'surprise':2, 'sadness':3, \n",
-        "                     'anger':4, 'disgust':5, 'fear':6, 'contempt':7}\n",
-        "    \n",
-        "    emotion_keys = list(emotion_table.keys())\n",
-        "    emotions = []\n",
-        "    for i in range(N):\n",
-        "        emotions.append(emotion_keys[classes[i]])\n",
-        "    return emotions\n",
-        "\n",
-        "def softmax(x):\n",
-        "    \"\"\"Compute softmax values (probabilities from 0 to 1) for each possible label.\"\"\"\n",
-        "    x = x.reshape(-1)\n",
-        "    e_x = np.exp(x - np.max(x))\n",
-        "    return e_x / e_x.sum(axis=0)\n",
-        "\n",
-        "def postprocess(scores):\n",
-        "    \"\"\"This function takes the scores generated by the network and \n",
-        "    returns the class IDs in decreasing order of probability.\"\"\"\n",
-        "    prob = softmax(scores)\n",
-        "    prob = np.squeeze(prob)\n",
-        "    classes = np.argsort(prob)[::-1]\n",
-        "    return classes"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Write Environment File"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies \n",
-        "\n",
-        "myenv = CondaDependencies()\n",
-        "myenv.add_pip_package(\"numpy\")\n",
-        "myenv.add_pip_package(\"azureml-core\")\n",
-        "myenv.add_pip_package(\"onnxruntime\")\n",
-        "\n",
-        "\n",
-        "with open(\"myenv.yml\",\"w\") as f:\n",
-        "    f.write(myenv.serialize_to_string())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create the Container Image\n",
-        "\n",
-        "This step will likely take a few minutes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.image import ContainerImage\n",
-        "\n",
-        "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
-        "                                                  runtime = \"python\",\n",
-        "                                                  conda_file = \"myenv.yml\",\n",
-        "                                                  description = \"Emotion ONNX Runtime container\",\n",
-        "                                                  tags = {\"demo\": \"onnx\"})\n",
-        "\n",
-        "\n",
-        "image = ContainerImage.create(name = \"onnxtest\",\n",
-        "                              # this is the model object\n",
-        "                              models = [model],\n",
-        "                              image_config = image_config,\n",
-        "                              workspace = ws)\n",
-        "\n",
-        "image.wait_for_creation(show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In case you need to debug your code, the next line of code accesses the log file."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(image.image_build_log_uri)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We're all done specifying what we want our virtual machine to do. Let's configure and deploy our container image.\n",
-        "\n",
-        "### Deploy the container image"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import AciWebservice\n",
-        "\n",
-        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
-        "                                               memory_gb = 1, \n",
-        "                                               tags = {'demo': 'onnx'}, \n",
-        "                                               description = 'ONNX for emotion recognition model')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import Webservice\n",
-        "\n",
-        "aci_service_name = 'onnx-demo-emotion'\n",
-        "print(\"Service\", aci_service_name)\n",
-        "\n",
-        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
-        "                                           image = image,\n",
-        "                                           name = aci_service_name,\n",
-        "                                           workspace = ws)\n",
-        "\n",
-        "aci_service.wait_for_deployment(True)\n",
-        "print(aci_service.state)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The following cell will likely take a few minutes to run as well."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "if aci_service.state != 'Healthy':\n",
-        "    # run this command for debugging.\n",
-        "    print(aci_service.get_logs())\n",
-        "\n",
-        "    # If your deployment fails, make sure to delete your aci_service before trying again!\n",
-        "    # aci_service.delete()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Success!\n",
-        "\n",
-        "If you've made it this far, you've deployed a working VM with a facial emotion recognition model running in the cloud using Azure ML. Congratulations!\n",
-        "\n",
-        "Let's see how well our model deals with our test images."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Testing and Evaluation\n",
-        "\n",
-        "### Useful Helper Functions\n",
-        "\n",
-        "We preprocess and postprocess our data (see score.py file) using the helper functions specified in the [ONNX FER+ Model page in the Model Zoo repository](https://github.com/onnx/models/tree/master/emotion_ferplus)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "def emotion_map(classes, N=1):\n",
-        "    \"\"\"Take the most probable labels (output of postprocess) and returns the \n",
-        "    top N emotional labels that fit the picture.\"\"\"\n",
-        "    \n",
-        "    emotion_table = {'neutral':0, 'happiness':1, 'surprise':2, 'sadness':3, \n",
-        "                     'anger':4, 'disgust':5, 'fear':6, 'contempt':7}\n",
-        "    \n",
-        "    emotion_keys = list(emotion_table.keys())\n",
-        "    emotions = []\n",
-        "    for i in range(N):\n",
-        "        emotions.append(emotion_keys[classes[i]])\n",
-        "        \n",
-        "    return emotions\n",
-        "\n",
-        "def softmax(x):\n",
-        "    \"\"\"Compute softmax values (probabilities from 0 to 1) for each possible label.\"\"\"\n",
-        "    x = x.reshape(-1)\n",
-        "    e_x = np.exp(x - np.max(x))\n",
-        "    return e_x / e_x.sum(axis=0)\n",
-        "\n",
-        "def postprocess(scores):\n",
-        "    \"\"\"This function takes the scores generated by the network and \n",
-        "    returns the class IDs in decreasing order of probability.\"\"\"\n",
-        "    prob = softmax(scores)\n",
-        "    prob = np.squeeze(prob)\n",
-        "    classes = np.argsort(prob)[::-1]\n",
-        "    return classes"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Load Test Data\n",
-        "\n",
-        "These are already in your directory from your ONNX model download (from the model zoo).\n",
-        "\n",
-        "Notice that our Model Zoo files have a .pb extension. This is because they are [protobuf files (Protocol Buffers)](https://developers.google.com/protocol-buffers/docs/pythontutorial), so we need to read in our data through our ONNX TensorProto reader into a format we can work with, like numerical arrays."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# to manipulate our arrays\n",
-        "import numpy as np \n",
-        "\n",
-        "# read in test data protobuf files included with the model\n",
-        "import onnx\n",
-        "from onnx import numpy_helper\n",
-        "\n",
-        "# to use parsers to read in our model/data\n",
-        "import json\n",
-        "import os\n",
-        "\n",
-        "test_inputs = []\n",
-        "test_outputs = []\n",
-        "\n",
-        "# read in 3 testing images from .pb files\n",
-        "test_data_size = 3\n",
-        "\n",
-        "for i in np.arange(test_data_size):\n",
-        "    input_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(i), 'input_0.pb')\n",
-        "    output_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(i), 'output_0.pb')\n",
-        "    \n",
-        "    # convert protobuf tensors to np arrays using the TensorProto reader from ONNX\n",
-        "    tensor = onnx.TensorProto()\n",
-        "    with open(input_test_data, 'rb') as f:\n",
-        "        tensor.ParseFromString(f.read())\n",
-        "    \n",
-        "    input_data = numpy_helper.to_array(tensor)\n",
-        "    test_inputs.append(input_data)\n",
-        "    \n",
-        "    with open(output_test_data, 'rb') as f:\n",
-        "        tensor.ParseFromString(f.read())\n",
-        "    \n",
-        "    output_data = numpy_helper.to_array(tensor)\n",
-        "    output_processed = emotion_map(postprocess(output_data))[0]\n",
-        "    test_outputs.append(output_processed)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "nbpresent": {
-          "id": "c3f2f57c-7454-4d3e-b38d-b0946cf066ea"
-        }
-      },
-      "source": [
-        "### Show some sample images\n",
-        "We use `matplotlib` to plot 3 test images from the dataset."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "nbpresent": {
-          "id": "396d478b-34aa-4afa-9898-cdce8222a516"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "plt.figure(figsize = (20, 20))\n",
-        "for test_image in np.arange(3):\n",
-        "    test_inputs[test_image].reshape(1, 64, 64)\n",
-        "    plt.subplot(1, 8, test_image+1)\n",
-        "    plt.axhline('')\n",
-        "    plt.axvline('')\n",
-        "    plt.text(x = 10, y = -10, s = test_outputs[test_image], fontsize = 18)\n",
-        "    plt.imshow(test_inputs[test_image].reshape(64, 64), cmap = plt.cm.gray)\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Run evaluation / prediction"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "plt.figure(figsize = (16, 6), frameon=False)\n",
-        "plt.subplot(1, 8, 1)\n",
-        "\n",
-        "plt.text(x = 0, y = -30, s = \"True Label: \", fontsize = 13, color = 'black')\n",
-        "plt.text(x = 0, y = -20, s = \"Result: \", fontsize = 13, color = 'black')\n",
-        "plt.text(x = 0, y = -10, s = \"Inference Time: \", fontsize = 13, color = 'black')\n",
-        "plt.text(x = 3, y = 14, s = \"Model Input\", fontsize = 12, color = 'black')\n",
-        "plt.text(x = 6, y = 18, s = \"(64 x 64)\", fontsize = 12, color = 'black')\n",
-        "plt.imshow(np.ones((28,28)), cmap=plt.cm.Greys)    \n",
-        "\n",
-        "\n",
-        "for i in np.arange(test_data_size):\n",
-        "    \n",
-        "    input_data = json.dumps({'data': test_inputs[i].tolist()})\n",
-        "\n",
-        "    # predict using the deployed model\n",
-        "    r = json.loads(aci_service.run(input_data))\n",
-        "    \n",
-        "    if \"error\" in r:\n",
-        "        print(r['error'])\n",
-        "        break\n",
-        "        \n",
-        "    result = r['result'][0]\n",
-        "    time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n",
-        "    \n",
-        "    ground_truth = test_outputs[i]\n",
-        "    \n",
-        "    # compare actual value vs. the predicted values:\n",
-        "    plt.subplot(1, 8, i+2)\n",
-        "    plt.axhline('')\n",
-        "    plt.axvline('')\n",
-        "\n",
-        "    # use different color for misclassified sample\n",
-        "    font_color = 'red' if ground_truth != result else 'black'\n",
-        "    clr_map = plt.cm.Greys if ground_truth != result else plt.cm.gray\n",
-        "\n",
-        "    # ground truth labels are in blue\n",
-        "    plt.text(x = 10, y = -70, s = ground_truth, fontsize = 18, color = 'blue')\n",
-        "    \n",
-        "    # predictions are in black if correct, red if incorrect\n",
-        "    plt.text(x = 10, y = -45, s = result, fontsize = 18, color = font_color)\n",
-        "    plt.text(x = 5, y = -22, s = str(time_ms) + ' ms', fontsize = 14, color = font_color)\n",
-        "\n",
-        "    \n",
-        "    plt.imshow(test_inputs[i].reshape(64, 64), cmap = clr_map)\n",
-        "\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Try classifying your own images!"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Preprocessing functions take your image and format it so it can be passed\n",
-        "# as input into our ONNX model\n",
-        "\n",
-        "import cv2\n",
-        "\n",
-        "def rgb2gray(rgb):\n",
-        "    \"\"\"Convert the input image into grayscale\"\"\"\n",
-        "    return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])\n",
-        "\n",
-        "def resize_img(img):\n",
-        "    \"\"\"Resize image to MNIST model input dimensions\"\"\"\n",
-        "    img = cv2.resize(img, dsize=(64, 64), interpolation=cv2.INTER_AREA)\n",
-        "    img.resize((1, 1, 64, 64))\n",
-        "    return img\n",
-        "\n",
-        "def preprocess(img):\n",
-        "    \"\"\"Resize input images and convert them to grayscale.\"\"\"\n",
-        "    if img.shape == (64, 64):\n",
-        "        img.resize((1, 1, 64, 64))\n",
-        "        return img\n",
-        "    \n",
-        "    grayscale = rgb2gray(img)\n",
-        "    processed_img = resize_img(grayscale)\n",
-        "    return processed_img"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Replace the following string with your own path/test image\n",
-        "# Make sure your image is square and the dimensions are equal (i.e. 100 * 100 pixels or 28 * 28 pixels)\n",
-        "\n",
-        "# Any PNG or JPG image file should work\n",
-        "# Make sure to include the entire path with // instead of /\n",
-        "\n",
-        "# e.g. your_test_image = \"C://Users//vinitra.swamy//Pictures//emotion_test_images//img_1.png\"\n",
-        "\n",
-        "import matplotlib.image as mpimg\n",
-        "\n",
-        "if your_test_image != \"<path to file>\":\n",
-        "    img = mpimg.imread(your_test_image)\n",
-        "    plt.subplot(1,3,1)\n",
-        "    plt.imshow(img, cmap = plt.cm.Greys)\n",
-        "    print(\"Old Dimensions: \", img.shape)\n",
-        "    img = preprocess(img)\n",
-        "    print(\"New Dimensions: \", img.shape)\n",
-        "else:\n",
-        "    img = None"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "if img is None:\n",
-        "    print(\"Add the path for your image data.\")\n",
-        "else:\n",
-        "    input_data = json.dumps({'data': img.tolist()})\n",
-        "\n",
-        "    try:\n",
-        "        r = json.loads(aci_service.run(input_data))\n",
-        "        result = r['result'][0]\n",
-        "        time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n",
-        "    except Exception as e:\n",
-        "        print(str(e))\n",
-        "\n",
-        "    plt.figure(figsize = (16, 6))\n",
-        "    plt.subplot(1,8,1)\n",
-        "    plt.axhline('')\n",
-        "    plt.axvline('')\n",
-        "    plt.text(x = -10, y = -40, s = \"Model prediction: \", fontsize = 14)\n",
-        "    plt.text(x = -10, y = -25, s = \"Inference time: \", fontsize = 14)\n",
-        "    plt.text(x = 100, y = -40, s = str(result), fontsize = 14)\n",
-        "    plt.text(x = 100, y = -25, s = str(time_ms) + \" ms\", fontsize = 14)\n",
-        "    plt.text(x = -10, y = -10, s = \"Model Input image: \", fontsize = 14)\n",
-        "    plt.imshow(img.reshape((64, 64)), cmap = plt.cm.gray)    \n",
-        "     "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# remember to delete your service after you are done using it!\n",
-        "\n",
-        "aci_service.delete()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Conclusion\n",
-        "\n",
-        "Congratulations!\n",
-        "\n",
-        "In this tutorial, you have:\n",
-        "- familiarized yourself with ONNX Runtime inference and the pretrained models in the ONNX model zoo\n",
-        "- understood a state-of-the-art convolutional neural net image classification model (FER+ in ONNX) and deployed it in the Azure ML cloud\n",
-        "- ensured that your deep learning model is working perfectly (in the cloud) on test data, and checked it against some of your own!\n",
-        "\n",
-        "Next steps:\n",
-        "- If you have not already, check out another interesting ONNX/AML application that lets you set up a state-of-the-art [handwritten image classification model (MNIST)](https://github.com/Azure/MachineLearningNotebooks/tree/master/onnx/onnx-inference-mnist.ipynb) in the cloud! This tutorial deploys a pre-trained ONNX Computer Vision model for handwritten digit classification in an Azure ML virtual machine.\n",
-        "- Keep an eye out for an updated version of this tutorial that uses ONNX Runtime GPU.\n",
-        "- Contribute to our [open source ONNX repository on github](http://github.com/onnx/onnx) and/or add to our [ONNX model zoo](http://github.com/onnx/models)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "viswamy"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    },
-    "msauthor": "vinitra.swamy"
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

onnx/onnx-inference-facial-expression-recognition-deploy.ipynb

@@ -0,0 +1,809 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Facial Expression Recognition (FER+) using ONNX Runtime on Azure ML\n",
+    "\n",
+    "This example shows how to deploy an image classification neural network using the Facial Expression Recognition ([FER](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data)) dataset and Open Neural Network eXchange format ([ONNX](http://aka.ms/onnxdocarticle)) on the Azure Machine Learning platform. This tutorial will show you how to deploy a FER+ model from the [ONNX model zoo](https://github.com/onnx/models), use it to make predictions using ONNX Runtime Inference, and deploy it as a web service in Azure.\n",
+    "\n",
+    "Throughout this tutorial, we will be referring to ONNX, a neural network exchange format used to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools (CNTK, PyTorch, Caffe, MXNet, TensorFlow) and choose the combination that is best for them. ONNX is developed and supported by a community of partners including Microsoft AI, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai) and [open source files](https://github.com/onnx).\n",
+    "\n",
+    "[ONNX Runtime](https://aka.ms/onnxruntime-python) is the runtime engine that enables evaluation of trained machine learning (Traditional ML and Deep Learning) models with high performance and low resource utilization. We use the CPU version of ONNX Runtime in this tutorial, but will soon be releasing an additional tutorial for deploying this model using ONNX Runtime GPU.\n",
+    "\n",
+    "#### Tutorial Objectives:\n",
+    "\n",
+    "1. Describe the FER+ dataset and pretrained Convolutional Neural Net ONNX model for Emotion Recognition, stored in the ONNX model zoo.\n",
+    "2. Deploy and run the pretrained FER+ ONNX model on an Azure Machine Learning instance\n",
+    "3. Predict labels for test set data points in the cloud using ONNX Runtime and Azure ML"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "\n",
+    "### 1. Install Azure ML SDK and create a new workspace\n",
+    "Please follow [Azure ML configuration notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) to set up your environment.\n",
+    "\n",
+    "### 2. Install additional packages needed for this Notebook\n",
+    "You need to install the popular plotting library `matplotlib`, the image manipulation library `opencv`, and the `onnx` library in the conda environment where Azure Maching Learning SDK is installed.\n",
+    "\n",
+    "```sh\n",
+    "(myenv) $ pip install matplotlib onnx opencv-python\n",
+    "```\n",
+    "\n",
+    "**Debugging tip**: Make sure that to activate your virtual environment (myenv) before you re-launch this notebook using the `jupyter notebook` comand. Choose the respective Python kernel for your new virtual environment using the `Kernel > Change Kernel` menu above. If you have completed the steps correctly, the upper right corner of your screen should state `Python [conda env:myenv]` instead of `Python [default]`.\n",
+    "\n",
+    "### 3. Download sample data and pre-trained ONNX model from ONNX Model Zoo.\n",
+    "\n",
+    "In the following lines of code, we download [the trained ONNX Emotion FER+ model and corresponding test data](https://github.com/onnx/models/tree/master/emotion_ferplus) and place them in the same folder as this tutorial notebook. For more information about the FER+ dataset, please visit Microsoft Researcher Emad Barsoum's [FER+ source data repository](https://github.com/ebarsoum/FERPlus)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# urllib is a built-in Python library to download files from URLs\n",
+    "\n",
+    "# Objective: retrieve the latest version of the ONNX Emotion FER+ model files from the\n",
+    "# ONNX Model Zoo and save it in the same folder as this tutorial\n",
+    "\n",
+    "import urllib.request\n",
+    "\n",
+    "onnx_model_url = \"https://www.cntk.ai/OnnxModels/emotion_ferplus/opset_7/emotion_ferplus.tar.gz\"\n",
+    "\n",
+    "urllib.request.urlretrieve(onnx_model_url, filename=\"emotion_ferplus.tar.gz\")\n",
+    "\n",
+    "# the ! magic command tells our jupyter notebook kernel to run the following line of \n",
+    "# code from the command line instead of the notebook kernel\n",
+    "\n",
+    "# We use tar and xvcf to unzip the files we just retrieved from the ONNX model zoo\n",
+    "\n",
+    "!tar xvzf emotion_ferplus.tar.gz"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deploy a VM with your ONNX model in the Cloud\n",
+    "\n",
+    "### Load Azure ML workspace\n",
+    "\n",
+    "We begin by instantiating a workspace object from the existing workspace created earlier in the configuration notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check core SDK version number\n",
+    "import azureml.core\n",
+    "\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.location, ws.resource_group, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Registering your model with Azure ML"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_dir = \"emotion_ferplus\" # replace this with the location of your model files\n",
+    "\n",
+    "# leave as is if it's in the same folder as this notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.model import Model\n",
+    "\n",
+    "model = Model.register(model_path = model_dir + \"/\" + \"model.onnx\",\n",
+    "                       model_name = \"onnx_emotion\",\n",
+    "                       tags = {\"onnx\": \"demo\"},\n",
+    "                       description = \"FER+ emotion recognition CNN from ONNX Model Zoo\",\n",
+    "                       workspace = ws)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Optional: Displaying your registered models\n",
+    "\n",
+    "This step is not required, so feel free to skip it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models = ws.models\n",
+    "for name, m in models.items():\n",
+    "    print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ONNX FER+ Model Methodology\n",
+    "\n",
+    "The image classification model we are using is pre-trained using Microsoft's deep learning cognitive toolkit, [CNTK](https://github.com/Microsoft/CNTK), from the [ONNX model zoo](http://github.com/onnx/models). The model zoo has many other models that can be deployed on cloud providers like AzureML without any additional training. To ensure that our cloud deployed model works, we use testing data from the well-known FER+ data set, provided as part of the [trained Emotion Recognition model](https://github.com/onnx/models/tree/master/emotion_ferplus) in the ONNX model zoo.\n",
+    "\n",
+    "The original Facial Emotion Recognition (FER) Dataset was released in 2013 by Pierre-Luc Carrier and Aaron Courville as part of a [Kaggle Competition](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data), but some of the labels are not entirely appropriate for the expression. In the FER+ Dataset, each photo was evaluated by at least 10 croud sourced reviewers, creating a more accurate basis for ground truth. \n",
+    "\n",
+    "You can see the difference of label quality in the sample model input below. The FER labels are the first word below each image, and the FER+ labels are the second word below each image.\n",
+    "\n",
+    "![](https://raw.githubusercontent.com/Microsoft/FERPlus/master/FER+vsFER.png)\n",
+    "\n",
+    "***Input: Photos of cropped faces from FER+ Dataset***\n",
+    "\n",
+    "***Task: Classify each facial image into its appropriate emotions in the emotion table***\n",
+    "\n",
+    "```    emotion_table = {'neutral':0, 'happiness':1, 'surprise':2, 'sadness':3, 'anger':4, 'disgust':5, 'fear':6, 'contempt':7} ```\n",
+    "\n",
+    "***Output: Emotion prediction for input image***\n",
+    "\n",
+    "\n",
+    "Remember, once the application is deployed in Azure ML, you can use your own images as input for the model to classify."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for images and plots in this notebook\n",
+    "import matplotlib.pyplot as plt  \n",
+    "from IPython.display import Image\n",
+    "\n",
+    "# display images inline\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model Description\n",
+    "\n",
+    "The FER+ model from the ONNX Model Zoo is summarized by the graphic below. You can see the entire workflow of our pre-trained model in the following image from Barsoum et. al's paper [\"Training Deep Networks for Facial Expression Recognition\n",
+    "with Crowd-Sourced Label Distribution\"](https://arxiv.org/pdf/1608.01041.pdf), with our (64 x 64) input images and our output probabilities for each of the labels."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](https://raw.githubusercontent.com/vinitra/FERPlus/master/emotion_model_img.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Specify our Score and Environment Files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are now going to deploy our ONNX Model on AML with inference in ONNX Runtime. We begin by writing a score.py file, which will help us run the model in our Azure ML virtual machine (VM), and then specify our environment by writing a yml file. You will also notice that we import the onnxruntime library to do runtime inference on our ONNX models (passing in input and evaluating out model's predicted output). More information on the API and commands can be found in the [ONNX Runtime documentation](https://aka.ms/onnxruntime).\n",
+    "\n",
+    "### Write Score File\n",
+    "\n",
+    "A score file is what tells our Azure cloud service what to do. After initializing our model using azureml.core.model, we start an ONNX Runtime inference session to evaluate the data passed in on our function calls."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile score.py\n",
+    "import json\n",
+    "import numpy as np\n",
+    "import onnxruntime\n",
+    "import sys\n",
+    "import os\n",
+    "from azureml.core.model import Model\n",
+    "import time\n",
+    "\n",
+    "def init():\n",
+    "    global session, input_name, output_name\n",
+    "    model = Model.get_model_path(model_name = 'onnx_emotion')\n",
+    "    session = onnxruntime.InferenceSession(model, None)\n",
+    "    input_name = session.get_inputs()[0].name\n",
+    "    output_name = session.get_outputs()[0].name \n",
+    "    \n",
+    "def run(input_data):\n",
+    "    '''Purpose: evaluate test input in Azure Cloud using onnxruntime.\n",
+    "        We will call the run function later from our Jupyter Notebook \n",
+    "        so our azure service can evaluate our model input in the cloud. '''\n",
+    "\n",
+    "    try:\n",
+    "        # load in our data, convert to readable format\n",
+    "        data = np.array(json.loads(input_data)['data']).astype('float32')\n",
+    "        \n",
+    "        start = time.time()\n",
+    "        r = session.run([output_name], {input_name : data})\n",
+    "        end = time.time()\n",
+    "        \n",
+    "        result = emotion_map(postprocess(r[0]))\n",
+    "        \n",
+    "        result_dict = {\"result\": result,\n",
+    "                      \"time_in_sec\": [end - start]}\n",
+    "    except Exception as e:\n",
+    "        result_dict = {\"error\": str(e)}\n",
+    "    \n",
+    "    return json.dumps(result_dict)\n",
+    "\n",
+    "def emotion_map(classes, N=1):\n",
+    "    \"\"\"Take the most probable labels (output of postprocess) and returns the \n",
+    "    top N emotional labels that fit the picture.\"\"\"\n",
+    "    \n",
+    "    emotion_table = {'neutral':0, 'happiness':1, 'surprise':2, 'sadness':3, \n",
+    "                     'anger':4, 'disgust':5, 'fear':6, 'contempt':7}\n",
+    "    \n",
+    "    emotion_keys = list(emotion_table.keys())\n",
+    "    emotions = []\n",
+    "    for i in range(N):\n",
+    "        emotions.append(emotion_keys[classes[i]])\n",
+    "    return emotions\n",
+    "\n",
+    "def softmax(x):\n",
+    "    \"\"\"Compute softmax values (probabilities from 0 to 1) for each possible label.\"\"\"\n",
+    "    x = x.reshape(-1)\n",
+    "    e_x = np.exp(x - np.max(x))\n",
+    "    return e_x / e_x.sum(axis=0)\n",
+    "\n",
+    "def postprocess(scores):\n",
+    "    \"\"\"This function takes the scores generated by the network and \n",
+    "    returns the class IDs in decreasing order of probability.\"\"\"\n",
+    "    prob = softmax(scores)\n",
+    "    prob = np.squeeze(prob)\n",
+    "    classes = np.argsort(prob)[::-1]\n",
+    "    return classes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Write Environment File"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.conda_dependencies import CondaDependencies \n",
+    "\n",
+    "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\"])\n",
+    "\n",
+    "with open(\"myenv.yml\",\"w\") as f:\n",
+    "    f.write(myenv.serialize_to_string())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create the Container Image\n",
+    "\n",
+    "This step will likely take a few minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.image import ContainerImage\n",
+    "\n",
+    "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
+    "                                                  runtime = \"python\",\n",
+    "                                                  conda_file = \"myenv.yml\",\n",
+    "                                                  description = \"Emotion ONNX Runtime container\",\n",
+    "                                                  tags = {\"demo\": \"onnx\"})\n",
+    "\n",
+    "\n",
+    "image = ContainerImage.create(name = \"onnximage\",\n",
+    "                              # this is the model object\n",
+    "                              models = [model],\n",
+    "                              image_config = image_config,\n",
+    "                              workspace = ws)\n",
+    "\n",
+    "image.wait_for_creation(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In case you need to debug your code, the next line of code accesses the log file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(image.image_build_log_uri)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We're all done specifying what we want our virtual machine to do. Let's configure and deploy our container image.\n",
+    "\n",
+    "### Deploy the container image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.webservice import AciWebservice\n",
+    "\n",
+    "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
+    "                                               memory_gb = 1, \n",
+    "                                               tags = {'demo': 'onnx'}, \n",
+    "                                               description = 'ONNX for emotion recognition model')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.webservice import Webservice\n",
+    "\n",
+    "aci_service_name = 'onnx-demo-emotion'\n",
+    "print(\"Service\", aci_service_name)\n",
+    "\n",
+    "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
+    "                                           image = image,\n",
+    "                                           name = aci_service_name,\n",
+    "                                           workspace = ws)\n",
+    "\n",
+    "aci_service.wait_for_deployment(True)\n",
+    "print(aci_service.state)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following cell will likely take a few minutes to run as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if aci_service.state != 'Healthy':\n",
+    "    # run this command for debugging.\n",
+    "    print(aci_service.get_logs())\n",
+    "\n",
+    "    # If your deployment fails, make sure to delete your aci_service before trying again!\n",
+    "    # aci_service.delete()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Success!\n",
+    "\n",
+    "If you've made it this far, you've deployed a working VM with a facial emotion recognition model running in the cloud using Azure ML. Congratulations!\n",
+    "\n",
+    "Let's see how well our model deals with our test images."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Testing and Evaluation\n",
+    "\n",
+    "### Useful Helper Functions\n",
+    "\n",
+    "We preprocess and postprocess our data (see score.py file) using the helper functions specified in the [ONNX FER+ Model page in the Model Zoo repository](https://github.com/onnx/models/tree/master/emotion_ferplus)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def emotion_map(classes, N=1):\n",
+    "    \"\"\"Take the most probable labels (output of postprocess) and returns the \n",
+    "    top N emotional labels that fit the picture.\"\"\"\n",
+    "    \n",
+    "    emotion_table = {'neutral':0, 'happiness':1, 'surprise':2, 'sadness':3, \n",
+    "                     'anger':4, 'disgust':5, 'fear':6, 'contempt':7}\n",
+    "    \n",
+    "    emotion_keys = list(emotion_table.keys())\n",
+    "    emotions = []\n",
+    "    for i in range(N):\n",
+    "        emotions.append(emotion_keys[classes[i]])\n",
+    "    return emotions\n",
+    "\n",
+    "def softmax(x):\n",
+    "    \"\"\"Compute softmax values (probabilities from 0 to 1) for each possible label.\"\"\"\n",
+    "    x = x.reshape(-1)\n",
+    "    e_x = np.exp(x - np.max(x))\n",
+    "    return e_x / e_x.sum(axis=0)\n",
+    "\n",
+    "def postprocess(scores):\n",
+    "    \"\"\"This function takes the scores generated by the network and \n",
+    "    returns the class IDs in decreasing order of probability.\"\"\"\n",
+    "    prob = softmax(scores)\n",
+    "    prob = np.squeeze(prob)\n",
+    "    classes = np.argsort(prob)[::-1]\n",
+    "    return classes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load Test Data\n",
+    "\n",
+    "These are already in your directory from your ONNX model download (from the model zoo).\n",
+    "\n",
+    "Notice that our Model Zoo files have a .pb extension. This is because they are [protobuf files (Protocol Buffers)](https://developers.google.com/protocol-buffers/docs/pythontutorial), so we need to read in our data through our ONNX TensorProto reader into a format we can work with, like numerical arrays."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# to manipulate our arrays\n",
+    "import numpy as np \n",
+    "\n",
+    "# read in test data protobuf files included with the model\n",
+    "import onnx\n",
+    "from onnx import numpy_helper\n",
+    "\n",
+    "# to use parsers to read in our model/data\n",
+    "import json\n",
+    "import os\n",
+    "\n",
+    "test_inputs = []\n",
+    "test_outputs = []\n",
+    "\n",
+    "# read in 3 testing images from .pb files\n",
+    "test_data_size = 3\n",
+    "\n",
+    "for i in np.arange(test_data_size):\n",
+    "    input_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(i), 'input_0.pb')\n",
+    "    output_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(i), 'output_0.pb')\n",
+    "    \n",
+    "    # convert protobuf tensors to np arrays using the TensorProto reader from ONNX\n",
+    "    tensor = onnx.TensorProto()\n",
+    "    with open(input_test_data, 'rb') as f:\n",
+    "        tensor.ParseFromString(f.read())\n",
+    "    \n",
+    "    input_data = numpy_helper.to_array(tensor)\n",
+    "    test_inputs.append(input_data)\n",
+    "    \n",
+    "    with open(output_test_data, 'rb') as f:\n",
+    "        tensor.ParseFromString(f.read())\n",
+    "    \n",
+    "    output_data = numpy_helper.to_array(tensor)\n",
+    "    output_processed = emotion_map(postprocess(output_data[0]))[0]\n",
+    "    test_outputs.append(output_processed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "nbpresent": {
+     "id": "c3f2f57c-7454-4d3e-b38d-b0946cf066ea"
+    }
+   },
+   "source": [
+    "### Show some sample images\n",
+    "We use `matplotlib` to plot 3 test images from the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "nbpresent": {
+     "id": "396d478b-34aa-4afa-9898-cdce8222a516"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize = (20, 20))\n",
+    "for test_image in np.arange(3):\n",
+    "    test_inputs[test_image].reshape(1, 64, 64)\n",
+    "    plt.subplot(1, 8, test_image+1)\n",
+    "    plt.axhline('')\n",
+    "    plt.axvline('')\n",
+    "    plt.text(x = 10, y = -10, s = test_outputs[test_image], fontsize = 18)\n",
+    "    plt.imshow(test_inputs[test_image].reshape(64, 64), cmap = plt.cm.gray)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run evaluation / prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize = (16, 6), frameon=False)\n",
+    "plt.subplot(1, 8, 1)\n",
+    "\n",
+    "plt.text(x = 0, y = -30, s = \"True Label: \", fontsize = 13, color = 'black')\n",
+    "plt.text(x = 0, y = -20, s = \"Result: \", fontsize = 13, color = 'black')\n",
+    "plt.text(x = 0, y = -10, s = \"Inference Time: \", fontsize = 13, color = 'black')\n",
+    "plt.text(x = 3, y = 14, s = \"Model Input\", fontsize = 12, color = 'black')\n",
+    "plt.text(x = 6, y = 18, s = \"(64 x 64)\", fontsize = 12, color = 'black')\n",
+    "plt.imshow(np.ones((28,28)), cmap=plt.cm.Greys)    \n",
+    "\n",
+    "\n",
+    "for i in np.arange(test_data_size):\n",
+    "    \n",
+    "    input_data = json.dumps({'data': test_inputs[i].tolist()})\n",
+    "\n",
+    "    # predict using the deployed model\n",
+    "    r = json.loads(aci_service.run(input_data))\n",
+    "    \n",
+    "    if \"error\" in r:\n",
+    "        print(r['error'])\n",
+    "        break\n",
+    "        \n",
+    "    result = r['result'][0]\n",
+    "    time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n",
+    "    \n",
+    "    ground_truth = test_outputs[i]\n",
+    "    \n",
+    "    # compare actual value vs. the predicted values:\n",
+    "    plt.subplot(1, 8, i+2)\n",
+    "    plt.axhline('')\n",
+    "    plt.axvline('')\n",
+    "\n",
+    "    # use different color for misclassified sample\n",
+    "    font_color = 'red' if ground_truth != result else 'black'\n",
+    "    clr_map = plt.cm.Greys if ground_truth != result else plt.cm.gray\n",
+    "\n",
+    "    # ground truth labels are in blue\n",
+    "    plt.text(x = 10, y = -70, s = ground_truth, fontsize = 18, color = 'blue')\n",
+    "    \n",
+    "    # predictions are in black if correct, red if incorrect\n",
+    "    plt.text(x = 10, y = -45, s = result, fontsize = 18, color = font_color)\n",
+    "    plt.text(x = 5, y = -22, s = str(time_ms) + ' ms', fontsize = 14, color = font_color)\n",
+    "\n",
+    "    \n",
+    "    plt.imshow(test_inputs[i].reshape(64, 64), cmap = clr_map)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Try classifying your own images!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocessing functions take your image and format it so it can be passed\n",
+    "# as input into our ONNX model\n",
+    "\n",
+    "import cv2\n",
+    "\n",
+    "def rgb2gray(rgb):\n",
+    "    \"\"\"Convert the input image into grayscale\"\"\"\n",
+    "    return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])\n",
+    "\n",
+    "def resize_img(img):\n",
+    "    \"\"\"Resize image to MNIST model input dimensions\"\"\"\n",
+    "    img = cv2.resize(img, dsize=(64, 64), interpolation=cv2.INTER_AREA)\n",
+    "    img.resize((1, 1, 64, 64))\n",
+    "    return img\n",
+    "\n",
+    "def preprocess(img):\n",
+    "    \"\"\"Resize input images and convert them to grayscale.\"\"\"\n",
+    "    if img.shape == (64, 64):\n",
+    "        img.resize((1, 1, 64, 64))\n",
+    "        return img\n",
+    "    \n",
+    "    grayscale = rgb2gray(img)\n",
+    "    processed_img = resize_img(grayscale)\n",
+    "    return processed_img"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Replace the following string with your own path/test image\n",
+    "# Make sure your image is square and the dimensions are equal (i.e. 100 * 100 pixels or 28 * 28 pixels)\n",
+    "\n",
+    "# Any PNG or JPG image file should work\n",
+    "# Make sure to include the entire path with // instead of /\n",
+    "\n",
+    "# e.g. your_test_image = \"C:/Users/vinitra.swamy/Pictures/face.png\"\n",
+    "\n",
+    "your_test_image = \"<path to file>\"\n",
+    "\n",
+    "import matplotlib.image as mpimg\n",
+    "\n",
+    "if your_test_image != \"<path to file>\":\n",
+    "    img = mpimg.imread(your_test_image)\n",
+    "    plt.subplot(1,3,1)\n",
+    "    plt.imshow(img, cmap = plt.cm.Greys)\n",
+    "    print(\"Old Dimensions: \", img.shape)\n",
+    "    img = preprocess(img)\n",
+    "    print(\"New Dimensions: \", img.shape)\n",
+    "else:\n",
+    "    img = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if img is None:\n",
+    "    print(\"Add the path for your image data.\")\n",
+    "else:\n",
+    "    input_data = json.dumps({'data': img.tolist()})\n",
+    "\n",
+    "    try:\n",
+    "        r = json.loads(aci_service.run(input_data))\n",
+    "        result = r['result'][0]\n",
+    "        time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n",
+    "    except Exception as e:\n",
+    "        print(str(e))\n",
+    "\n",
+    "    plt.figure(figsize = (16, 6))\n",
+    "    plt.subplot(1,8,1)\n",
+    "    plt.axhline('')\n",
+    "    plt.axvline('')\n",
+    "    plt.text(x = -10, y = -40, s = \"Model prediction: \", fontsize = 14)\n",
+    "    plt.text(x = -10, y = -25, s = \"Inference time: \", fontsize = 14)\n",
+    "    plt.text(x = 100, y = -40, s = str(result), fontsize = 14)\n",
+    "    plt.text(x = 100, y = -25, s = str(time_ms) + \" ms\", fontsize = 14)\n",
+    "    plt.text(x = -10, y = -10, s = \"Model Input image: \", fontsize = 14)\n",
+    "    plt.imshow(img.reshape((64, 64)), cmap = plt.cm.gray)    \n",
+    "     "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remember to delete your service after you are done using it!\n",
+    "\n",
+    "# aci_service.delete()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "Congratulations!\n",
+    "\n",
+    "In this tutorial, you have:\n",
+    "- familiarized yourself with ONNX Runtime inference and the pretrained models in the ONNX model zoo\n",
+    "- understood a state-of-the-art convolutional neural net image classification model (FER+ in ONNX) and deployed it in the Azure ML cloud\n",
+    "- ensured that your deep learning model is working perfectly (in the cloud) on test data, and checked it against some of your own!\n",
+    "\n",
+    "Next steps:\n",
+    "- If you have not already, check out another interesting ONNX/AML application that lets you set up a state-of-the-art [handwritten image classification model (MNIST)](https://github.com/Azure/MachineLearningNotebooks/tree/master/onnx/onnx-inference-mnist.ipynb) in the cloud! This tutorial deploys a pre-trained ONNX Computer Vision model for handwritten digit classification in an Azure ML virtual machine.\n",
+    "- Keep an eye out for an updated version of this tutorial that uses ONNX Runtime GPU.\n",
+    "- Contribute to our [open source ONNX repository on github](http://github.com/onnx/onnx) and/or add to our [ONNX model zoo](http://github.com/onnx/models)"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "viswamy"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  },
+  "msauthor": "vinitra.swamy"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

onnx/onnx-inference-mnist-deploy.ipynb

@@ -0,0 +1,792 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Handwritten Digit Classification (MNIST) using ONNX Runtime on Azure ML\n",
+    "\n",
+    "This example shows how to deploy an image classification neural network using the Modified National Institute of Standards and Technology ([MNIST](http://yann.lecun.com/exdb/mnist/)) dataset and Open Neural Network eXchange format ([ONNX](http://aka.ms/onnxdocarticle)) on the Azure Machine Learning platform. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing number from 0 to 9. This tutorial will show you how to deploy a MNIST model from the [ONNX model zoo](https://github.com/onnx/models), use it to make predictions using ONNX Runtime Inference, and deploy it as a web service in Azure.\n",
+    "\n",
+    "Throughout this tutorial, we will be referring to ONNX, a neural network exchange format used to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools (CNTK, PyTorch, Caffe, MXNet, TensorFlow) and choose the combination that is best for them. ONNX is developed and supported by a community of partners including Microsoft AI, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai) and [open source files](https://github.com/onnx).\n",
+    "\n",
+    "[ONNX Runtime](https://aka.ms/onnxruntime-python) is the runtime engine that enables evaluation of trained machine learning (Traditional ML and Deep Learning) models with high performance and low resource utilization.\n",
+    "\n",
+    "#### Tutorial Objectives:\n",
+    "\n",
+    "- Describe the MNIST dataset and pretrained Convolutional Neural Net ONNX model, stored in the ONNX model zoo.\n",
+    "- Deploy and run the pretrained MNIST ONNX model on an Azure Machine Learning instance\n",
+    "- Predict labels for test set data points in the cloud using ONNX Runtime and Azure ML"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "\n",
+    "### 1. Install Azure ML SDK and create a new workspace\n",
+    "Please follow [Azure ML configuration notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) to set up your environment.\n",
+    "\n",
+    "### 2. Install additional packages needed for this tutorial notebook\n",
+    "You need to install the popular plotting library `matplotlib`, the image manipulation library `opencv`, and the `onnx` library in the conda environment where Azure Maching Learning SDK is installed. \n",
+    "\n",
+    "```sh\n",
+    "(myenv) $ pip install matplotlib onnx opencv-python\n",
+    "```\n",
+    "\n",
+    "**Debugging tip**: Make sure that you run the \"jupyter notebook\" command to launch this notebook after activating your virtual environment. Choose the respective Python kernel for your new virtual environment using the `Kernel > Change Kernel` menu above. If you have completed the steps correctly, the upper right corner of your screen should state `Python [conda env:myenv]` instead of `Python [default]`.\n",
+    "\n",
+    "### 3. Download sample data and pre-trained ONNX model from ONNX Model Zoo.\n",
+    "\n",
+    "In the following lines of code, we download [the trained ONNX MNIST model and corresponding test data](https://github.com/onnx/models/tree/master/mnist) and place them in the same folder as this tutorial notebook. For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# urllib is a built-in Python library to download files from URLs\n",
+    "\n",
+    "# Objective: retrieve the latest version of the ONNX MNIST model files from the\n",
+    "# ONNX Model Zoo and save it in the same folder as this tutorial\n",
+    "\n",
+    "import urllib.request\n",
+    "\n",
+    "onnx_model_url = \"https://www.cntk.ai/OnnxModels/mnist/opset_7/mnist.tar.gz\"\n",
+    "\n",
+    "urllib.request.urlretrieve(onnx_model_url, filename=\"mnist.tar.gz\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the ! magic command tells our jupyter notebook kernel to run the following line of \n",
+    "# code from the command line instead of the notebook kernel\n",
+    "\n",
+    "# We use tar and xvcf to unzip the files we just retrieved from the ONNX model zoo\n",
+    "\n",
+    "!tar xvzf mnist.tar.gz"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deploy a VM with your ONNX model in the Cloud\n",
+    "\n",
+    "### Load Azure ML workspace\n",
+    "\n",
+    "We begin by instantiating a workspace object from the existing workspace created earlier in the configuration notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check core SDK version number\n",
+    "import azureml.core\n",
+    "\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.resource_group, ws.location, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Registering your model with Azure ML"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_dir = \"mnist\" # replace this with the location of your model files\n",
+    "\n",
+    "# leave as is if it's in the same folder as this notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.model import Model\n",
+    "\n",
+    "model = Model.register(workspace = ws,\n",
+    "                       model_path = model_dir + \"/\" + \"model.onnx\",\n",
+    "                       model_name = \"mnist_1\",\n",
+    "                       tags = {\"onnx\": \"demo\"},\n",
+    "                       description = \"MNIST image classification CNN from ONNX Model Zoo\",)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Optional: Displaying your registered models\n",
+    "\n",
+    "This step is not required, so feel free to skip it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models = ws.models\n",
+    "for name, m in models.items():\n",
+    "    print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "nbpresent": {
+     "id": "c3f2f57c-7454-4d3e-b38d-b0946cf066ea"
+    }
+   },
+   "source": [
+    "### ONNX MNIST Model Methodology\n",
+    "\n",
+    "The image classification model we are using is pre-trained using Microsoft's deep learning cognitive toolkit, [CNTK](https://github.com/Microsoft/CNTK), from the [ONNX model zoo](http://github.com/onnx/models). The model zoo has many other models that can be deployed on cloud providers like AzureML without any additional training. To ensure that our cloud deployed model works, we use testing data from the famous MNIST data set, provided as part of the [trained MNIST model](https://github.com/onnx/models/tree/master/mnist) in the ONNX model zoo.\n",
+    "\n",
+    "***Input: Handwritten Images from MNIST Dataset***\n",
+    "\n",
+    "***Task: Classify each MNIST image into an appropriate digit***\n",
+    "\n",
+    "***Output: Digit prediction for input image***\n",
+    "\n",
+    "Run the cell below to look at some of the sample images from the MNIST dataset that we used to train this ONNX model. Remember, once the application is deployed in Azure ML, you can use your own images as input for the model to classify!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for images and plots in this notebook\n",
+    "import matplotlib.pyplot as plt  \n",
+    "from IPython.display import Image\n",
+    "\n",
+    "# display images inline\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Image(url=\"http://3.bp.blogspot.com/_UpN7DfJA0j4/TJtUBWPk0SI/AAAAAAAAABY/oWPMtmqJn3k/s1600/mnist_originals.png\", width=200, height=200)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Specify our Score and Environment Files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are now going to deploy our ONNX Model on AML with inference in ONNX Runtime. We begin by writing a score.py file, which will help us run the model in our Azure ML virtual machine (VM), and then specify our environment by writing a yml file. You will also notice that we import the onnxruntime library to do runtime inference on our ONNX models (passing in input and evaluating out model's predicted output). More information on the API and commands can be found in the [ONNX Runtime documentation](https://aka.ms/onnxruntime).\n",
+    "\n",
+    "### Write Score File\n",
+    "\n",
+    "A score file is what tells our Azure cloud service what to do. After initializing our model using azureml.core.model, we start an ONNX Runtime inference session to evaluate the data passed in on our function calls."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile score.py\n",
+    "import json\n",
+    "import numpy as np\n",
+    "import onnxruntime\n",
+    "import sys\n",
+    "import os\n",
+    "from azureml.core.model import Model\n",
+    "import time\n",
+    "\n",
+    "\n",
+    "def init():\n",
+    "    global session, input_name, output_name\n",
+    "    model = Model.get_model_path(model_name = 'mnist_1')\n",
+    "    session = onnxruntime.InferenceSession(model, None)\n",
+    "    input_name = session.get_inputs()[0].name\n",
+    "    output_name = session.get_outputs()[0].name \n",
+    "    \n",
+    "def run(input_data):\n",
+    "    '''Purpose: evaluate test input in Azure Cloud using onnxruntime.\n",
+    "        We will call the run function later from our Jupyter Notebook \n",
+    "        so our azure service can evaluate our model input in the cloud. '''\n",
+    "\n",
+    "    try:\n",
+    "        # load in our data, convert to readable format\n",
+    "        data = np.array(json.loads(input_data)['data']).astype('float32')\n",
+    "\n",
+    "        start = time.time()\n",
+    "        r = session.run([output_name], {input_name: data})[0]\n",
+    "        end = time.time()\n",
+    "        result = choose_class(r[0])\n",
+    "        result_dict = {\"result\": [result],\n",
+    "                      \"time_in_sec\": [end - start]}\n",
+    "    except Exception as e:\n",
+    "        result_dict = {\"error\": str(e)}\n",
+    "    \n",
+    "    return json.dumps(result_dict)\n",
+    "\n",
+    "def choose_class(result_prob):\n",
+    "    \"\"\"We use argmax to determine the right label to choose from our output\"\"\"\n",
+    "    return int(np.argmax(result_prob, axis=0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Write Environment File\n",
+    "\n",
+    "This step creates a YAML environment file that specifies which dependencies we would like to see in our Linux Virtual Machine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.conda_dependencies import CondaDependencies \n",
+    "\n",
+    "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\"])\n",
+    "\n",
+    "with open(\"myenv.yml\",\"w\") as f:\n",
+    "    f.write(myenv.serialize_to_string())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create the Container Image\n",
+    "This step will likely take a few minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.image import ContainerImage\n",
+    "\n",
+    "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
+    "                                                  runtime = \"python\",\n",
+    "                                                  conda_file = \"myenv.yml\",\n",
+    "                                                  description = \"MNIST ONNX Runtime container\",\n",
+    "                                                  tags = {\"demo\": \"onnx\"}) \n",
+    "\n",
+    "\n",
+    "image = ContainerImage.create(name = \"onnximage\",\n",
+    "                              # this is the model object\n",
+    "                              models = [model],\n",
+    "                              image_config = image_config,\n",
+    "                              workspace = ws)\n",
+    "\n",
+    "image.wait_for_creation(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In case you need to debug your code, the next line of code accesses the log file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(image.image_build_log_uri)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We're all done specifying what we want our virtual machine to do. Let's configure and deploy our container image.\n",
+    "\n",
+    "### Deploy the container image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.webservice import AciWebservice\n",
+    "\n",
+    "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
+    "                                               memory_gb = 1, \n",
+    "                                               tags = {'demo': 'onnx'}, \n",
+    "                                               description = 'ONNX for mnist model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following cell will likely take a few minutes to run as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.webservice import Webservice\n",
+    "\n",
+    "aci_service_name = 'onnx-demo-mnist'\n",
+    "print(\"Service\", aci_service_name)\n",
+    "\n",
+    "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
+    "                                           image = image,\n",
+    "                                           name = aci_service_name,\n",
+    "                                           workspace = ws)\n",
+    "\n",
+    "aci_service.wait_for_deployment(True)\n",
+    "print(aci_service.state)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if aci_service.state != 'Healthy':\n",
+    "    # run this command for debugging.\n",
+    "    print(aci_service.get_logs())\n",
+    "\n",
+    "    # If your deployment fails, make sure to delete your aci_service or rename your service before trying again!\n",
+    "    # aci_service.delete()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Success!\n",
+    "\n",
+    "If you've made it this far, you've deployed a working VM with a handwritten digit classifier running in the cloud using Azure ML. Congratulations!\n",
+    "\n",
+    "Let's see how well our model deals with our test images."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Testing and Evaluation\n",
+    "\n",
+    "### Load Test Data\n",
+    "\n",
+    "These are already in your directory from your ONNX model download (from the model zoo).\n",
+    "\n",
+    "Notice that our Model Zoo files have a .pb extension. This is because they are [protobuf files (Protocol Buffers)](https://developers.google.com/protocol-buffers/docs/pythontutorial), so we need to read in our data through our ONNX TensorProto reader into a format we can work with, like numerical arrays."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# to manipulate our arrays\n",
+    "import numpy as np \n",
+    "\n",
+    "# read in test data protobuf files included with the model\n",
+    "import onnx\n",
+    "from onnx import numpy_helper\n",
+    "\n",
+    "# to use parsers to read in our model/data\n",
+    "import json\n",
+    "import os\n",
+    "\n",
+    "test_inputs = []\n",
+    "test_outputs = []\n",
+    "\n",
+    "# read in 3 testing images from .pb files\n",
+    "test_data_size = 3\n",
+    "\n",
+    "for i in np.arange(test_data_size):\n",
+    "    input_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(i), 'input_0.pb')\n",
+    "    output_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(i), 'output_0.pb')\n",
+    "    \n",
+    "    # convert protobuf tensors to np arrays using the TensorProto reader from ONNX\n",
+    "    tensor = onnx.TensorProto()\n",
+    "    with open(input_test_data, 'rb') as f:\n",
+    "        tensor.ParseFromString(f.read())\n",
+    "    \n",
+    "    input_data = numpy_helper.to_array(tensor)\n",
+    "    test_inputs.append(input_data)\n",
+    "    \n",
+    "    with open(output_test_data, 'rb') as f:\n",
+    "        tensor.ParseFromString(f.read())\n",
+    "    \n",
+    "    output_data = numpy_helper.to_array(tensor)\n",
+    "    test_outputs.append(output_data)\n",
+    "    \n",
+    "if len(test_inputs) == test_data_size:\n",
+    "    print('Test data loaded successfully.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "nbpresent": {
+     "id": "c3f2f57c-7454-4d3e-b38d-b0946cf066ea"
+    }
+   },
+   "source": [
+    "### Show some sample images\n",
+    "We use `matplotlib` to plot 3 test images from the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "nbpresent": {
+     "id": "396d478b-34aa-4afa-9898-cdce8222a516"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize = (16, 6))\n",
+    "for test_image in np.arange(3):\n",
+    "    plt.subplot(1, 15, test_image+1)\n",
+    "    plt.axhline('')\n",
+    "    plt.axvline('')\n",
+    "    plt.imshow(test_inputs[test_image].reshape(28, 28), cmap = plt.cm.Greys)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run evaluation / prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize = (16, 6), frameon=False)\n",
+    "plt.subplot(1, 8, 1)\n",
+    "\n",
+    "plt.text(x = 0, y = -30, s = \"True Label: \", fontsize = 13, color = 'black')\n",
+    "plt.text(x = 0, y = -20, s = \"Result: \", fontsize = 13, color = 'black')\n",
+    "plt.text(x = 0, y = -10, s = \"Inference Time: \", fontsize = 13, color = 'black')\n",
+    "plt.text(x = 3, y = 14, s = \"Model Input\", fontsize = 12, color = 'black')\n",
+    "plt.text(x = 6, y = 18, s = \"(28 x 28)\", fontsize = 12, color = 'black')\n",
+    "plt.imshow(np.ones((28,28)), cmap=plt.cm.Greys)    \n",
+    "\n",
+    "\n",
+    "for i in np.arange(test_data_size):\n",
+    "    \n",
+    "    input_data = json.dumps({'data': test_inputs[i].tolist()})\n",
+    "    \n",
+    "    # predict using the deployed model\n",
+    "    r = json.loads(aci_service.run(input_data))\n",
+    "    \n",
+    "    if \"error\" in r:\n",
+    "        print(r['error'])\n",
+    "        break\n",
+    "        \n",
+    "    result = r['result'][0]\n",
+    "    time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n",
+    "    \n",
+    "    ground_truth = int(np.argmax(test_outputs[i]))\n",
+    "    \n",
+    "    # compare actual value vs. the predicted values:\n",
+    "    plt.subplot(1, 8, i+2)\n",
+    "    plt.axhline('')\n",
+    "    plt.axvline('')\n",
+    "\n",
+    "    # use different color for misclassified sample\n",
+    "    font_color = 'red' if ground_truth != result else 'black'\n",
+    "    clr_map = plt.cm.gray if ground_truth != result else plt.cm.Greys\n",
+    "\n",
+    "    # ground truth labels are in blue\n",
+    "    plt.text(x = 10, y = -30, s = ground_truth, fontsize = 18, color = 'blue')\n",
+    "    \n",
+    "    # predictions are in black if correct, red if incorrect\n",
+    "    plt.text(x = 10, y = -20, s = result, fontsize = 18, color = font_color)\n",
+    "    plt.text(x = 5, y = -10, s = str(time_ms) + ' ms', fontsize = 14, color = font_color)\n",
+    "\n",
+    "    \n",
+    "    plt.imshow(test_inputs[i].reshape(28, 28), cmap = clr_map)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Try classifying your own images!\n",
+    "\n",
+    "Create your own handwritten image and pass it into the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocessing functions take your image and format it so it can be passed\n",
+    "# as input into our ONNX model\n",
+    "\n",
+    "import cv2\n",
+    "\n",
+    "def rgb2gray(rgb):\n",
+    "    \"\"\"Convert the input image into grayscale\"\"\"\n",
+    "    return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])\n",
+    "\n",
+    "def resize_img(img):\n",
+    "    \"\"\"Resize image to MNIST model input dimensions\"\"\"\n",
+    "    img = cv2.resize(img, dsize=(28, 28), interpolation=cv2.INTER_AREA)\n",
+    "    img.resize((1, 1, 28, 28))\n",
+    "    return img\n",
+    "\n",
+    "def preprocess(img):\n",
+    "    \"\"\"Resize input images and convert them to grayscale.\"\"\"\n",
+    "    if img.shape == (28, 28):\n",
+    "        img.resize((1, 1, 28, 28))\n",
+    "        return img\n",
+    "    \n",
+    "    grayscale = rgb2gray(img)\n",
+    "    processed_img = resize_img(grayscale)\n",
+    "    return processed_img"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Replace this string with your own path/test image\n",
+    "# Make sure your image is square and the dimensions are equal (i.e. 100 * 100 pixels or 28 * 28 pixels)\n",
+    "\n",
+    "# Any PNG or JPG image file should work\n",
+    "\n",
+    "your_test_image = \"<path to file>\"\n",
+    "\n",
+    "# e.g. your_test_image = \"C:/Users/vinitra.swamy/Pictures/handwritten_digit.png\"\n",
+    "\n",
+    "import matplotlib.image as mpimg\n",
+    "\n",
+    "if your_test_image != \"<path to file>\":\n",
+    "    img = mpimg.imread(your_test_image)\n",
+    "    plt.subplot(1,3,1)\n",
+    "    plt.imshow(img, cmap = plt.cm.Greys)\n",
+    "    print(\"Old Dimensions: \", img.shape)\n",
+    "    img = preprocess(img)\n",
+    "    print(\"New Dimensions: \", img.shape)\n",
+    "else:\n",
+    "    img = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if img is None:\n",
+    "    print(\"Add the path for your image data.\")\n",
+    "else:\n",
+    "    input_data = json.dumps({'data': img.tolist()})\n",
+    "\n",
+    "    try:\n",
+    "        r = json.loads(aci_service.run(input_data))\n",
+    "        result = r['result'][0]\n",
+    "        time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n",
+    "    except Exception as e:\n",
+    "        print(str(e))\n",
+    "\n",
+    "    plt.figure(figsize = (16, 6))\n",
+    "    plt.subplot(1, 15,1)\n",
+    "    plt.axhline('')\n",
+    "    plt.axvline('')\n",
+    "    plt.text(x = -100, y = -20, s = \"Model prediction: \", fontsize = 14)\n",
+    "    plt.text(x = -100, y = -10, s = \"Inference time: \", fontsize = 14)\n",
+    "    plt.text(x = 0, y = -20, s = str(result), fontsize = 14)\n",
+    "    plt.text(x = 0, y = -10, s = str(time_ms) + \" ms\", fontsize = 14)\n",
+    "    plt.text(x = -100, y = 14, s = \"Input image: \", fontsize = 14)\n",
+    "    plt.imshow(img.reshape(28, 28), cmap = plt.cm.gray)    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Optional: How does our  ONNX MNIST model work? \n",
+    "#### A brief explanation of Convolutional Neural Networks\n",
+    "\n",
+    "A [convolutional neural network](https://en.wikipedia.org/wiki/Convolutional_neural_network) (CNN, or ConvNet) is a type of [feed-forward](https://en.wikipedia.org/wiki/Feedforward_neural_network) artificial neural network made up of neurons that have learnable weights and biases. The CNNs take advantage of the spatial nature of the data. In nature, we perceive different objects by their shapes, size and colors. For example, objects in a natural scene are typically edges, corners/vertices (defined by two of more edges), color patches etc. These primitives are often identified using different detectors (e.g., edge detection, color detector) or combination of detectors interacting to facilitate image interpretation (object classification, region of interest detection, scene description etc.) in real world vision related tasks. These detectors are also known as filters. Convolution is a mathematical operator that takes an image and a filter as input and produces a filtered output (representing say edges, corners, or colors in the input image).  \n",
+    "\n",
+    "Historically, these filters are a set of weights that were often hand crafted or modeled with mathematical functions (e.g., [Gaussian](https://en.wikipedia.org/wiki/Gaussian_filter) / [Laplacian](http://homepages.inf.ed.ac.uk/rbf/HIPR2/log.htm) / [Canny](https://en.wikipedia.org/wiki/Canny_edge_detector) filter).  The filter outputs are mapped through non-linear activation functions mimicking human brain cells called [neurons](https://en.wikipedia.org/wiki/Neuron). Popular deep CNNs or ConvNets (such as [AlexNet](https://en.wikipedia.org/wiki/AlexNet), [VGG](https://arxiv.org/abs/1409.1556), [Inception](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf), [ResNet](https://arxiv.org/pdf/1512.03385v1.pdf)) that are used for various [computer vision](https://en.wikipedia.org/wiki/Computer_vision) tasks have many of these architectural primitives (inspired from biology).  \n",
+    "\n",
+    "### Convolution Layer\n",
+    "\n",
+    "A convolution layer is a set of filters. Each filter is defined by a weight (**W**) matrix, and  bias ($b$).\n",
+    "\n",
+    "![](https://www.cntk.ai/jup/cntk103d_filterset_v2.png)\n",
+    "\n",
+    "These filters are scanned across the image performing the dot product between the weights and corresponding input value ($x$). The bias value is added to the output of the dot product and the resulting sum is optionally mapped through an activation function. This process is illustrated in the following animation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Image(url=\"https://www.cntk.ai/jup/cntk103d_conv2d_final.gif\", width= 200)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model Description\n",
+    "\n",
+    "The MNIST model from the ONNX Model Zoo uses maxpooling to update the weights in its convolutions, summarized by the graphic below. You can see the entire workflow of our pre-trained model in the following image, with our input images and our output probabilities of each of our 10 labels. If you're interested in exploring the logic behind creating a Deep Learning model further, please look at the [training tutorial for our ONNX MNIST Convolutional Neural Network](https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_103D_MNIST_ConvolutionalNeuralNetwork.ipynb). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Max-Pooling for Convolutional Neural Nets\n",
+    "\n",
+    "![](http://www.cntk.ai/jup/c103d_max_pooling.gif)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Pre-Trained Model Architecture\n",
+    "\n",
+    "![](http://www.cntk.ai/jup/conv103d_mnist-conv-mp.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remember to delete your service after you are done using it!\n",
+    "\n",
+    "# aci_service.delete()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "Congratulations!\n",
+    "\n",
+    "In this tutorial, you have:\n",
+    "- familiarized yourself with ONNX Runtime inference and the pretrained models in the ONNX model zoo\n",
+    "- understood a state-of-the-art convolutional neural net image classification model (MNIST in ONNX) and deployed it in Azure ML cloud\n",
+    "- ensured that your deep learning model is working perfectly (in the cloud) on test data, and checked it against some of your own!\n",
+    "\n",
+    "Next steps:\n",
+    "- Check out another interesting application based on a Microsoft Research computer vision paper that lets you set up a [facial emotion recognition model](https://github.com/Azure/MachineLearningNotebooks/tree/master/onnx/onnx-inference-emotion-recognition.ipynb) in the cloud! This tutorial deploys a pre-trained ONNX Computer Vision model in an Azure ML virtual machine.\n",
+    "- Contribute to our [open source ONNX repository on github](http://github.com/onnx/onnx) and/or add to our [ONNX model zoo](http://github.com/onnx/models)"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "viswamy"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  },
+  "msauthor": "vinitra.swamy"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

onnx/onnx-inference-mnist.ipynb

@@ -1,804 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Handwritten Digit Classification (MNIST) using ONNX Runtime on Azure ML\n",
-        "\n",
-        "This example shows how to deploy an image classification neural network using the Modified National Institute of Standards and Technology ([MNIST](http://yann.lecun.com/exdb/mnist/)) dataset and Open Neural Network eXchange format ([ONNX](http://aka.ms/onnxdocarticle)) on the Azure Machine Learning platform. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing number from 0 to 9. This tutorial will show you how to deploy a MNIST model from the [ONNX model zoo](https://github.com/onnx/models), use it to make predictions using ONNX Runtime Inference, and deploy it as a web service in Azure.\n",
-        "\n",
-        "Throughout this tutorial, we will be referring to ONNX, a neural network exchange format used to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools (CNTK, PyTorch, Caffe, MXNet, TensorFlow) and choose the combination that is best for them. ONNX is developed and supported by a community of partners including Microsoft AI, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai) and [open source files](https://github.com/onnx).\n",
-        "\n",
-        "[ONNX Runtime](https://aka.ms/onnxruntime-python) is the runtime engine that enables evaluation of trained machine learning (Traditional ML and Deep Learning) models with high performance and low resource utilization.\n",
-        "\n",
-        "#### Tutorial Objectives:\n",
-        "\n",
-        "- Describe the MNIST dataset and pretrained Convolutional Neural Net ONNX model, stored in the ONNX model zoo.\n",
-        "- Deploy and run the pretrained MNIST ONNX model on an Azure Machine Learning instance\n",
-        "- Predict labels for test set data points in the cloud using ONNX Runtime and Azure ML"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "\n",
-        "### 1. Install Azure ML SDK and create a new workspace\n",
-        "Please follow [Azure ML configuration notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) to set up your environment.\n",
-        "\n",
-        "### 2. Install additional packages needed for this tutorial notebook\n",
-        "You need to install the popular plotting library `matplotlib`, the image manipulation library `opencv`, and the `onnx` library in the conda environment where Azure Maching Learning SDK is installed. \n",
-        "\n",
-        "```sh\n",
-        "(myenv) $ pip install matplotlib onnx opencv-python\n",
-        "```\n",
-        "\n",
-        "**Debugging tip**: Make sure that you run the \"jupyter notebook\" command to launch this notebook after activating your virtual environment. Choose the respective Python kernel for your new virtual environment using the `Kernel > Change Kernel` menu above. If you have completed the steps correctly, the upper right corner of your screen should state `Python [conda env:myenv]` instead of `Python [default]`.\n",
-        "\n",
-        "### 3. Download sample data and pre-trained ONNX model from ONNX Model Zoo.\n",
-        "\n",
-        "In the following lines of code, we download [the trained ONNX MNIST model and corresponding test data](https://github.com/onnx/models/tree/master/mnist) and place them in the same folder as this tutorial notebook. For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# urllib is a built-in Python library to download files from URLs\n",
-        "\n",
-        "# Objective: retrieve the latest version of the ONNX MNIST model files from the\n",
-        "# ONNX Model Zoo and save it in the same folder as this tutorial\n",
-        "\n",
-        "import urllib.request\n",
-        "\n",
-        "onnx_model_url = \"https://www.cntk.ai/OnnxModels/mnist/opset_7/mnist.tar.gz\"\n",
-        "\n",
-        "urllib.request.urlretrieve(onnx_model_url, filename=\"mnist.tar.gz\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# the ! magic command tells our jupyter notebook kernel to run the following line of \n",
-        "# code from the command line instead of the notebook kernel\n",
-        "\n",
-        "# We use tar and xvcf to unzip the files we just retrieved from the ONNX model zoo\n",
-        "\n",
-        "!tar xvzf mnist.tar.gz"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Deploy a VM with your ONNX model in the Cloud\n",
-        "\n",
-        "### Load Azure ML workspace\n",
-        "\n",
-        "We begin by instantiating a workspace object from the existing workspace created earlier in the configuration notebook."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Check core SDK version number\n",
-        "import azureml.core\n",
-        "\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Registering your model with Azure ML"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model_dir = \"mnist\" # replace this with the location of your model files\n",
-        "\n",
-        "# leave as is if it's in the same folder as this notebook"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.model import Model\n",
-        "\n",
-        "model = Model.register(workspace = ws,\n",
-        "                       model_path = model_dir + \"/\" + \"model.onnx\",\n",
-        "                       model_name = \"mnist_1\",\n",
-        "                       tags = {\"onnx\": \"demo\"},\n",
-        "                       description = \"MNIST image classification CNN from ONNX Model Zoo\",)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Optional: Displaying your registered models\n",
-        "\n",
-        "This step is not required, so feel free to skip it."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "models = ws.models()\n",
-        "for name, m in models.items():\n",
-        "    print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "nbpresent": {
-          "id": "c3f2f57c-7454-4d3e-b38d-b0946cf066ea"
-        }
-      },
-      "source": [
-        "### ONNX MNIST Model Methodology\n",
-        "\n",
-        "The image classification model we are using is pre-trained using Microsoft's deep learning cognitive toolkit, [CNTK](https://github.com/Microsoft/CNTK), from the [ONNX model zoo](http://github.com/onnx/models). The model zoo has many other models that can be deployed on cloud providers like AzureML without any additional training. To ensure that our cloud deployed model works, we use testing data from the famous MNIST data set, provided as part of the [trained MNIST model](https://github.com/onnx/models/tree/master/mnist) in the ONNX model zoo.\n",
-        "\n",
-        "***Input: Handwritten Images from MNIST Dataset***\n",
-        "\n",
-        "***Task: Classify each MNIST image into an appropriate digit***\n",
-        "\n",
-        "***Output: Digit prediction for input image***\n",
-        "\n",
-        "Run the cell below to look at some of the sample images from the MNIST dataset that we used to train this ONNX model. Remember, once the application is deployed in Azure ML, you can use your own images as input for the model to classify!"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# for images and plots in this notebook\n",
-        "import matplotlib.pyplot as plt  \n",
-        "from IPython.display import Image\n",
-        "\n",
-        "# display images inline\n",
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "Image(url=\"http://3.bp.blogspot.com/_UpN7DfJA0j4/TJtUBWPk0SI/AAAAAAAAABY/oWPMtmqJn3k/s1600/mnist_originals.png\", width=200, height=200)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Specify our Score and Environment Files"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We are now going to deploy our ONNX Model on AML with inference in ONNX Runtime. We begin by writing a score.py file, which will help us run the model in our Azure ML virtual machine (VM), and then specify our environment by writing a yml file. You will also notice that we import the onnxruntime library to do runtime inference on our ONNX models (passing in input and evaluating out model's predicted output). More information on the API and commands can be found in the [ONNX Runtime documentation](https://aka.ms/onnxruntime).\n",
-        "\n",
-        "### Write Score File\n",
-        "\n",
-        "A score file is what tells our Azure cloud service what to do. After initializing our model using azureml.core.model, we start an ONNX Runtime inference session to evaluate the data passed in on our function calls."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile score.py\n",
-        "import json\n",
-        "import numpy as np\n",
-        "import onnxruntime\n",
-        "import sys\n",
-        "import os\n",
-        "from azureml.core.model import Model\n",
-        "import time\n",
-        "\n",
-        "\n",
-        "def init():\n",
-        "    global session, input_name, output_name\n",
-        "    model = Model.get_model_path(model_name = 'mnist_1')\n",
-        "    session = onnxruntime.InferenceSession(model, None)\n",
-        "    input_name = session.get_inputs()[0].name\n",
-        "    output_name = session.get_outputs()[0].name \n",
-        "    \n",
-        "def run(input_data):\n",
-        "    '''Purpose: evaluate test input in Azure Cloud using onnxruntime.\n",
-        "        We will call the run function later from our Jupyter Notebook \n",
-        "        so our azure service can evaluate our model input in the cloud. '''\n",
-        "\n",
-        "    try:\n",
-        "        # load in our data, convert to readable format\n",
-        "        data = np.array(json.loads(input_data)['data']).astype('float32')\n",
-        "\n",
-        "        start = time.time()\n",
-        "        r = session.run([output_name], {input_name: data})[0]\n",
-        "        end = time.time()\n",
-        "        result = choose_class(r[0])\n",
-        "        result_dict = {\"result\": [result],\n",
-        "                      \"time_in_sec\": [end - start]}\n",
-        "    except Exception as e:\n",
-        "        result_dict = {\"error\": str(e)}\n",
-        "    \n",
-        "    return json.dumps(result_dict)\n",
-        "\n",
-        "def choose_class(result_prob):\n",
-        "    \"\"\"We use argmax to determine the right label to choose from our output\"\"\"\n",
-        "    return int(np.argmax(result_prob, axis=0))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Write Environment File\n",
-        "\n",
-        "This step creates a YAML environment file that specifies which dependencies we would like to see in our Linux Virtual Machine."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies \n",
-        "\n",
-        "myenv = CondaDependencies()\n",
-        "myenv.add_pip_package(\"numpy\")\n",
-        "myenv.add_pip_package(\"azureml-core\")\n",
-        "myenv.add_pip_package(\"onnxruntime\")\n",
-        "\n",
-        "\n",
-        "with open(\"myenv.yml\",\"w\") as f:\n",
-        "    f.write(myenv.serialize_to_string())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create the Container Image\n",
-        "This step will likely take a few minutes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.image import ContainerImage\n",
-        "help(ContainerImage.image_configuration)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.image import ContainerImage\n",
-        "\n",
-        "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
-        "                                                  runtime = \"python\",\n",
-        "                                                  conda_file = \"myenv.yml\",\n",
-        "                                                  description = \"MNIST ONNX Runtime container\",\n",
-        "                                                  tags = {\"demo\": \"onnx\"}) \n",
-        "\n",
-        "\n",
-        "image = ContainerImage.create(name = \"onnxtest\",\n",
-        "                              # this is the model object\n",
-        "                              models = [model],\n",
-        "                              image_config = image_config,\n",
-        "                              workspace = ws)\n",
-        "\n",
-        "image.wait_for_creation(show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In case you need to debug your code, the next line of code accesses the log file."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(image.image_build_log_uri)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We're all done specifying what we want our virtual machine to do. Let's configure and deploy our container image.\n",
-        "\n",
-        "### Deploy the container image"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import AciWebservice\n",
-        "\n",
-        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
-        "                                               memory_gb = 1, \n",
-        "                                               tags = {'demo': 'onnx'}, \n",
-        "                                               description = 'ONNX for mnist model')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The following cell will likely take a few minutes to run as well."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import Webservice\n",
-        "\n",
-        "aci_service_name = 'onnx-demo-mnist20'\n",
-        "print(\"Service\", aci_service_name)\n",
-        "\n",
-        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
-        "                                           image = image,\n",
-        "                                           name = aci_service_name,\n",
-        "                                           workspace = ws)\n",
-        "\n",
-        "aci_service.wait_for_deployment(True)\n",
-        "print(aci_service.state)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "if aci_service.state != 'Healthy':\n",
-        "    # run this command for debugging.\n",
-        "    print(aci_service.get_logs())\n",
-        "\n",
-        "    # If your deployment fails, make sure to delete your aci_service or rename your service before trying again!\n",
-        "    # aci_service.delete()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Success!\n",
-        "\n",
-        "If you've made it this far, you've deployed a working VM with a handwritten digit classifier running in the cloud using Azure ML. Congratulations!\n",
-        "\n",
-        "Let's see how well our model deals with our test images."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Testing and Evaluation\n",
-        "\n",
-        "### Load Test Data\n",
-        "\n",
-        "These are already in your directory from your ONNX model download (from the model zoo).\n",
-        "\n",
-        "Notice that our Model Zoo files have a .pb extension. This is because they are [protobuf files (Protocol Buffers)](https://developers.google.com/protocol-buffers/docs/pythontutorial), so we need to read in our data through our ONNX TensorProto reader into a format we can work with, like numerical arrays."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# to manipulate our arrays\n",
-        "import numpy as np \n",
-        "\n",
-        "# read in test data protobuf files included with the model\n",
-        "import onnx\n",
-        "from onnx import numpy_helper\n",
-        "\n",
-        "# to use parsers to read in our model/data\n",
-        "import json\n",
-        "import os\n",
-        "\n",
-        "test_inputs = []\n",
-        "test_outputs = []\n",
-        "\n",
-        "# read in 3 testing images from .pb files\n",
-        "test_data_size = 3\n",
-        "\n",
-        "for i in np.arange(test_data_size):\n",
-        "    input_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(i), 'input_0.pb')\n",
-        "    output_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(i), 'output_0.pb')\n",
-        "    \n",
-        "    # convert protobuf tensors to np arrays using the TensorProto reader from ONNX\n",
-        "    tensor = onnx.TensorProto()\n",
-        "    with open(input_test_data, 'rb') as f:\n",
-        "        tensor.ParseFromString(f.read())\n",
-        "    \n",
-        "    input_data = numpy_helper.to_array(tensor)\n",
-        "    test_inputs.append(input_data)\n",
-        "    \n",
-        "    with open(output_test_data, 'rb') as f:\n",
-        "        tensor.ParseFromString(f.read())\n",
-        "    \n",
-        "    output_data = numpy_helper.to_array(tensor)\n",
-        "    test_outputs.append(output_data)\n",
-        "    \n",
-        "if len(test_inputs) == test_data_size:\n",
-        "    print('Test data loaded successfully.')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "nbpresent": {
-          "id": "c3f2f57c-7454-4d3e-b38d-b0946cf066ea"
-        }
-      },
-      "source": [
-        "### Show some sample images\n",
-        "We use `matplotlib` to plot 3 test images from the dataset."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "nbpresent": {
-          "id": "396d478b-34aa-4afa-9898-cdce8222a516"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "plt.figure(figsize = (16, 6))\n",
-        "for test_image in np.arange(3):\n",
-        "    plt.subplot(1, 15, test_image+1)\n",
-        "    plt.axhline('')\n",
-        "    plt.axvline('')\n",
-        "    plt.imshow(test_inputs[test_image].reshape(28, 28), cmap = plt.cm.Greys)\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Run evaluation / prediction"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "plt.figure(figsize = (16, 6), frameon=False)\n",
-        "plt.subplot(1, 8, 1)\n",
-        "\n",
-        "plt.text(x = 0, y = -30, s = \"True Label: \", fontsize = 13, color = 'black')\n",
-        "plt.text(x = 0, y = -20, s = \"Result: \", fontsize = 13, color = 'black')\n",
-        "plt.text(x = 0, y = -10, s = \"Inference Time: \", fontsize = 13, color = 'black')\n",
-        "plt.text(x = 3, y = 14, s = \"Model Input\", fontsize = 12, color = 'black')\n",
-        "plt.text(x = 6, y = 18, s = \"(28 x 28)\", fontsize = 12, color = 'black')\n",
-        "plt.imshow(np.ones((28,28)), cmap=plt.cm.Greys)    \n",
-        "\n",
-        "\n",
-        "for i in np.arange(test_data_size):\n",
-        "    \n",
-        "    input_data = json.dumps({'data': test_inputs[i].tolist()})\n",
-        "    \n",
-        "    # predict using the deployed model\n",
-        "    r = json.loads(aci_service.run(input_data))\n",
-        "    \n",
-        "    if \"error\" in r:\n",
-        "        print(r['error'])\n",
-        "        break\n",
-        "        \n",
-        "    result = r['result'][0]\n",
-        "    time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n",
-        "    \n",
-        "    ground_truth = int(np.argmax(test_outputs[i]))\n",
-        "    \n",
-        "    # compare actual value vs. the predicted values:\n",
-        "    plt.subplot(1, 8, i+2)\n",
-        "    plt.axhline('')\n",
-        "    plt.axvline('')\n",
-        "\n",
-        "    # use different color for misclassified sample\n",
-        "    font_color = 'red' if ground_truth != result else 'black'\n",
-        "    clr_map = plt.cm.gray if ground_truth != result else plt.cm.Greys\n",
-        "\n",
-        "    # ground truth labels are in blue\n",
-        "    plt.text(x = 10, y = -30, s = ground_truth, fontsize = 18, color = 'blue')\n",
-        "    \n",
-        "    # predictions are in black if correct, red if incorrect\n",
-        "    plt.text(x = 10, y = -20, s = result, fontsize = 18, color = font_color)\n",
-        "    plt.text(x = 5, y = -10, s = str(time_ms) + ' ms', fontsize = 14, color = font_color)\n",
-        "\n",
-        "    \n",
-        "    plt.imshow(test_inputs[i].reshape(28, 28), cmap = clr_map)\n",
-        "\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Try classifying your own images!\n",
-        "\n",
-        "Create your own handwritten image and pass it into the model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Preprocessing functions take your image and format it so it can be passed\n",
-        "# as input into our ONNX model\n",
-        "\n",
-        "import cv2\n",
-        "\n",
-        "def rgb2gray(rgb):\n",
-        "    \"\"\"Convert the input image into grayscale\"\"\"\n",
-        "    return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])\n",
-        "\n",
-        "def resize_img(img):\n",
-        "    \"\"\"Resize image to MNIST model input dimensions\"\"\"\n",
-        "    img = cv2.resize(img, dsize=(28, 28), interpolation=cv2.INTER_AREA)\n",
-        "    img.resize((1, 1, 28, 28))\n",
-        "    return img\n",
-        "\n",
-        "def preprocess(img):\n",
-        "    \"\"\"Resize input images and convert them to grayscale.\"\"\"\n",
-        "    if img.shape == (28, 28):\n",
-        "        img.resize((1, 1, 28, 28))\n",
-        "        return img\n",
-        "    \n",
-        "    grayscale = rgb2gray(img)\n",
-        "    processed_img = resize_img(grayscale)\n",
-        "    return processed_img"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Replace this string with your own path/test image\n",
-        "# Make sure your image is square and the dimensions are equal (i.e. 100 * 100 pixels or 28 * 28 pixels)\n",
-        "\n",
-        "# Any PNG or JPG image file should work\n",
-        "\n",
-        "# e.g. your_test_image = \"C:/Users/vinitra.swamy/Pictures/handwritten_digit.png\"\n",
-        "\n",
-        "import matplotlib.image as mpimg\n",
-        "\n",
-        "if your_test_image != \"<path to file>\":\n",
-        "    img = mpimg.imread(your_test_image)\n",
-        "    plt.subplot(1,3,1)\n",
-        "    plt.imshow(img, cmap = plt.cm.Greys)\n",
-        "    print(\"Old Dimensions: \", img.shape)\n",
-        "    img = preprocess(img)\n",
-        "    print(\"New Dimensions: \", img.shape)\n",
-        "else:\n",
-        "    img = None"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "if img is None:\n",
-        "    print(\"Add the path for your image data.\")\n",
-        "else:\n",
-        "    input_data = json.dumps({'data': img.tolist()})\n",
-        "\n",
-        "    try:\n",
-        "        r = json.loads(aci_service.run(input_data))\n",
-        "        result = r['result'][0]\n",
-        "        time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n",
-        "    except Exception as e:\n",
-        "        print(str(e))\n",
-        "\n",
-        "    plt.figure(figsize = (16, 6))\n",
-        "    plt.subplot(1, 15,1)\n",
-        "    plt.axhline('')\n",
-        "    plt.axvline('')\n",
-        "    plt.text(x = -100, y = -20, s = \"Model prediction: \", fontsize = 14)\n",
-        "    plt.text(x = -100, y = -10, s = \"Inference time: \", fontsize = 14)\n",
-        "    plt.text(x = 0, y = -20, s = str(result), fontsize = 14)\n",
-        "    plt.text(x = 0, y = -10, s = str(time_ms) + \" ms\", fontsize = 14)\n",
-        "    plt.text(x = -100, y = 14, s = \"Input image: \", fontsize = 14)\n",
-        "    plt.imshow(img.reshape(28, 28), cmap = plt.cm.gray)    "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Optional: How does our  ONNX MNIST model work? \n",
-        "#### A brief explanation of Convolutional Neural Networks\n",
-        "\n",
-        "A [convolutional neural network](https://en.wikipedia.org/wiki/Convolutional_neural_network) (CNN, or ConvNet) is a type of [feed-forward](https://en.wikipedia.org/wiki/Feedforward_neural_network) artificial neural network made up of neurons that have learnable weights and biases. The CNNs take advantage of the spatial nature of the data. In nature, we perceive different objects by their shapes, size and colors. For example, objects in a natural scene are typically edges, corners/vertices (defined by two of more edges), color patches etc. These primitives are often identified using different detectors (e.g., edge detection, color detector) or combination of detectors interacting to facilitate image interpretation (object classification, region of interest detection, scene description etc.) in real world vision related tasks. These detectors are also known as filters. Convolution is a mathematical operator that takes an image and a filter as input and produces a filtered output (representing say edges, corners, or colors in the input image).  \n",
-        "\n",
-        "Historically, these filters are a set of weights that were often hand crafted or modeled with mathematical functions (e.g., [Gaussian](https://en.wikipedia.org/wiki/Gaussian_filter) / [Laplacian](http://homepages.inf.ed.ac.uk/rbf/HIPR2/log.htm) / [Canny](https://en.wikipedia.org/wiki/Canny_edge_detector) filter).  The filter outputs are mapped through non-linear activation functions mimicking human brain cells called [neurons](https://en.wikipedia.org/wiki/Neuron). Popular deep CNNs or ConvNets (such as [AlexNet](https://en.wikipedia.org/wiki/AlexNet), [VGG](https://arxiv.org/abs/1409.1556), [Inception](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf), [ResNet](https://arxiv.org/pdf/1512.03385v1.pdf)) that are used for various [computer vision](https://en.wikipedia.org/wiki/Computer_vision) tasks have many of these architectural primitives (inspired from biology).  \n",
-        "\n",
-        "### Convolution Layer\n",
-        "\n",
-        "A convolution layer is a set of filters. Each filter is defined by a weight (**W**) matrix, and  bias ($b$).\n",
-        "\n",
-        "![](https://www.cntk.ai/jup/cntk103d_filterset_v2.png)\n",
-        "\n",
-        "These filters are scanned across the image performing the dot product between the weights and corresponding input value ($x$). The bias value is added to the output of the dot product and the resulting sum is optionally mapped through an activation function. This process is illustrated in the following animation."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "Image(url=\"https://www.cntk.ai/jup/cntk103d_conv2d_final.gif\", width= 200)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Model Description\n",
-        "\n",
-        "The MNIST model from the ONNX Model Zoo uses maxpooling to update the weights in its convolutions, summarized by the graphic below. You can see the entire workflow of our pre-trained model in the following image, with our input images and our output probabilities of each of our 10 labels. If you're interested in exploring the logic behind creating a Deep Learning model further, please look at the [training tutorial for our ONNX MNIST Convolutional Neural Network](https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_103D_MNIST_ConvolutionalNeuralNetwork.ipynb). "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Max-Pooling for Convolutional Neural Nets\n",
-        "\n",
-        "![](http://www.cntk.ai/jup/c103d_max_pooling.gif)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Pre-Trained Model Architecture\n",
-        "\n",
-        "![](http://www.cntk.ai/jup/conv103d_mnist-conv-mp.png)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# remember to delete your service after you are done using it!\n",
-        "\n",
-        "aci_service.delete()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Conclusion\n",
-        "\n",
-        "Congratulations!\n",
-        "\n",
-        "In this tutorial, you have:\n",
-        "- familiarized yourself with ONNX Runtime inference and the pretrained models in the ONNX model zoo\n",
-        "- understood a state-of-the-art convolutional neural net image classification model (MNIST in ONNX) and deployed it in Azure ML cloud\n",
-        "- ensured that your deep learning model is working perfectly (in the cloud) on test data, and checked it against some of your own!\n",
-        "\n",
-        "Next steps:\n",
-        "- Check out another interesting application based on a Microsoft Research computer vision paper that lets you set up a [facial emotion recognition model](https://github.com/Azure/MachineLearningNotebooks/tree/master/onnx/onnx-inference-emotion-recognition.ipynb) in the cloud! This tutorial deploys a pre-trained ONNX Computer Vision model in an Azure ML virtual machine.\n",
-        "- Contribute to our [open source ONNX repository on github](http://github.com/onnx/onnx) and/or add to our [ONNX model zoo](http://github.com/onnx/models)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "viswamy"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    },
-    "msauthor": "vinitra.swamy"
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb

@@ -1,409 +1,419 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# ResNet50 Image Classification using ONNX and AzureML\n",
-        "\n",
-        "This example shows how to deploy the ResNet50 ONNX model as a web service using Azure Machine Learning services and the ONNX Runtime.\n",
-        "\n",
-        "## What is ONNX\n",
-        "ONNX is an open format for representing machine learning and deep learning models. ONNX enables open and interoperable AI by enabling data scientists and developers to use the tools of their choice without worrying about lock-in and flexibility to deploy to a variety of platforms. ONNX is developed and supported by a community of partners including Microsoft, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai).\n",
-        "\n",
-        "## ResNet50 Details\n",
-        "ResNet classifies the major object in an input image into a set of 1000 pre-defined classes. For more information about the ResNet50 model and how it was created can be found on the [ONNX Model Zoo github](https://github.com/onnx/models/tree/master/models/image_classification/resnet). "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "\n",
-        "To make the best use of your time, make sure you have done the following:\n",
-        "\n",
-        "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
-        "* Go through the [00.configuration.ipynb](../00.configuration.ipynb) notebook to:\n",
-        "    * install the AML SDK\n",
-        "    * create a workspace and its configuration file (config.json)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Check core SDK version number\n",
-        "import azureml.core\n",
-        "\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Download pre-trained ONNX model from ONNX Model Zoo.\n",
-        "\n",
-        "Download the [ResNet50v2 model and test data](https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.tar.gz) and place it in the same folder as this tutorial notebook. You can unzip the file through the following line of code.\n",
-        "\n",
-        "```sh\n",
-        "(myenv) $ tar xvzf resnet50v2.tar.gz\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Deploying as a web service with Azure ML"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Load your Azure ML workspace\n",
-        "\n",
-        "We begin by instantiating a workspace object from the existing workspace created earlier in the configuration notebook."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.location, ws.resource_group, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Register your model with Azure ML\n",
-        "\n",
-        "Now we upload the model and register it in the workspace."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.model import Model\n",
-        "\n",
-        "model = Model.register(model_path = \"resnet50v2/resnet50v2.onnx\",\n",
-        "                       model_name = \"resnet50v2\",\n",
-        "                       tags = {\"onnx\": \"demo\"},\n",
-        "                       description = \"ResNet50v2 from ONNX Model Zoo\",\n",
-        "                       workspace = ws)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Displaying your registered models\n",
-        "\n",
-        "You can optionally list out all the models that you have registered in this workspace."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "models = ws.models()\n",
-        "for m in models:\n",
-        "    print(\"Name:\", m.name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Write scoring file\n",
-        "\n",
-        "We are now going to deploy our ONNX model on Azure ML using the ONNX Runtime. We begin by writing a score.py file that will be invoked by the web service call. The `init()` function is called once when the container is started so we load the model using the ONNX Runtime into a global session object."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile score.py\n",
-        "import json\n",
-        "import time\n",
-        "import sys\n",
-        "import os\n",
-        "from azureml.core.model import Model\n",
-        "import numpy as np    # we're going to use numpy to process input and output data\n",
-        "import onnxruntime    # to inference ONNX models, we use the ONNX Runtime\n",
-        "\n",
-        "def softmax(x):\n",
-        "    x = x.reshape(-1)\n",
-        "    e_x = np.exp(x - np.max(x))\n",
-        "    return e_x / e_x.sum(axis=0)\n",
-        "\n",
-        "def init():\n",
-        "    global session\n",
-        "    model = Model.get_model_path(model_name = 'resnet50v2')\n",
-        "    session = onnxruntime.InferenceSession(model, None)\n",
-        "\n",
-        "def preprocess(input_data_json):\n",
-        "    # convert the JSON data into the tensor input\n",
-        "    img_data = np.array(json.loads(input_data_json)['data']).astype('float32')\n",
-        "    \n",
-        "    #normalize\n",
-        "    mean_vec = np.array([0.485, 0.456, 0.406])\n",
-        "    stddev_vec = np.array([0.229, 0.224, 0.225])\n",
-        "    norm_img_data = np.zeros(img_data.shape).astype('float32')\n",
-        "    for i in range(img_data.shape[0]):\n",
-        "        norm_img_data[i,:,:] = (img_data[i,:,:]/255 - mean_vec[i]) / stddev_vec[i]\n",
-        "\n",
-        "    return norm_img_data\n",
-        "\n",
-        "def postprocess(result):\n",
-        "    return softmax(np.array(result)).tolist()\n",
-        "\n",
-        "def run(input_data_json):\n",
-        "    try:\n",
-        "        start = time.time()\n",
-        "        # load in our data which is expected as NCHW 224x224 image\n",
-        "        input_data = preprocess(input_data_json)\n",
-        "        input_name = session.get_inputs()[0].name  # get the id of the first input of the model   \n",
-        "        result = session.run([], {input_name: input_data})\n",
-        "        end = time.time()     # stop timer\n",
-        "        return {\"result\": postprocess(result),\n",
-        "                \"time\": end - start}\n",
-        "    except Exception as e:\n",
-        "        result = str(e)\n",
-        "        return {\"error\": result}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create container image"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "First we create a YAML file that specifies which dependencies we would like to see in our container."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies \n",
-        "\n",
-        "myenv = CondaDependencies.create(pip_packages=[\"numpy\",\"onnxruntime\"])\n",
-        "\n",
-        "with open(\"myenv.yml\",\"w\") as f:\n",
-        "    f.write(myenv.serialize_to_string())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Then we have Azure ML create the container. This step will likely take a few minutes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.image import ContainerImage\n",
-        "\n",
-        "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
-        "                                                  runtime = \"python\",\n",
-        "                                                  conda_file = \"myenv.yml\",\n",
-        "                                                  description = \"ONNX ResNet50 Demo\",\n",
-        "                                                  tags = {\"demo\": \"onnx\"}\n",
-        "                                                 )\n",
-        "\n",
-        "\n",
-        "image = ContainerImage.create(name = \"onnxresnet50v2\",\n",
-        "                              models = [model],\n",
-        "                              image_config = image_config,\n",
-        "                              workspace = ws)\n",
-        "\n",
-        "image.wait_for_creation(show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In case you need to debug your code, the next line of code accesses the log file."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(image.image_build_log_uri)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We're all set! Let's get our model chugging.\n",
-        "\n",
-        "### Deploy the container image"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import AciWebservice\n",
-        "\n",
-        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
-        "                                               memory_gb = 1, \n",
-        "                                               tags = {'demo': 'onnx'}, \n",
-        "                                               description = 'web service for ResNet50 ONNX model')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The following cell will likely take a few minutes to run as well."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import Webservice\n",
-        "from random import randint\n",
-        "\n",
-        "aci_service_name = 'onnx-demo-resnet50'+str(randint(0,100))\n",
-        "print(\"Service\", aci_service_name)\n",
-        "\n",
-        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
-        "                                           image = image,\n",
-        "                                           name = aci_service_name,\n",
-        "                                           workspace = ws)\n",
-        "\n",
-        "aci_service.wait_for_deployment(True)\n",
-        "print(aci_service.state)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In case the deployment fails, you can check the logs. Make sure to delete your aci_service before trying again."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "if aci_service.state != 'Healthy':\n",
-        "    # run this command for debugging.\n",
-        "    print(aci_service.get_logs())\n",
-        "    aci_service.delete()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Success!\n",
-        "\n",
-        "If you've made it this far, you've deployed a working web service that does image classification using an ONNX model. You can get the URL for the webservice with the code below."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(aci_service.scoring_uri)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "When you are eventually done using the web service, remember to delete it."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#aci_service.delete()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "onnx"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.5.6"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ResNet50 Image Classification using ONNX and AzureML\n",
+    "\n",
+    "This example shows how to deploy the ResNet50 ONNX model as a web service using Azure Machine Learning services and the ONNX Runtime.\n",
+    "\n",
+    "## What is ONNX\n",
+    "ONNX is an open format for representing machine learning and deep learning models. ONNX enables open and interoperable AI by enabling data scientists and developers to use the tools of their choice without worrying about lock-in and flexibility to deploy to a variety of platforms. ONNX is developed and supported by a community of partners including Microsoft, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai).\n",
+    "\n",
+    "## ResNet50 Details\n",
+    "ResNet classifies the major object in an input image into a set of 1000 pre-defined classes. For more information about the ResNet50 model and how it was created can be found on the [ONNX Model Zoo github](https://github.com/onnx/models/tree/master/models/image_classification/resnet). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "\n",
+    "To make the best use of your time, make sure you have done the following:\n",
+    "\n",
+    "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
+    "* Go through the [00.configuration.ipynb](../00.configuration.ipynb) notebook to:\n",
+    "    * install the AML SDK\n",
+    "    * create a workspace and its configuration file (config.json)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check core SDK version number\n",
+    "import azureml.core\n",
+    "\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Download pre-trained ONNX model from ONNX Model Zoo.\n",
+    "\n",
+    "Download the [ResNet50v2 model and test data](https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.tar.gz) and extract it in the same folder as this tutorial notebook.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import urllib.request\n",
+    "\n",
+    "onnx_model_url = \"https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.tar.gz\"\n",
+    "urllib.request.urlretrieve(onnx_model_url, filename=\"resnet50v2.tar.gz\")\n",
+    "\n",
+    "!tar xvzf resnet50v2.tar.gz"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deploying as a web service with Azure ML"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load your Azure ML workspace\n",
+    "\n",
+    "We begin by instantiating a workspace object from the existing workspace created earlier in the configuration notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.location, ws.resource_group, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Register your model with Azure ML\n",
+    "\n",
+    "Now we upload the model and register it in the workspace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.model import Model\n",
+    "\n",
+    "model = Model.register(model_path = \"resnet50v2/resnet50v2.onnx\",\n",
+    "                       model_name = \"resnet50v2\",\n",
+    "                       tags = {\"onnx\": \"demo\"},\n",
+    "                       description = \"ResNet50v2 from ONNX Model Zoo\",\n",
+    "                       workspace = ws)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Displaying your registered models\n",
+    "\n",
+    "You can optionally list out all the models that you have registered in this workspace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models = ws.models\n",
+    "for name, m in models.items():\n",
+    "    print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Write scoring file\n",
+    "\n",
+    "We are now going to deploy our ONNX model on Azure ML using the ONNX Runtime. We begin by writing a score.py file that will be invoked by the web service call. The `init()` function is called once when the container is started so we load the model using the ONNX Runtime into a global session object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile score.py\n",
+    "import json\n",
+    "import time\n",
+    "import sys\n",
+    "import os\n",
+    "from azureml.core.model import Model\n",
+    "import numpy as np    # we're going to use numpy to process input and output data\n",
+    "import onnxruntime    # to inference ONNX models, we use the ONNX Runtime\n",
+    "\n",
+    "def softmax(x):\n",
+    "    x = x.reshape(-1)\n",
+    "    e_x = np.exp(x - np.max(x))\n",
+    "    return e_x / e_x.sum(axis=0)\n",
+    "\n",
+    "def init():\n",
+    "    global session\n",
+    "    model = Model.get_model_path(model_name = 'resnet50v2')\n",
+    "    session = onnxruntime.InferenceSession(model, None)\n",
+    "\n",
+    "def preprocess(input_data_json):\n",
+    "    # convert the JSON data into the tensor input\n",
+    "    img_data = np.array(json.loads(input_data_json)['data']).astype('float32')\n",
+    "    \n",
+    "    #normalize\n",
+    "    mean_vec = np.array([0.485, 0.456, 0.406])\n",
+    "    stddev_vec = np.array([0.229, 0.224, 0.225])\n",
+    "    norm_img_data = np.zeros(img_data.shape).astype('float32')\n",
+    "    for i in range(img_data.shape[0]):\n",
+    "        norm_img_data[i,:,:] = (img_data[i,:,:]/255 - mean_vec[i]) / stddev_vec[i]\n",
+    "\n",
+    "    return norm_img_data\n",
+    "\n",
+    "def postprocess(result):\n",
+    "    return softmax(np.array(result)).tolist()\n",
+    "\n",
+    "def run(input_data_json):\n",
+    "    try:\n",
+    "        start = time.time()\n",
+    "        # load in our data which is expected as NCHW 224x224 image\n",
+    "        input_data = preprocess(input_data_json)\n",
+    "        input_name = session.get_inputs()[0].name  # get the id of the first input of the model   \n",
+    "        result = session.run([], {input_name: input_data})\n",
+    "        end = time.time()     # stop timer\n",
+    "        return {\"result\": postprocess(result),\n",
+    "                \"time\": end - start}\n",
+    "    except Exception as e:\n",
+    "        result = str(e)\n",
+    "        return {\"error\": result}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create container image"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we create a YAML file that specifies which dependencies we would like to see in our container."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.conda_dependencies import CondaDependencies \n",
+    "\n",
+    "myenv = CondaDependencies.create(pip_packages=[\"numpy\",\"onnxruntime\",\"azureml-core\"])\n",
+    "\n",
+    "with open(\"myenv.yml\",\"w\") as f:\n",
+    "    f.write(myenv.serialize_to_string())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we have Azure ML create the container. This step will likely take a few minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.image import ContainerImage\n",
+    "\n",
+    "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
+    "                                                  runtime = \"python\",\n",
+    "                                                  conda_file = \"myenv.yml\",\n",
+    "                                                  description = \"ONNX ResNet50 Demo\",\n",
+    "                                                  tags = {\"demo\": \"onnx\"}\n",
+    "                                                 )\n",
+    "\n",
+    "\n",
+    "image = ContainerImage.create(name = \"onnxresnet50v2\",\n",
+    "                              models = [model],\n",
+    "                              image_config = image_config,\n",
+    "                              workspace = ws)\n",
+    "\n",
+    "image.wait_for_creation(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In case you need to debug your code, the next line of code accesses the log file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(image.image_build_log_uri)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We're all set! Let's get our model chugging.\n",
+    "\n",
+    "### Deploy the container image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.webservice import AciWebservice\n",
+    "\n",
+    "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
+    "                                               memory_gb = 1, \n",
+    "                                               tags = {'demo': 'onnx'}, \n",
+    "                                               description = 'web service for ResNet50 ONNX model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following cell will likely take a few minutes to run as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.webservice import Webservice\n",
+    "from random import randint\n",
+    "\n",
+    "aci_service_name = 'onnx-demo-resnet50'+str(randint(0,100))\n",
+    "print(\"Service\", aci_service_name)\n",
+    "\n",
+    "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
+    "                                           image = image,\n",
+    "                                           name = aci_service_name,\n",
+    "                                           workspace = ws)\n",
+    "\n",
+    "aci_service.wait_for_deployment(True)\n",
+    "print(aci_service.state)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In case the deployment fails, you can check the logs. Make sure to delete your aci_service before trying again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if aci_service.state != 'Healthy':\n",
+    "    # run this command for debugging.\n",
+    "    print(aci_service.get_logs())\n",
+    "    aci_service.delete()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Success!\n",
+    "\n",
+    "If you've made it this far, you've deployed a working web service that does image classification using an ONNX model. You can get the URL for the webservice with the code below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(aci_service.scoring_uri)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you are eventually done using the web service, remember to delete it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#aci_service.delete()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "onnx"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

pipeline/00.pipeline-setup.ipynb

@@ -1,81 +1,81 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Packages"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "!pip install pandas\n",
-        "!pip install requests"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Widgets\n",
-        "Install the following widgets to see the status of each run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "!jupyter nbextension install --py --user azureml.train.widgets\n",
-        "!jupyter nbextension enable --py --user azureml.train.widgets"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "hichando"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.3"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install pandas\n",
+    "!pip install requests"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Widgets\n",
+    "Install the following widgets to see the status of each run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!jupyter nbextension install --py --user azureml.widgets\n",
+    "!jupyter nbextension enable --py --user azureml.widgets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "hichando"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

pipeline/README.md

@@ -0,0 +1,48 @@
+# Azure Machine Learning Pipeline
+
+## Overview
+
+The [Azure Machine Learning Pipelines](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines) enables data scientists to create and manage multiple simple and complex workflows concurrently. A typical pipeline would have multiple tasks to prepare data, train, deploy and evaluate models. Individual steps in the pipeline can make use of diverse compute options (for example: CPU for data preparation and GPU for training) and languages. 
+
+The Python-based Azure Machine Learning Pipeline SDK provides interfaces to work with Azure Machine Learning Pipelines. To get started quickly, the SDK includes imperative constructs for sequencing and parallelization of steps. With the use of declarative data dependencies, optimized execution of the tasks can be achieved. The SDK can be easily used from Jupyter Notebook or any other preferred IDE. The SDK includes a framework of pre-built modules for common tasks such as data transfer and compute provisioning.
+
+Data management and reuse across pipelines and pipeline runs is simplified using named and strictly versioned data sources and named inputs and outputs for processing tasks. Pipelines enable collaboration across teams of data scientists by recording all intermediate tasks and data.
+
+### Why build pipelines?
+
+With pipelines, you can optimize your workflow with simplicity, speed, portability, and reuse. When building pipelines with Azure Machine Learning, you can focus on what you know best — machine learning — rather than infrastructure.
+
+Using distinct steps makes it possible to rerun only the steps you need as you tweak and test your workflow. Once the pipeline is designed, there is often more fine-tuning around the training loop of the pipeline. When you rerun a pipeline, the execution jumps to the steps that need to be rerun, such as an updated training script, and skips what hasn't changed. The same paradigm applies to unchanged scripts and metadata.
+
+With Azure Machine Learning, you can use distinct toolkits and frameworks for each step in your pipeline. Azure coordinates between the various compute targets you use so that your intermediate data can be shared with the downstream compute targets easily.
+
+![MLLifecycle](aml-pipelines-concept.png)
+
+
+### Azure Machine Learning Pipelines Features
+Azure Machine Learning Pipelines optimize for simplicity, speed, and efficiency. The following key concepts make it possible for a data scientist to focus on ML rather than infrastructure.
+
+**Unattended execution**: Schedule a few scripts to run in parallel or in sequence in a reliable and unattended manner. Since data prep and modeling can last days or weeks, you can now focus on other tasks while your pipeline is running.
+
+**Mixed and diverse compute**: Use multiple pipelines that are reliably coordinated across heterogeneous and scalable computes and storages. Individual pipeline steps can be run on different compute targets, such as HDInsight, GPU Data Science VMs, and Databricks, to make efficient use of available compute options.
+
+**Reusability**: Pipelines can be templatized for specific scenarios such as retraining and batch scoring. They can be triggered from external systems via simple REST calls.
+
+**Tracking and versioning**: Instead of manually tracking data and result paths as you iterate, use the pipelines SDK to explicitly name and version your data sources, inputs, and outputs as well as manage scripts and data separately for increased productivity.
+
+### Notebooks 
+
+In this directory, there are two types of notebooks: 
+
+* The first type of notebooks will introduce you to core Azure Machine Learning Pipelines features. The notebooks below belong in this category, and are designed to go in sequence:
+
+1. [aml-pipelines-getting-started.ipynb](aml-pipelines-getting-started.ipynb)
+2. [aml-pipelines-with-data-dependency-steps.ipynb](aml-pipelines-with-data-dependency-steps.ipynb)
+3. [aml-pipelines-publish-and-run-using-rest-endpoint.ipynb](aml-pipelines-publish-and-run-using-rest-endpoint.ipynb)
+4. [aml-pipelines-data-transfer.ipynb](aml-pipelines-data-transfer.ipynb)
+5. [aml-pipelines-use-databricks-as-compute-target.ipynb](aml-pipelines-use-databricks-as-compute-target.ipynb)
+6. [aml-pipelines-use-adla-as-compute-target.ipynb](aml-pipelines-use-adla-as-compute-target.ipynb)
+
+* The second type of notebooks illustrate more sophisticated scenarios, and are independent of each other. These notebooks include:
+  - [pipeline-batch-scoring.ipynb](pipeline-batch-scoring.ipynb)
+  - [pipeline-style-transfer.ipynb](pipeline-style-transfer.ipynb)

pipeline/aml-pipelines-data-transfer.ipynb

@@ -0,0 +1,336 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Azure Machine Learning Pipeline with DataTranferStep\n",
+    "This notebook is used to demonstrate the use of DataTranferStep in Azure Machine Learning Pipeline.\n",
+    "\n",
+    "In certain cases, you will need to transfer data from one data location to another. For example, your data may be in Files storage and you may want to move it to Blob storage. Or, if your data is in an ADLS account and you want to make it available in the Blob storage. The built-in **DataTransferStep** class helps you transfer data in these situations.\n",
+    "\n",
+    "The below example shows how to move data in an ADLS account to Blob storage."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Azure Machine Learning and Pipeline SDK-specific imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import azureml.core\n",
+    "from azureml.core.compute import ComputeTarget, DatabricksCompute, DataFactoryCompute\n",
+    "from azureml.exceptions import ComputeTargetException\n",
+    "from azureml.core import Workspace, Run, Experiment\n",
+    "from azureml.pipeline.core import Pipeline, PipelineData\n",
+    "from azureml.pipeline.steps import AdlaStep\n",
+    "from azureml.core.datastore import Datastore\n",
+    "from azureml.data.data_reference import DataReference\n",
+    "from azureml.data.sql_data_reference import SqlDataReference\n",
+    "from azureml.core import attach_legacy_compute_target\n",
+    "from azureml.data.stored_procedure_parameter import StoredProcedureParameter, StoredProcedureParameterType\n",
+    "from azureml.pipeline.steps import DataTransferStep\n",
+    "\n",
+    "# Check core SDK version number\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize Workspace\n",
+    "\n",
+    "Initialize a workspace object from persisted configuration. Make sure the config file is present at .\\config.json\n",
+    "\n",
+    "If you don't have a config.json file, please go through the configuration Notebook located here:\n",
+    "https://github.com/Azure/MachineLearningNotebooks. \n",
+    "\n",
+    "This sets you up with a working config file that has information on your workspace, subscription id, etc. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "create workspace"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Register Datastores\n",
+    "\n",
+    "In the code cell below, you will need to fill in the appropriate values for the workspace name, datastore name, subscription id, resource group, store name, tenant id, client id, and client secret that are associated with your ADLS datastore. \n",
+    "\n",
+    "For background on registering your data store, consult this article:\n",
+    "\n",
+    "https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-service-to-service-authenticate-using-active-directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# un-comment the following and replace the strings with the \n",
+    "# correct values for your ADLS datastore\n",
+    "\n",
+    "# workspace = \"<my-workspace-name>\"\n",
+    "# datastore_name = \"<my-datastore-name>\" # ADLS datastore name\n",
+    "# subscription_id = \"<my-subscription-id>\" # subscription id of ADLS account\n",
+    "# resource_group = \"<my-resource-group>\" # resource group of ADLS account\n",
+    "# store_name = \"<my-storename>\" # ADLS account name\n",
+    "# tenant_id = \"<my-tenant-id>\" # tenant id of service principal\n",
+    "# client_id = \"<my-client-id>\" # client id of service principal\n",
+    "# client_secret = \"<my-client-secret>\" # the secret of service principal\n",
+    "\n",
+    "\n",
+    "try:\n",
+    "    adls_datastore = Datastore.get(ws, datastore_name)\n",
+    "    print(\"found datastore with name: %s\" % datastore_name)\n",
+    "except:\n",
+    "    adls_datastore = Datastore.register_azure_data_lake(\n",
+    "        workspace=ws,\n",
+    "        datastore_name=datastore_name,\n",
+    "        subscription_id=subscription_id, # subscription id of ADLS account\n",
+    "        resource_group=resource_group, # resource group of ADLS account\n",
+    "        store_name=store_name, # ADLS account name\n",
+    "        tenant_id=tenant_id, # tenant id of service principal\n",
+    "        client_id=client_id, # client id of service principal\n",
+    "        client_secret=client_secret) # the secret of service principal\n",
+    "    print(\"registered datastore with name: %s\" % datastore_name)\n",
+    "\n",
+    "# un-comment the following and replace the strings with the\n",
+    "# correct values for your blob datastore\n",
+    "\n",
+    "# blob_datastore_name = \"<my-blob-datastore-name>\"\n",
+    "# account_name = \"<my-blob-account-name>\"\n",
+    "# container_name = \"<my-blob-container-name>\"\n",
+    "# account_key = \"<my-blob-account-key>\"\n",
+    "\n",
+    "try:\n",
+    "    blob_datastore = Datastore.get(ws, blob_datastore_name)\n",
+    "    print(\"found blob datastore with name: %s\" % blob_datastore_name)\n",
+    "except:\n",
+    "    blob_datastore = Datastore.register_azure_blob_container(\n",
+    "        workspace=ws,\n",
+    "        datastore_name=blob_datastore_name,\n",
+    "        account_name=account_name, # Storage account name\n",
+    "        container_name=container_name, # Name of Azure blob container\n",
+    "        account_key=account_key) # Storage account key\"\n",
+    "    print(\"registered blob datastore with name: %s\" % blob_datastore_name)\n",
+    "\n",
+    "# CLI:\n",
+    "# az ml datastore register-blob -n <datastore-name> -a <account-name> -c <container-name> -k <account-key> [-t <sas-token>]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create DataReferences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adls_datastore = Datastore(workspace=ws, name=\"MyAdlsDatastore\")\n",
+    "\n",
+    "# adls\n",
+    "adls_data_ref = DataReference(\n",
+    "    datastore=adls_datastore,\n",
+    "    data_reference_name=\"adls_test_data\",\n",
+    "    path_on_datastore=\"testdata\")\n",
+    "\n",
+    "blob_datastore = Datastore(workspace=ws, name=\"MyBlobDatastore\")\n",
+    "\n",
+    "# blob data\n",
+    "blob_data_ref = DataReference(\n",
+    "    datastore=blob_datastore,\n",
+    "    data_reference_name=\"blob_test_data\",\n",
+    "    path_on_datastore=\"testdata\")\n",
+    "\n",
+    "print(\"obtained adls, blob data references\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup Data Factory Account"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_factory_name = 'adftest'\n",
+    "\n",
+    "def get_or_create_data_factory(workspace, factory_name):\n",
+    "    try:\n",
+    "        return DataFactoryCompute(workspace, factory_name)\n",
+    "    except ComputeTargetException as e:\n",
+    "        if 'ComputeTargetNotFound' in e.message:\n",
+    "            print('Data factory not found, creating...')\n",
+    "            provisioning_config = DataFactoryCompute.provisioning_configuration()\n",
+    "            data_factory = ComputeTarget.create(workspace, factory_name, provisioning_config)\n",
+    "            data_factory.wait_for_provisioning()\n",
+    "            return data_factory\n",
+    "        else:\n",
+    "            raise e\n",
+    "            \n",
+    "data_factory_compute = get_or_create_data_factory(ws, data_factory_name)\n",
+    "\n",
+    "print(\"setup data factory account complete\")\n",
+    "\n",
+    "# CLI:\n",
+    "# Create: az ml computetarget setup datafactory -n <name>\n",
+    "# BYOC: az ml computetarget attach datafactory -n <name> -i <resource-id>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a DataTransferStep"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**DataTransferStep** is used to transfer data between Azure Blob, Azure Data Lake Store, and Azure SQL database.\n",
+    "\n",
+    "- **name:** Name of module\n",
+    "- **source_data_reference:** Input connection that serves as source of data transfer operation.\n",
+    "- **destination_data_reference:** Input connection that serves as destination of data transfer operation.\n",
+    "- **compute_target:** Azure Data Factory to use for transferring data.\n",
+    "- **allow_reuse:** Whether the step should reuse results of previous DataTransferStep when run with same inputs. Set as False to force data to be transferred again.\n",
+    "\n",
+    "Optional arguments to explicitly specify whether a path corresponds to a file or a directory. These are useful when storage contains both file and directory with the same name or when creating a new destination path.\n",
+    "\n",
+    "- **source_reference_type:** An optional string specifying the type of source_data_reference. Possible values include: 'file', 'directory'. When not specified, we use the type of existing path or directory if it's a new path.\n",
+    "- **destination_reference_type:** An optional string specifying the type of destination_data_reference. Possible values include: 'file', 'directory'. When not specified, we use the type of existing path or directory if it's a new path."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transfer_adls_to_blob = DataTransferStep(\n",
+    "    name=\"transfer_adls_to_blob\",\n",
+    "    source_data_reference=adls_data_ref,\n",
+    "    destination_data_reference=blob_data_ref,\n",
+    "    compute_target=data_factory_compute)\n",
+    "\n",
+    "print(\"data transfer step created\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Build and Submit the Experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipeline = Pipeline(\n",
+    "    description=\"data_transfer_101\",\n",
+    "    workspace=ws,\n",
+    "    steps=[transfer_adls_to_blob])\n",
+    "\n",
+    "pipeline_run = Experiment(ws, \"Data_Transfer_example\").submit(pipeline)\n",
+    "pipeline_run.wait_for_completion()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### View Run Details"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(pipeline_run).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Next: Databricks as a Compute Target\n",
+    "To use Databricks as a compute target from Azure Machine Learning Pipeline, a DatabricksStep is used. This [notebook](./aml-pipelines-use-databricks-as-compute-target.ipynb) demonstrates the use of a DatabricksStep in an Azure Machine Learning Pipeline."
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "diray"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

pipeline/aml-pipelines-getting-started.ipynb

@@ -0,0 +1,631 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Azure Machine Learning Pipelines: Getting Started\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "Read [Azure Machine Learning Pipelines](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines) overview, or the [readme article](./README.md) on Azure Machine Learning Pipelines to get more information.\n",
+    " \n",
+    "\n",
+    "This Notebook shows basic construction of a **pipeline** that runs jobs unattended in different compute clusters. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites and Azure Machine Learning Basics\n",
+    "Make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installing Packages\n",
+    "These packages are used at later stages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install pandas\n",
+    "!pip install requests"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Enabling Widgets\n",
+    "\n",
+    "Install the following jupyter extensions to support Azure Machine Learning widgets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install azureml.widgets\n",
+    "!jupyter nbextension install --py --user azureml.widgets\n",
+    "!jupyter nbextension enable --py --user azureml.widgets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Azure Machine Learning Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import azureml.core\n",
+    "from azureml.core import Workspace, Run, Experiment, Datastore\n",
+    "from azureml.core.compute import AmlCompute\n",
+    "from azureml.core.compute import ComputeTarget\n",
+    "from azureml.core.compute import DataFactoryCompute\n",
+    "from azureml.widgets import RunDetails\n",
+    "\n",
+    "# Check core SDK version number\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pipeline SDK-specific imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.data.data_reference import DataReference\n",
+    "from azureml.pipeline.core import Pipeline, PipelineData, StepSequence\n",
+    "from azureml.pipeline.steps import PythonScriptStep\n",
+    "from azureml.pipeline.steps import DataTransferStep\n",
+    "from azureml.pipeline.core import PublishedPipeline\n",
+    "from azureml.pipeline.core.graph import PipelineParameter\n",
+    "\n",
+    "print(\"Pipeline SDK-specific imports completed\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize Workspace\n",
+    "\n",
+    "Initialize a [workspace](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace(class%29) object from persisted configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "create workspace"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')\n",
+    "\n",
+    "# Default datastore (Azure file storage)\n",
+    "def_file_store = ws.get_default_datastore() \n",
+    "# The above call is equivalent to Datastore(ws, \"workspacefilestore\") or simply Datastore(ws)\n",
+    "print(\"Default datastore's name: {}\".format(def_file_store.name))\n",
+    "\n",
+    "# Blob storage associated with the workspace\n",
+    "# The following call GETS the Azure Blob Store associated with your workspace.\n",
+    "# Note that workspaceblobstore is **the name of this store and CANNOT BE CHANGED and must be used as is** \n",
+    "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
+    "print(\"Blobstore's name: {}\".format(def_blob_store.name))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# project folder\n",
+    "project_folder = '.'\n",
+    "    \n",
+    "print('Sample projects will be created in {}.'.format(project_folder))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Required data and script files for the the tutorial\n",
+    "Sample files required to finish this tutorial are already copied to the project folder specified above. Even though the .py provided in the samples don't have much \"ML work,\" as a data scientist, you will work on this extensively as part of your work. To complete this tutorial, the contents of these files are not very important. The one-line files are for demostration purpose only."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Datastore concepts\n",
+    "A [Datastore](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore(class) is a place where data can be stored that is then made accessible to a compute either by means of mounting or copying the data to the compute target. \n",
+    "\n",
+    "A Datastore can either be backed by an Azure File Storage (default) or by an Azure Blob Storage.\n",
+    "\n",
+    "In this next step, we will upload the training and test set into the workspace's default storage (File storage), and another piece of data to Azure Blob Storage. When to use [Azure Blobs](https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction), [Azure Files](https://docs.microsoft.com/en-us/azure/storage/files/storage-files-introduction), or [Azure Disks](https://docs.microsoft.com/en-us/azure/virtual-machines/linux/managed-disks-overview) is [detailed here](https://docs.microsoft.com/en-us/azure/storage/common/storage-decide-blobs-files-disks).\n",
+    "\n",
+    "**Please take good note of the concept of the datastore.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Upload data to default datastore\n",
+    "Default datastore on workspace is the Azure  File storage. The workspace has a Blob storage associated with it as well. Let's upload a file to each of these storages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get_default_datastore() gets the default Azure File Store associated with your workspace.\n",
+    "# Here we are reusing the def_file_store object we obtained earlier\n",
+    "\n",
+    "# target_path is the directory at the destination\n",
+    "def_file_store.upload_files(['./20news.pkl'], \n",
+    "                                  target_path = '20newsgroups', \n",
+    "                                  overwrite = True, \n",
+    "                                  show_progress = True)\n",
+    "\n",
+    "# Here we are reusing the def_blob_store we created earlier\n",
+    "def_blob_store.upload_files([\"./20news.pkl\"], target_path=\"20newsgroups\", overwrite=True)\n",
+    "\n",
+    "print(\"Upload calls completed\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (Optional) See your files using Azure Portal\n",
+    "Once you successfully uploaded the files, you can browse to them (or upload more files) using [Azure Portal](https://portal.azure.com). At the portal, make sure you have selected **AzureML Nursery** as your subscription (click *Resource Groups* and then select the subscription). Then look for your **Machine Learning Workspace** (it has your *alias* as the name). It has a link to your storage. Click on the storage link. It will take you to a page where you can see [Blobs](https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction), [Files](https://docs.microsoft.com/en-us/azure/storage/files/storage-files-introduction), [Tables](https://docs.microsoft.com/en-us/azure/storage/tables/table-storage-overview), and [Queues](https://docs.microsoft.com/en-us/azure/storage/queues/storage-queues-introduction). We have just uploaded a file to the Blob storage and another one to the File storage. You should be able to see both of these files in their respective locations. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compute Targets\n",
+    "A compute target specifies where to execute your program such as a remote Docker on a VM, or a cluster. A compute target needs to be addressable and accessible by you.\n",
+    "\n",
+    "**You need at least one compute target to send your payload to. We are planning to use Azure Machine Learning Compute exclusively for this tutorial for all steps. However in some cases you may require multiple compute targets as some steps may run in one compute target like Azure Machine Learning Compute, and some other steps in the same pipeline could run in a different compute target.**\n",
+    "\n",
+    "*The example belows show creating/retrieving/attaching to an Azure Machine Learning Compute instance.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### List of Compute Targets on the workspace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cts = ws.compute_targets\n",
+    "for ct in cts:\n",
+    "    print(ct)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Retrieve or create a Azure Machine Learning compute\n",
+    "Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's create a new Azure Machine Learning Compute in the current workspace, if it doesn't already exist. We will then run the training script on this compute target.\n",
+    "\n",
+    "If we could not find the compute with the given name in the previous cell, then we will create a new compute here. We will create an Azure Machine Learning Compute containing **STANDARD_D2_V2 CPU VMs**. This process is broken down into the following steps:\n",
+    "\n",
+    "1. Create the configuration\n",
+    "2. Create the Azure Machine Learning compute\n",
+    "\n",
+    "**This process will take about 3 minutes and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "aml_compute_target = \"aml-compute\"\n",
+    "try:\n",
+    "    aml_compute = AmlCompute(ws, aml_compute_target)\n",
+    "    print(\"found existing compute target.\")\n",
+    "except:\n",
+    "    print(\"creating new compute target\")\n",
+    "    \n",
+    "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n",
+    "                                                                min_nodes = 1, \n",
+    "                                                                max_nodes = 4)    \n",
+    "    aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n",
+    "    aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
+    "    \n",
+    "print(\"Azure Machine Learning Compute attached\")\n",
+    "# For a more detailed view of current Azure Machine Learning Compute status, use the 'status' property    \n",
+    "print(aml_compute.status.serialize())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Wait for this call to finish before proceeding (you will see the asterisk turning to a number).**\n",
+    "\n",
+    "Now that you have created the compute target, let's see what the workspace's compute_targets() function returns. You should now see one entry named 'amlcompute' of type AmlCompute."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Now that we have completed learning the basics of Azure Machine Learning (AML), let's go ahead and start understanding the Pipeline concepts.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a Step in a Pipeline\n",
+    "A Step is a unit of execution. Step typically needs a target of execution (compute target), a script to execute, and may require script arguments and inputs, and can produce outputs. The step also could take a number of other parameters. Azure Machine Learning Pipelines provides the following built-in Steps:\n",
+    "\n",
+    "- [**PythonScriptStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.python_script_step.pythonscriptstep?view=azure-ml-py): Add a step to run a Python script in a Pipeline.\n",
+    "- [**AdlaStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.adla_step.adlastep?view=azure-ml-py): Adds a step to run U-SQL script using Azure Data Lake Analytics.\n",
+    "- [**DataTransferStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.data_transfer_step.datatransferstep?view=azure-ml-py): Transfers data between Azure Blob and Data Lake accounts.\n",
+    "- [**DatabricksStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep?view=azure-ml-py): Adds a DataBricks notebook as a step in a Pipeline.\n",
+    "- [**HyperDriveStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.hyper_drive_step.hyperdrivestep?view=azure-ml-py): Creates a Hyper Drive step for Hyper Parameter Tuning in a Pipeline.\n",
+    "\n",
+    "The following code will create a PythonScriptStep to be executed in the Azure Machine Learning Compute we created above using train.py, one of the files already made available in the project folder.\n",
+    "\n",
+    "A **PythonScriptStep** is a basic, built-in step to run a Python Script on a compute target. It takes a script name and optionally other parameters like arguments for the script, compute target, inputs and outputs. If no compute target is specified, default compute target for the workspace is used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Uses default values for PythonScriptStep construct.\n",
+    "\n",
+    "# Syntax\n",
+    "# PythonScriptStep(\n",
+    "#     script_name, \n",
+    "#     name=None, \n",
+    "#     arguments=None, \n",
+    "#     compute_target=None, \n",
+    "#     runconfig=None, \n",
+    "#     inputs=None, \n",
+    "#     outputs=None, \n",
+    "#     params=None, \n",
+    "#     source_directory=None, \n",
+    "#     allow_reuse=True, \n",
+    "#     version=None, \n",
+    "#     hash_paths=None)\n",
+    "# This returns a Step\n",
+    "step1 = PythonScriptStep(name=\"train_step\",\n",
+    "                         script_name=\"train.py\", \n",
+    "                         compute_target=aml_compute, \n",
+    "                         source_directory=project_folder,\n",
+    "                         allow_reuse=False)\n",
+    "print(\"Step1 created\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note:** In the above call to PythonScriptStep(), the flag *allow_reuse* determines whether the step should reuse previous results when run with the same settings/inputs. This flag's default value is *True*; the default is set to *True* because, when inputs and parameters have not changed, we typically do not want to re-run a given pipeline step. \n",
+    "\n",
+    "If *allow_reuse* is set to *False*, a new run will always be generated for this step during pipeline execution. The *allow_reuse* flag can come in handy in situations where you do *not* want to re-run a pipeline step."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Running a few steps in parallel\n",
+    "Here we are looking at a simple scenario where we are running a few steps (all involving PythonScriptStep)  in parallel. Running nodes in **parallel** is the default behavior for steps in a pipeline.\n",
+    "\n",
+    "We already have one step defined earlier. Let's define few more steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# All steps use files already available in the project_folder\n",
+    "# All steps use the same Azure Machine Learning compute target as well\n",
+    "step2 = PythonScriptStep(name=\"compare_step\",\n",
+    "                         script_name=\"compare.py\", \n",
+    "                         compute_target=aml_compute, \n",
+    "                         source_directory=project_folder)\n",
+    "\n",
+    "step3 = PythonScriptStep(name=\"extract_step\",\n",
+    "                         script_name=\"extract.py\", \n",
+    "                         compute_target=aml_compute, \n",
+    "                         source_directory=project_folder)\n",
+    "\n",
+    "# list of steps to run\n",
+    "steps = [step1, step2, step3]\n",
+    "print(\"Step lists created\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Build the pipeline\n",
+    "Once we have the steps (or steps collection), we can build the [pipeline](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py). By deafult, all these steps will run in **parallel** once we submit the pipeline for run.\n",
+    "\n",
+    "A pipeline is created with a list of steps and a workspace. Submit a pipeline using [submit](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment%28class%29?view=azure-ml-py#submit). When submit is called, a [PipelineRun](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinerun?view=azure-ml-py) is created which in turn creates [StepRun](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.steprun?view=azure-ml-py) objects for each step in the workflow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Syntax\n",
+    "# Pipeline(workspace, \n",
+    "#          steps, \n",
+    "#          description=None, \n",
+    "#          default_datastore_name=None, \n",
+    "#          default_source_directory=None, \n",
+    "#          resolve_closure=True, \n",
+    "#          _workflow_provider=None, \n",
+    "#          _service_endpoint=None)\n",
+    "\n",
+    "pipeline1 = Pipeline(workspace=ws, steps=steps)\n",
+    "print (\"Pipeline is built\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Validate the pipeline\n",
+    "You have the option to [validate](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py#validate) the pipeline prior to submitting for run. The platform runs validation steps such as checking for circular dependencies and parameter checks etc. even if you do not explicitly call validate method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipeline1.validate()\n",
+    "print(\"Pipeline validation complete\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Submit the pipeline\n",
+    "[Submitting](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py#submit) the pipeline involves creating an [Experiment](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment?view=azure-ml-py) object and providing the built pipeline for submission. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Submit syntax\n",
+    "# submit(experiment_name, \n",
+    "#        pipeline_parameters=None, \n",
+    "#        continue_on_node_failure=False, \n",
+    "#        regenerate_outputs=False)\n",
+    "\n",
+    "pipeline_run1 = Experiment(ws, 'Hello_World1').submit(pipeline1, regenerate_outputs=True)\n",
+    "print(\"Pipeline is submitted for execution\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note:** If regenerate_outputs is set to True, a new submit will always force generation of all step outputs, and disallow data reuse for any step of this run. Once this run is complete, however, subsequent runs may reuse the results of this run.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Examine the pipeline run\n",
+    "\n",
+    "#### Use RunDetails Widget\n",
+    "We are going to use the RunDetails widget to examine the run of the pipeline. You can click each row below to get more details on the step runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RunDetails(pipeline_run1).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Use Pipeline SDK objects\n",
+    "You can cycle through the node_run objects and examine job logs, stdout, and stderr of each of the steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "step_runs = pipeline_run1.get_children()\n",
+    "for step_run in step_runs:\n",
+    "    status = step_run.get_status()\n",
+    "    print('Script:', step_run.name, 'status:', status)\n",
+    "    \n",
+    "    # Change this if you want to see details even if the Step has succeeded.\n",
+    "    if status == \"Failed\":\n",
+    "        joblog = step_run.get_job_log()\n",
+    "        print('job log:', joblog)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Get additonal run details\n",
+    "If you wait until the pipeline_run is finished, you may be able to get additional details on the run. **Since this is a blocking call, the following code is commented out.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#pipeline_run1.wait_for_completion()\n",
+    "#for step_run in pipeline_run1.get_children():\n",
+    "#    print(\"{}: {}\".format(step_run.name, step_run.get_metrics()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Running a few steps in sequence\n",
+    "Now let's see how we run a few steps in sequence. We already have three steps defined earlier. Let's *reuse* those steps for this part.\n",
+    "\n",
+    "We will reuse step1, step2, step3, but build the pipeline in such a way that we chain step3 after step2 and step2 after step1. Note that there is no explicit data dependency between these steps, but still steps can be made dependent by using the [run_after](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.builder.pipelinestep?view=azure-ml-py#run-after) construct."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "step2.run_after(step1)\n",
+    "step3.run_after(step2)\n",
+    "\n",
+    "# Try a loop\n",
+    "#step2.run_after(step3)\n",
+    "\n",
+    "# Now, construct the pipeline using the steps.\n",
+    "\n",
+    "# We can specify the \"final step\" in the chain, \n",
+    "# Pipeline will take care of \"transitive closure\" and \n",
+    "# figure out the implicit or explicit dependencies\n",
+    "# https://www.geeksforgeeks.org/transitive-closure-of-a-graph/\n",
+    "pipeline2 = Pipeline(workspace=ws, steps=[step3])\n",
+    "print (\"Pipeline is built\")\n",
+    "\n",
+    "pipeline2.validate()\n",
+    "print(\"Simple validation complete\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipeline_run2 = Experiment(ws, 'Hello_World2').submit(pipeline2)\n",
+    "print(\"Pipeline is submitted for execution\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RunDetails(pipeline_run2).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Next: Pipelines with data dependency\n",
+    "The next [notebook](./aml-pipelines-with-data-dependency-steps.ipynb) demostrates how to construct a pipeline with data dependency."
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "diray"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

pipeline/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb

@@ -0,0 +1,358 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How to Publish a Pipeline and Invoke the REST endpoint\n",
+    "In this notebook, we will see how we can publish a pipeline and then invoke the REST endpoint."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites and Azure Machine Learning Basics\n",
+    "Make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n",
+    "\n",
+    "### Initialization Steps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import azureml.core\n",
+    "from azureml.core import Workspace, Run, Experiment, Datastore\n",
+    "from azureml.core.compute import AmlCompute\n",
+    "from azureml.core.compute import ComputeTarget\n",
+    "from azureml.core.compute import DataFactoryCompute\n",
+    "from azureml.widgets import RunDetails\n",
+    "\n",
+    "# Check core SDK version number\n",
+    "print(\"SDK version:\", azureml.core.VERSION)\n",
+    "\n",
+    "from azureml.data.data_reference import DataReference\n",
+    "from azureml.pipeline.core import Pipeline, PipelineData, StepSequence\n",
+    "from azureml.pipeline.steps import PythonScriptStep\n",
+    "from azureml.pipeline.steps import DataTransferStep\n",
+    "from azureml.pipeline.core import PublishedPipeline\n",
+    "from azureml.pipeline.core.graph import PipelineParameter\n",
+    "\n",
+    "print(\"Pipeline SDK-specific imports completed\")\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')\n",
+    "\n",
+    "# Default datastore (Azure file storage)\n",
+    "def_file_store = ws.get_default_datastore() \n",
+    "print(\"Default datastore's name: {}\".format(def_file_store.name))\n",
+    "\n",
+    "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
+    "print(\"Blobstore's name: {}\".format(def_blob_store.name))\n",
+    "\n",
+    "# project folder\n",
+    "project_folder = '.'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compute Targets\n",
+    "#### Retrieve an already attached  Azure Machine Learning Compute"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "aml_compute_target = \"aml-compute\"\n",
+    "try:\n",
+    "    aml_compute = AmlCompute(ws, aml_compute_target)\n",
+    "    print(\"found existing compute target.\")\n",
+    "except:\n",
+    "    print(\"creating new compute target\")\n",
+    "    \n",
+    "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n",
+    "                                                                min_nodes = 1, \n",
+    "                                                                max_nodes = 4)    \n",
+    "    aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n",
+    "    aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
+    "    \n",
+    "print(aml_compute.status.serialize())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Building Pipeline Steps with Inputs and Outputs\n",
+    "As mentioned earlier, a step in the pipeline can take data as input. This data can be a data source that lives in one of the accessible data locations, or intermediate data produced by a previous step in the pipeline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reference the data uploaded to blob storage using DataReference\n",
+    "# Assign the datasource to blob_input_data variable\n",
+    "blob_input_data = DataReference(\n",
+    "    datastore=def_blob_store,\n",
+    "    data_reference_name=\"test_data\",\n",
+    "    path_on_datastore=\"20newsgroups/20news.pkl\")\n",
+    "print(\"DataReference object created\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define intermediate data using PipelineData\n",
+    "processed_data1 = PipelineData(\"processed_data1\",datastore=def_blob_store)\n",
+    "print(\"PipelineData object created\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define a Step that consumes a datasource and produces intermediate data.\n",
+    "In this step, we define a step that consumes a datasource and produces intermediate data.\n",
+    "\n",
+    "**Open `train.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.** "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# trainStep consumes the datasource (Datareference) in the previous step\n",
+    "# and produces processed_data1\n",
+    "trainStep = PythonScriptStep(\n",
+    "    script_name=\"train.py\", \n",
+    "        arguments=[\"--input_data\", blob_input_data, \"--output_train\", processed_data1],\n",
+    "    inputs=[blob_input_data],\n",
+    "    outputs=[processed_data1],\n",
+    "    compute_target=aml_compute, \n",
+    "    source_directory=project_folder\n",
+    ")\n",
+    "print(\"trainStep created\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define a Step that consumes intermediate data and produces intermediate data\n",
+    "In this step, we define a step that consumes an intermediate data and produces intermediate data.\n",
+    "\n",
+    "**Open `extract.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.** "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# extractStep to use the intermediate data produced by step4\n",
+    "# This step also produces an output processed_data2\n",
+    "processed_data2 = PipelineData(\"processed_data2\", datastore=def_blob_store)\n",
+    "\n",
+    "extractStep = PythonScriptStep(\n",
+    "    script_name=\"extract.py\",\n",
+    "    arguments=[\"--input_extract\", processed_data1, \"--output_extract\", processed_data2],\n",
+    "    inputs=[processed_data1],\n",
+    "    outputs=[processed_data2],\n",
+    "    compute_target=aml_compute, \n",
+    "    source_directory=project_folder)\n",
+    "print(\"extractStep created\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define a Step that consumes multiple intermediate data and produces intermediate data\n",
+    "In this step, we define a step that consumes multiple intermediate data and produces intermediate data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PipelineParameter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This step also has a [PipelineParameter](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.graph.pipelineparameter?view=azure-ml-py) argument that help with calling the REST endpoint of the published pipeline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We will use this later in publishing pipeline\n",
+    "pipeline_param = PipelineParameter(name=\"pipeline_arg\", default_value=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Open `compare.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Now define step6 that takes two inputs (both intermediate data), and produce an output\n",
+    "processed_data3 = PipelineData(\"processed_data3\", datastore=def_blob_store)\n",
+    "\n",
+    "\n",
+    "\n",
+    "compareStep = PythonScriptStep(\n",
+    "    script_name=\"compare.py\",\n",
+    "    arguments=[\"--compare_data1\", processed_data1, \"--compare_data2\", processed_data2, \"--output_compare\", processed_data3, \"--pipeline_param\", pipeline_param],\n",
+    "    inputs=[processed_data1, processed_data2],\n",
+    "    outputs=[processed_data3],    \n",
+    "    compute_target=aml_compute, \n",
+    "    source_directory=project_folder)\n",
+    "print(\"compareStep created\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Build the pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipeline1 = Pipeline(workspace=ws, steps=[compareStep])\n",
+    "print (\"Pipeline is built\")\n",
+    "\n",
+    "pipeline1.validate()\n",
+    "print(\"Simple validation complete\") "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Publish the pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "published_pipeline1 = pipeline1.publish(name=\"My_New_Pipeline\", description=\"My Published Pipeline Description\")\n",
+    "print(published_pipeline1.id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run published pipeline using its REST endpoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.authentication import AzureCliAuthentication\n",
+    "import requests\n",
+    "\n",
+    "cli_auth = AzureCliAuthentication()\n",
+    "aad_token = cli_auth.get_authentication_header()\n",
+    "\n",
+    "rest_endpoint1 = published_pipeline1.endpoint\n",
+    "\n",
+    "print(rest_endpoint1)\n",
+    "\n",
+    "# specify the param when running the pipeline\n",
+    "response = requests.post(rest_endpoint1, \n",
+    "                         headers=aad_token, \n",
+    "                         json={\"ExperimentName\": \"My_Pipeline1\",\n",
+    "                               \"RunSource\": \"SDK\",\n",
+    "                               \"ParameterAssignments\": {\"pipeline_arg\": 45}})\n",
+    "run_id = response.json()[\"Id\"]\n",
+    "\n",
+    "print(run_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Next: Data Transfer\n",
+    "The next [notebook](./aml-pipelines-data-transfer.ipynb) will showcase data transfer steps between different types of data stores."
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "diray"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

pipeline/aml-pipelines-use-adla-as-compute-target.ipynb

@@ -0,0 +1,348 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AML Pipeline with AdlaStep\n",
+    "This notebook is used to demonstrate the use of AdlaStep in AML Pipeline."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## AML and Pipeline SDK-specific imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import azureml.core\n",
+    "from azureml.core.compute import ComputeTarget, DatabricksCompute\n",
+    "from azureml.exceptions import ComputeTargetException\n",
+    "from azureml.core import Workspace, Run, Experiment\n",
+    "from azureml.pipeline.core import Pipeline, PipelineData\n",
+    "from azureml.pipeline.steps import AdlaStep\n",
+    "from azureml.core.datastore import Datastore\n",
+    "from azureml.data.data_reference import DataReference\n",
+    "from azureml.core import attach_legacy_compute_target\n",
+    "\n",
+    "# Check core SDK version number\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize Workspace\n",
+    "\n",
+    "Initialize a workspace object from persisted configuration. Make sure the config file is present at .\\config.json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "create workspace"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "script_folder = '.'\n",
+    "experiment_name = \"adla_101_experiment\"\n",
+    "ws._initialize_folder(experiment_name=experiment_name, directory=script_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Register Datastore"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# un-comment the following and replace the strings with the \n",
+    "# correct values for your ADLS datastore\n",
+    "\n",
+    "# workspace=\"<my-workspace-name\"\n",
+    "# datastore_name= \"<my-adls-datastore-name>\"\n",
+    "# subscription_id = \"<my-subscription-id>\"\n",
+    "# resource_group = \"<my-rg>\"\n",
+    "# store_name = \"<my-sotrename>\"\n",
+    "# tenant_id = \"<my-tenant>\"\n",
+    "# client_id = \"<my-client-id>\"\n",
+    "# client_secret = \"<my-client-secret>\"\n",
+    "\n",
+    "\n",
+    "try:\n",
+    "    adls_datastore = Datastore.get(ws, datastore_name)\n",
+    "    print(\"found datastore with name: %s\" % datastore_name)\n",
+    "except:\n",
+    "    adls_datastore = Datastore.register_azure_data_lake(\n",
+    "        workspace=ws,\n",
+    "        datastore_name=datastore_name,\n",
+    "        subscription_id=subscription_id, # subscription id of ADLS account\n",
+    "        resource_group=resource_group, # resource group of ADLS account\n",
+    "        store_name=store_name, # ADLS account name\n",
+    "        tenant_id=tenant_id, # tenant id of service principal\n",
+    "        client_id=client_id, # client id of service principal\n",
+    "        client_secret=client_secret) # the secret of service principal\n",
+    "    print(\"registered datastore with name: %s\" % datastore_name)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create DataReferences and PipelineData\n",
+    "\n",
+    "In the code cell below, replace datastorename with your default datastore name. Copy the file `testdata.txt` (located in the pipeline folder that this notebook is in) to the path on the datastore."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datastorename = \"TestAdlsDatastore\"\n",
+    "\n",
+    "adls_datastore = Datastore(workspace=ws, name=datastorename)\n",
+    "script_input = DataReference(\n",
+    "    datastore=adls_datastore,\n",
+    "    data_reference_name=\"script_input\",\n",
+    "    path_on_datastore=\"testdata/testdata.txt\")\n",
+    "\n",
+    "script_output = PipelineData(\"script_output\", datastore=adls_datastore)\n",
+    "\n",
+    "print(\"Created Pipeline Data\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup Data Lake Account\n",
+    "\n",
+    "ADLA can only use data that is located in the default data store associated with that ADLA account. Through Azure portal, check the name of the default data store corresponding to the ADLA account you are using below. Replace the value associated with `adla_compute_name` in the code cell below accordingly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adla_compute_name = 'testadl' # Replace this with your default compute\n",
+    "\n",
+    "from azureml.core.compute import ComputeTarget, AdlaCompute\n",
+    "\n",
+    "def get_or_create_adla_compute(workspace, compute_name):\n",
+    "    try:\n",
+    "        return AdlaCompute(workspace, compute_name)\n",
+    "    except ComputeTargetException as e:\n",
+    "        if 'ComputeTargetNotFound' in e.message:\n",
+    "            print('adla compute not found, creating...')\n",
+    "            provisioning_config = AdlaCompute.provisioning_configuration()\n",
+    "            adla_compute = ComputeTarget.create(workspace, compute_name, provisioning_config)\n",
+    "            adla_compute.wait_for_completion()\n",
+    "            return adla_compute\n",
+    "        else:\n",
+    "            raise e\n",
+    "            \n",
+    "adla_compute = get_or_create_adla_compute(ws, adla_compute_name)\n",
+    "\n",
+    "# CLI:\n",
+    "# Create: az ml computetarget setup adla -n <name>\n",
+    "# BYOC: az ml computetarget attach adla -n <name> -i <resource-id>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once the above code cell completes, run the below to check your ADLA compute status:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"ADLA compute state:{}\".format(adla_compute.provisioning_state))\n",
+    "print(\"ADLA compute state:{}\".format(adla_compute.provisioning_errors))\n",
+    "print(\"Using ADLA compute:{}\".format(adla_compute.cluster_resource_id))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an AdlaStep"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**AdlaStep** is used to run U-SQL script using Azure Data Lake Analytics.\n",
+    "\n",
+    "- **name:** Name of module\n",
+    "- **script_name:** name of U-SQL script\n",
+    "- **inputs:** List of input port bindings\n",
+    "- **outputs:** List of output port bindings\n",
+    "- **adla_compute:** the ADLA compute to use for this job\n",
+    "- **params:** Dictionary of name-value pairs to pass to U-SQL job *(optional)*\n",
+    "- **degree_of_parallelism:** the degree of parallelism to use for this job *(optional)*\n",
+    "- **priority:** the priority value to use for the current job *(optional)*\n",
+    "- **runtime_version:** the runtime version of the Data Lake Analytics engine *(optional)*\n",
+    "- **root_folder:** folder that contains the script, assemblies etc. *(optional)*\n",
+    "- **hash_paths:** list of paths to hash to detect a change (script file is always hashed) *(optional)*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adla_step = AdlaStep(\n",
+    "    name='adla_script_step',\n",
+    "    script_name='test_adla_script.usql',\n",
+    "    inputs=[script_input],\n",
+    "    outputs=[script_output],\n",
+    "    compute_target=adla_compute)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Build and Submit the Experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipeline = Pipeline(\n",
+    "    description=\"adla_102\",\n",
+    "    workspace=ws, \n",
+    "    steps=[adla_step],\n",
+    "    default_source_directory=script_folder)\n",
+    "\n",
+    "pipeline_run = Experiment(workspace, experiment_name).submit(pipeline)\n",
+    "pipeline_run.wait_for_completion()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### View Run Details"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(pipeline_run).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Examine the run\n",
+    "You can cycle through the node_run objects and examine job logs, stdout, and stderr of each of the steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "step_runs = pipeline_run.get_children()\n",
+    "for step_run in step_runs:\n",
+    "    status = step_run.get_status()\n",
+    "    print('node', step_run.name, 'status:', status)\n",
+    "    if status == \"Failed\":\n",
+    "        joblog = step_run.get_job_log()\n",
+    "        print('job log:', joblog)\n",
+    "        stdout_log = step_run.get_stdout_log()\n",
+    "        print('stdout log:', stdout_log)\n",
+    "        stderr_log = step_run.get_stderr_log()\n",
+    "        print('stderr log:', stderr_log)\n",
+    "        with open(\"logs-\" + step_run.name + \".txt\", \"w\") as f:\n",
+    "            f.write(joblog)\n",
+    "            print(\"Job log written to logs-\"+ step_run.name + \".txt\")\n",
+    "    if status == \"Finished\":\n",
+    "        stdout_log = step_run.get_stdout_log()\n",
+    "        print('stdout log:', stdout_log)"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "diray"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

pipeline/aml-pipelines-use-databricks-as-compute-target.ipynb

@@ -0,0 +1,651 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using Databricks as a Compute Target from Azure Machine Learning Pipeline\n",
+    "To use Databricks as a compute target from [Azure Machine Learning Pipeline](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines), a [DatabricksStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep?view=azure-ml-py) is used. This notebook demonstrates the use of DatabricksStep in Azure Machine Learning Pipeline.\n",
+    "\n",
+    "The notebook will show:\n",
+    "1. Running an arbitrary Databricks notebook that the customer has in Databricks workspace\n",
+    "2. Running an arbitrary Python script that the customer has in DBFS\n",
+    "3. Running an arbitrary Python script that is available on local computer (will upload to DBFS, and then run in Databricks) \n",
+    "4. Running a JAR job that the customer has in DBFS.\n",
+    "\n",
+    "## Before you begin:\n",
+    "\n",
+    "1. **Create an Azure Databricks workspace** in the same subscription where you have your Azure Machine Learning workspace. You will need details of this workspace later on to define DatabricksStep. [Click here](https://ms.portal.azure.com/#blade/HubsExtension/Resources/resourceType/Microsoft.Databricks%2Fworkspaces) for more information.\n",
+    "2. **Create PAT (access token)**: Manually create a Databricks access token at the Azure Databricks portal. See [this](https://docs.databricks.com/api/latest/authentication.html#generate-a-token) for more information.\n",
+    "3. **Add demo notebook to ADB**: This notebook has a sample you can use as is. Launch Azure Databricks attached to your Azure Machine Learning workspace and add a new notebook. \n",
+    "4. **Create/attach a Blob storage** for use from ADB"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Add demo notebook to ADB Workspace\n",
+    "Copy and paste the below code to create a new notebook in your ADB workspace."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "# direct access\n",
+    "dbutils.widgets.get(\"myparam\")\n",
+    "p = getArgument(\"myparam\")\n",
+    "print (\"Param -\\'myparam':\")\n",
+    "print (p)\n",
+    "\n",
+    "dbutils.widgets.get(\"input\")\n",
+    "i = getArgument(\"input\")\n",
+    "print (\"Param -\\'input':\")\n",
+    "print (i)\n",
+    "\n",
+    "dbutils.widgets.get(\"output\")\n",
+    "o = getArgument(\"output\")\n",
+    "print (\"Param -\\'output':\")\n",
+    "print (o)\n",
+    "\n",
+    "n = i + \"/testdata.txt\"\n",
+    "df = spark.read.csv(n)\n",
+    "\n",
+    "display (df)\n",
+    "\n",
+    "data = [('value1', 'value2')]\n",
+    "df2 = spark.createDataFrame(data)\n",
+    "\n",
+    "z = o + \"/output.txt\"\n",
+    "df2.write.csv(z)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Azure Machine Learning and Pipeline SDK-specific imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import azureml.core\n",
+    "from azureml.core.runconfig import JarLibrary\n",
+    "from azureml.core.compute import ComputeTarget, DatabricksCompute\n",
+    "from azureml.exceptions import ComputeTargetException\n",
+    "from azureml.core import Workspace, Run, Experiment\n",
+    "from azureml.pipeline.core import Pipeline, PipelineData\n",
+    "from azureml.pipeline.steps import DatabricksStep\n",
+    "from azureml.core.datastore import Datastore\n",
+    "from azureml.data.data_reference import DataReference\n",
+    "\n",
+    "# Check core SDK version number\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize Workspace\n",
+    "\n",
+    "Initialize a workspace object from persisted configuration. Make sure the config file is present at .\\config.json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Attach Databricks compute target\n",
+    "Next, you need to add your Databricks workspace to Azure Machine Learning as a compute target and give it a name. You will use this name to refer to your Databricks workspace compute target inside Azure Machine Learning.\n",
+    "\n",
+    "- **Resource Group** - The resource group name of your Azure Machine Learning workspace\n",
+    "- **Databricks Workspace Name** - The workspace name of your Azure Databricks workspace\n",
+    "- **Databricks Access Token** - The access token you created in ADB"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Replace with your account info before running.\n",
+    "\n",
+    "# db_compute_name = \"<my-databricks_compute_name>\"\n",
+    "# aml_resource_group = \"<my-aml-resource-group>\"\n",
+    "# db_workspace_name = \"<my-databricks_workspace_name>\"\n",
+    "# access_token = \"<my-databricks_access_token>\"\n",
+    "\n",
+    "try:\n",
+    "    databricks_compute = ComputeTarget(workspace=ws, name=db_compute_name)\n",
+    "    print('Compute target {} already exists'.format(db_compute_name))\n",
+    "except ComputeTargetException:\n",
+    "    print('compute not found')\n",
+    "    print('databricks_compute_name {}'.format(db_compute_name))\n",
+    "    print('databricks_resource_id {}'.format(db_workspace_name))\n",
+    "    print('databricks_access_token {}'.format(access_token))\n",
+    "\n",
+    "    config = DatabricksCompute.attach_configuration(aml_resource_group, db_workspace_name, access_token)\n",
+    "    ComputeTarget.attach(ws, db_compute_name, config)\n",
+    "    databricks_compute.wait_for_completion(True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Connections with Inputs and Outputs\n",
+    "The DatabricksStep supports Azure Bloband ADLS for inputs and outputs. You also will need to define a [Secrets](https://docs.azuredatabricks.net/user-guide/secrets/index.html) scope to enable authentication to external data sources such as Blob and ADLS from Databricks.\n",
+    "\n",
+    "- Databricks documentation on [Azure Blob](https://docs.azuredatabricks.net/spark/latest/data-sources/azure/azure-storage.html)\n",
+    "- Databricks documentation on [ADLS](https://docs.databricks.com/spark/latest/data-sources/azure/azure-datalake.html)\n",
+    "\n",
+    "### Type of Data Access\n",
+    "Databricks allows to interact with Azure Blob and ADLS in two ways.\n",
+    "- **Direct Access**: Databricks allows you to interact with Azure Blob or ADLS URIs directly. The input or output URIs will be mapped to a Databricks widget param in the Databricks notebook.\n",
+    "- **Mouting**: You will be supplied with additional parameters and secrets that will enable you to mount your ADLS or Azure Blob input or output location in your Databricks notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Direct Access: Python sample code\n",
+    "If you have a data reference named \"input\" it will represent the URI of the input and you can access it directly in the Databricks python notebook like so:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "dbutils.widgets.get(\"input\")\n",
+    "y = getArgument(\"input\")\n",
+    "df = spark.read.csv(y)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Mounting: Python sample code for Azure Blob\n",
+    "Given an Azure Blob data reference named \"input\" the following widget params will be made available in the Databricks notebook:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "# This contains the input URI\n",
+    "dbutils.widgets.get(\"input\")\n",
+    "myinput_uri = getArgument(\"input\")\n",
+    "\n",
+    "# How to get the input datastore name inside ADB notebook\n",
+    "# This contains the name of a Databricks secret (in the predefined \"amlscope\" secret scope) \n",
+    "# that contians an access key or sas for the Azure Blob input (this name is obtained by appending \n",
+    "# the name of the input with \"_blob_secretname\". \n",
+    "dbutils.widgets.get(\"input_blob_secretname\") \n",
+    "myinput_blob_secretname = getArgument(\"input_blob_secretname\")\n",
+    "\n",
+    "# This contains the required configuration for mounting\n",
+    "dbutils.widgets.get(\"input_blob_config\")\n",
+    "myinput_blob_config = getArgument(\"input_blob_config\")\n",
+    "\n",
+    "# Usage\n",
+    "dbutils.fs.mount(\n",
+    "  source = myinput_uri,\n",
+    "  mount_point = \"/mnt/input\",\n",
+    "  extra_configs = {myinput_blob_config:dbutils.secrets.get(scope = \"amlscope\", key = myinput_blob_secretname)})\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Mounting: Python sample code for ADLS\n",
+    "Given an ADLS data reference named \"input\" the following widget params will be made available in the Databricks notebook:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "# This contains the input URI\n",
+    "dbutils.widgets.get(\"input\") \n",
+    "myinput_uri = getArgument(\"input\")\n",
+    "\n",
+    "# This contains the client id for the service principal \n",
+    "# that has access to the adls input\n",
+    "dbutils.widgets.get(\"input_adls_clientid\") \n",
+    "myinput_adls_clientid = getArgument(\"input_adls_clientid\")\n",
+    "\n",
+    "# This contains the name of a Databricks secret (in the predefined \"amlscope\" secret scope) \n",
+    "# that contains the secret for the above mentioned service principal\n",
+    "dbutils.widgets.get(\"input_adls_secretname\") \n",
+    "myinput_adls_secretname = getArgument(\"input_adls_secretname\")\n",
+    "\n",
+    "# This contains the refresh url for the mounting configs\n",
+    "dbutils.widgets.get(\"input_adls_refresh_url\") \n",
+    "myinput_adls_refresh_url = getArgument(\"input_adls_refresh_url\")\n",
+    "\n",
+    "# Usage \n",
+    "configs = {\"dfs.adls.oauth2.access.token.provider.type\": \"ClientCredential\",\n",
+    "           \"dfs.adls.oauth2.client.id\": myinput_adls_clientid,\n",
+    "           \"dfs.adls.oauth2.credential\": dbutils.secrets.get(scope = \"amlscope\", key =myinput_adls_secretname),\n",
+    "           \"dfs.adls.oauth2.refresh.url\": myinput_adls_refresh_url}\n",
+    "\n",
+    "dbutils.fs.mount(\n",
+    "  source = myinput_uri,\n",
+    "  mount_point = \"/mnt/output\",\n",
+    "  extra_configs = configs)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use Databricks from Azure Machine Learning Pipeline\n",
+    "To use Databricks as a compute target from Azure Machine Learning Pipeline, a DatabricksStep is used. Let's define a datasource (via DataReference) and intermediate data (via PipelineData) to be used in DatabricksStep."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use the default blob storage\n",
+    "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
+    "print('Datastore {} will be used'.format(def_blob_store.name))\n",
+    "\n",
+    "# We are uploading a sample file in the local directory to be used as a datasource\n",
+    "def_blob_store.upload_files([\"./testdata.txt\"], target_path=\"dbtest\", overwrite=False)\n",
+    "\n",
+    "step_1_input = DataReference(datastore=def_blob_store, path_on_datastore=\"dbtest\",\n",
+    "                                     data_reference_name=\"input\")\n",
+    "\n",
+    "step_1_output = PipelineData(\"output\", datastore=def_blob_store)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add a DatabricksStep\n",
+    "Adds a Databricks notebook as a step in a Pipeline.\n",
+    "- ***name:** Name of the Module\n",
+    "- **inputs:** List of input connections for data consumed by this step. Fetch this inside the notebook using dbutils.widgets.get(\"input\")\n",
+    "- **outputs:** List of output port definitions for outputs produced by this step. Fetch this inside the notebook using dbutils.widgets.get(\"output\")\n",
+    "- **spark_version:** Version of spark for the databricks run cluster. default value: 4.0.x-scala2.11\n",
+    "- **node_type:** Azure vm node types for the databricks run cluster. default value: Standard_D3_v2\n",
+    "- **num_workers:** Number of workers for the databricks run cluster\n",
+    "- **autoscale:** The autoscale configuration for the databricks run cluster\n",
+    "- **spark_env_variables:** Spark environment variables for the databricks run cluster (dictionary of {str:str}). default value: {'PYSPARK_PYTHON': '/databricks/python3/bin/python3'}\n",
+    "- ***notebook_path:** Path to the notebook in the databricks instance.\n",
+    "- **notebook_params:** Parameters  for the databricks notebook (dictionary of {str:str}). Fetch this inside the notebook using dbutils.widgets.get(\"myparam\")\n",
+    "- **run_name:** Name in databricks for this run\n",
+    "- **timeout_seconds:** Timeout for the databricks run\n",
+    "- **maven_libraries:** maven libraries for the databricks run\n",
+    "- **pypi_libraries:** pypi libraries for the databricks run\n",
+    "- **egg_libraries:** egg libraries for the databricks run\n",
+    "- **jar_libraries:** jar libraries for the databricks run\n",
+    "- **rcran_libraries:** rcran libraries for the databricks run\n",
+    "- **databricks_compute:** Azure Databricks compute\n",
+    "- **databricks_compute_name:** Name of Azure Databricks compute\n",
+    "\n",
+    "\\* *denotes required fields*  \n",
+    "*You must provide exactly one of num_workers or autoscale paramaters*  \n",
+    "*You must provide exactly one of databricks_compute or databricks_compute_name parameters*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id='notebook_howto'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Running the demo notebook already added to the Databricks workspace\n",
+    "The commented out code in the below cell assumes that you have created a notebook called `demo_notebook` in Azure Databricks under your user folder so you can use `notebook_path = \"/Users/you@company.com/demo_notebook\"`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# notebook_path = \"/Users/you@company.com/demo_notebook\"\n",
+    "\n",
+    "dbNbStep = DatabricksStep(\n",
+    "    name=\"DBNotebookInWS\",\n",
+    "    inputs=[step_1_input],\n",
+    "    outputs=[step_1_output],\n",
+    "    num_workers=1,\n",
+    "    notebook_path=notebook_path,\n",
+    "    notebook_params={'myparam': 'testparam'},\n",
+    "    run_name='DB_Notebook_demo',\n",
+    "    compute_target=databricks_compute,\n",
+    "    allow_reuse=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Build and submit the Experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "steps = [dbNbStep]\n",
+    "pipeline = Pipeline(workspace=ws, steps=steps)\n",
+    "pipeline_run = Experiment(ws, 'DB_Notebook_demo').submit(pipeline)\n",
+    "pipeline_run.wait_for_completion()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### View Run Details"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(pipeline_run).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Running a Python script that is already added in DBFS\n",
+    "To run a Python script that is already uploaded to DBFS, follow the instructions below. You will first upload the Python script to DBFS using the [CLI](https://docs.azuredatabricks.net/user-guide/dbfs-databricks-file-system.html).\n",
+    "\n",
+    "The commented out code in the below cell assumes that you have uploaded `train-db-dbfs.py` to the root folder in DBFS. You can upload `train-db-dbfs.py` to the root folder in DBFS using this commandline so you can use `python_script_path = \"dbfs:/train-db-dbfs.py\"`:\n",
+    "\n",
+    "```\n",
+    "dbfs cp ./train-db-dbfs.py dbfs:/train-db-dbfs.py\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "python_script_path = \"dbfs:/train-db-dbfs.py\"\n",
+    "\n",
+    "dbPythonInDbfsStep = DatabricksStep(\n",
+    "    name=\"DBPythonInDBFS\",\n",
+    "    inputs=[step_1_input],\n",
+    "    num_workers=1,\n",
+    "    python_script_path=python_script_path,\n",
+    "    python_script_params={'--input_data'},\n",
+    "    run_name='DB_Python_demo',\n",
+    "    compute_target=databricks_compute,\n",
+    "    allow_reuse=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Build and submit the Experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "steps = [dbPythonInDbfsStep]\n",
+    "pipeline = Pipeline(workspace=ws, steps=steps)\n",
+    "pipeline_run = Experiment(ws, 'DB_Python_demo').submit(pipeline)\n",
+    "pipeline_run.wait_for_completion()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### View Run Details"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(pipeline_run).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Running a Python script in Databricks that currenlty is in local computer\n",
+    "To run a Python script that is currently in your local computer, follow the instructions below. \n",
+    "\n",
+    "The commented out code below code assumes that you have `train-db-local.py` in the `scripts` subdirectory under the current working directory.\n",
+    "\n",
+    "In this case, the Python script will be uploaded first to DBFS, and then the script will be run in Databricks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "python_script_name = \"train-db-local.py\"\n",
+    "source_directory = \".\"\n",
+    "\n",
+    "dbPythonInLocalMachineStep = DatabricksStep(\n",
+    "    name=\"DBPythonInLocalMachine\",\n",
+    "    inputs=[step_1_input],\n",
+    "    num_workers=1,\n",
+    "    python_script_name=python_script_name,\n",
+    "    source_directory=source_directory,\n",
+    "    run_name='DB_Python_Local_demo',\n",
+    "    compute_target=databricks_compute,\n",
+    "    allow_reuse=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Build and submit the Experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "steps = [dbPythonInLocalMachineStep]\n",
+    "pipeline = Pipeline(workspace=ws, steps=steps)\n",
+    "pipeline_run = Experiment(ws, 'DB_Python_Local_demo').submit(pipeline)\n",
+    "pipeline_run.wait_for_completion()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### View Run Details"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(pipeline_run).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Running a JAR job that is alreay added in DBFS\n",
+    "To run a JAR job that is already uploaded to DBFS, follow the instructions below. You will first upload the JAR file to DBFS using the [CLI](https://docs.azuredatabricks.net/user-guide/dbfs-databricks-file-system.html).\n",
+    "\n",
+    "The commented out code in the below cell assumes that you have uploaded `train-db-dbfs.jar` to the root folder in DBFS. You can upload `train-db-dbfs.jar` to the root folder in DBFS using this commandline so you can use `jar_library_dbfs_path = \"dbfs:/train-db-dbfs.jar\"`:\n",
+    "\n",
+    "```\n",
+    "dbfs cp ./train-db-dbfs.jar dbfs:/train-db-dbfs.jar\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "main_jar_class_name = \"com.microsoft.aeva.Main\"\n",
+    "jar_library_dbfs_path = \"dbfs:/train-db-dbfs.jar\"\n",
+    "\n",
+    "dbJarInDbfsStep = DatabricksStep(\n",
+    "    name=\"DBJarInDBFS\",\n",
+    "    inputs=[step_1_input],\n",
+    "    num_workers=1,\n",
+    "    main_class_name=main_jar_class_name,\n",
+    "    jar_params={'arg1', 'arg2'},\n",
+    "    run_name='DB_JAR_demo',\n",
+    "    jar_libraries=[JarLibrary(jar_library_dbfs_path)],\n",
+    "    compute_target=databricks_compute,\n",
+    "    allow_reuse=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Build and submit the Experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "steps = [dbJarInDbfsStep]\n",
+    "pipeline = Pipeline(workspace=ws, steps=steps)\n",
+    "pipeline_run = Experiment(ws, 'DB_JAR_demo').submit(pipeline)\n",
+    "pipeline_run.wait_for_completion()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### View Run Details"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(pipeline_run).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Next: ADLA as a Compute Target\n",
+    "To use ADLA as a compute target from Azure Machine Learning Pipeline, a AdlaStep is used. This [notebook](./aml-pipelines-use-adla-as-compute-target.ipynb) demonstrates the use of AdlaStep in Azure Machine Learning Pipeline."
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "diray"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

pipeline/aml-pipelines-with-data-dependency-steps.ipynb

@@ -0,0 +1,409 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Azure Machine Learning Pipelines with Data Dependency\n",
+    "In this notebook, we will see how we can build a pipeline with implicit data dependancy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites and Azure Machine Learning Basics\n",
+    "Make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n",
+    "\n",
+    "### Azure Machine Learning and Pipeline SDK-specific Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import azureml.core\n",
+    "from azureml.core import Workspace, Run, Experiment, Datastore\n",
+    "from azureml.core.compute import AmlCompute\n",
+    "from azureml.core.compute import ComputeTarget\n",
+    "from azureml.core.compute import DataFactoryCompute\n",
+    "from azureml.widgets import RunDetails\n",
+    "\n",
+    "# Check core SDK version number\n",
+    "print(\"SDK version:\", azureml.core.VERSION)\n",
+    "\n",
+    "from azureml.data.data_reference import DataReference\n",
+    "from azureml.pipeline.core import Pipeline, PipelineData, StepSequence\n",
+    "from azureml.pipeline.steps import PythonScriptStep\n",
+    "from azureml.pipeline.steps import DataTransferStep\n",
+    "from azureml.pipeline.core import PublishedPipeline\n",
+    "from azureml.pipeline.core.graph import PipelineParameter\n",
+    "\n",
+    "print(\"Pipeline SDK-specific imports completed\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize Workspace\n",
+    "\n",
+    "Initialize a [workspace](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace(class%29) object from persisted configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "create workspace"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')\n",
+    "\n",
+    "# Default datastore (Azure file storage)\n",
+    "def_file_store = ws.get_default_datastore() \n",
+    "print(\"Default datastore's name: {}\".format(def_file_store.name))\n",
+    "\n",
+    "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
+    "print(\"Blobstore's name: {}\".format(def_blob_store.name))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# project folder\n",
+    "project_folder = '.'\n",
+    "    \n",
+    "print('Sample projects will be created in {}.'.format(project_folder))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Required data and script files for the the tutorial\n",
+    "Sample files required to finish this tutorial are already copied to the project folder specified above. Even though the .py provided in the samples don't have much \"ML work,\" as a data scientist, you will work on this extensively as part of your work. To complete this tutorial, the contents of these files are not very important. The one-line files are for demostration purpose only."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compute Targets\n",
+    "See the list of Compute Targets on the workspace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cts = ws.compute_targets\n",
+    "for ct in cts:\n",
+    "    print(ct)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Retrieve or create a Aml compute\n",
+    "Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's create a new Aml Compute in the current workspace, if it doesn't already exist. We will then run the training script on this compute target."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "aml_compute_target = \"aml-compute\"\n",
+    "try:\n",
+    "    aml_compute = AmlCompute(ws, aml_compute_target)\n",
+    "    print(\"found existing compute target.\")\n",
+    "except:\n",
+    "    print(\"creating new compute target\")\n",
+    "    \n",
+    "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n",
+    "                                                                min_nodes = 1, \n",
+    "                                                                max_nodes = 4)    \n",
+    "    aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n",
+    "    aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
+    "    \n",
+    "print(\"Aml Compute attached\")\n",
+    "# For a more detailed view of current AmlCompute status, use the 'status' property    \n",
+    "print(aml_compute.status.serialize())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Wait for this call to finish before proceeding (you will see the asterisk turning to a number).**\n",
+    "\n",
+    "Now that you have created the compute target, let's see what the workspace's compute_targets() function returns. You should now see one entry named 'amlcompute' of type AmlCompute."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Building Pipeline Steps with Inputs and Outputs\n",
+    "As mentioned earlier, a step in the pipeline can take data as input. This data can be a data source that lives in one of the accessible data locations, or intermediate data produced by a previous step in the pipeline.\n",
+    "\n",
+    "### Datasources\n",
+    "Datasource is represented by **[DataReference](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.data_reference.datareference?view=azure-ml-py)** object and points to data that lives in or is accessible from Datastore. DataReference could be a pointer to a file or a directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reference the data uploaded to blob storage using DataReference\n",
+    "# Assign the datasource to blob_input_data variable\n",
+    "\n",
+    "# DataReference(datastore, \n",
+    "#               data_reference_name=None, \n",
+    "#               path_on_datastore=None, \n",
+    "#               mode='mount', \n",
+    "#               path_on_compute=None, \n",
+    "#               overwrite=False)\n",
+    "\n",
+    "blob_input_data = DataReference(\n",
+    "    datastore=def_blob_store,\n",
+    "    data_reference_name=\"test_data\",\n",
+    "    path_on_datastore=\"20newsgroups/20news.pkl\")\n",
+    "print(\"DataReference object created\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Intermediate/Output Data\n",
+    "Intermediate data (or output of a Step) is represented by **[PipelineData](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py)** object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps.\n",
+    "\n",
+    "#### Constructing PipelineData\n",
+    "- **name:** [*Required*] Name of the data item within the pipeline graph\n",
+    "- **datastore_name:** Name of the Datastore to write this output to\n",
+    "- **output_name:** Name of the output\n",
+    "- **output_mode:** Specifies \"upload\" or \"mount\" modes for producing output (default: mount)\n",
+    "- **output_path_on_compute:** For \"upload\" mode, the path to which the module writes this output during execution\n",
+    "- **output_overwrite:** Flag to overwrite pre-existing data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define intermediate data using PipelineData\n",
+    "# Syntax\n",
+    "\n",
+    "# PipelineData(name, \n",
+    "#              datastore=None, \n",
+    "#              output_name=None, \n",
+    "#              output_mode='mount', \n",
+    "#              output_path_on_compute=None, \n",
+    "#              output_overwrite=None, \n",
+    "#              data_type=None, \n",
+    "#              is_directory=None)\n",
+    "\n",
+    "# Naming the intermediate data as processed_data1 and assigning it to the variable processed_data1.\n",
+    "processed_data1 = PipelineData(\"processed_data1\",datastore=def_blob_store)\n",
+    "print(\"PipelineData object created\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pipelines steps using datasources and intermediate data\n",
+    "Machine learning pipelines have many steps and these steps could use or reuse datasources and intermediate data. Here's how we construct such a pipeline:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define a Step that consumes a datasource and produces intermediate data.\n",
+    "In this step, we define a step that consumes a datasource and produces intermediate data.\n",
+    "\n",
+    "**Open `train.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.** "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# step4 consumes the datasource (Datareference) in the previous step\n",
+    "# and produces processed_data1\n",
+    "trainStep = PythonScriptStep(\n",
+    "    script_name=\"train.py\", \n",
+    "    arguments=[\"--input_data\", blob_input_data, \"--output_train\", processed_data1],\n",
+    "    inputs=[blob_input_data],\n",
+    "    outputs=[processed_data1],\n",
+    "    compute_target=aml_compute, \n",
+    "    source_directory=project_folder\n",
+    ")\n",
+    "print(\"trainStep created\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define a Step that consumes intermediate data and produces intermediate data\n",
+    "In this step, we define a step that consumes an intermediate data and produces intermediate data.\n",
+    "\n",
+    "**Open `extract.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.** "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# step5 to use the intermediate data produced by step4\n",
+    "# This step also produces an output processed_data2\n",
+    "processed_data2 = PipelineData(\"processed_data2\", datastore=def_blob_store)\n",
+    "\n",
+    "extractStep = PythonScriptStep(\n",
+    "    script_name=\"extract.py\",\n",
+    "    arguments=[\"--input_extract\", processed_data1, \"--output_extract\", processed_data2],\n",
+    "    inputs=[processed_data1],\n",
+    "    outputs=[processed_data2],\n",
+    "    compute_target=aml_compute, \n",
+    "    source_directory=project_folder)\n",
+    "print(\"extractStep created\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define a Step that consumes multiple intermediate data and produces intermediate data\n",
+    "In this step, we define a step that consumes multiple intermediate data and produces intermediate data.\n",
+    "\n",
+    "**Open `compare.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Now define step6 that takes two inputs (both intermediate data), and produce an output\n",
+    "processed_data3 = PipelineData(\"processed_data3\", datastore=def_blob_store)\n",
+    "\n",
+    "compareStep = PythonScriptStep(\n",
+    "    script_name=\"compare.py\",\n",
+    "    arguments=[\"--compare_data1\", processed_data1, \"--compare_data2\", processed_data2, \"--output_compare\", processed_data3],\n",
+    "    inputs=[processed_data1, processed_data2],\n",
+    "    outputs=[processed_data3],    \n",
+    "    compute_target=aml_compute, \n",
+    "    source_directory=project_folder)\n",
+    "print(\"compareStep created\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Build the pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipeline1 = Pipeline(workspace=ws, steps=[compareStep])\n",
+    "print (\"Pipeline is built\")\n",
+    "\n",
+    "pipeline1.validate()\n",
+    "print(\"Simple validation complete\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipeline_run1 = Experiment(ws, 'Data_dependency').submit(pipeline1)\n",
+    "print(\"Pipeline is submitted for execution\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RunDetails(pipeline_run1).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Next: Publishing the Pipeline and calling it from the REST endpoint\n",
+    "See this [notebook](./aml-pipelines-publish-and-run-using-rest-endpoint.ipynb) to understand how the pipeline is published and you can call the REST endpoint to run the pipeline."
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "diray"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

pipeline/batch_scoring.py

@@ -0,0 +1,119 @@
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+import os
+import argparse
+import datetime
+import time
+import tensorflow as tf
+from math import ceil
+import numpy as np
+import shutil
+from tensorflow.contrib.slim.python.slim.nets import inception_v3
+from azureml.core.model import Model
+
+slim = tf.contrib.slim
+
+parser = argparse.ArgumentParser(description="Start a tensorflow model serving")
+parser.add_argument('--model_name', dest="model_name", required=True)
+parser.add_argument('--label_dir', dest="label_dir", required=True)
+parser.add_argument('--dataset_path', dest="dataset_path", required=True)
+parser.add_argument('--output_dir', dest="output_dir", required=True)
+parser.add_argument('--batch_size', dest="batch_size", type=int, required=True)
+
+args = parser.parse_args()
+
+image_size = 299
+num_channel = 3
+
+# create output directory if it does not exist
+os.makedirs(args.output_dir, exist_ok=True)
+
+
+def get_class_label_dict(label_file):
+    label = []
+    proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()
+    for l in proto_as_ascii_lines:
+        label.append(l.rstrip())
+    return label
+
+
+class DataIterator:
+    def __init__(self, data_dir):
+        self.file_paths = []
+        image_list = os.listdir(data_dir)
+        # total_size = len(image_list)
+        self.file_paths = [data_dir + '/' + file_name.rstrip() for file_name in image_list]
+
+        self.labels = [1 for file_name in self.file_paths]
+
+    @property
+    def size(self):
+        return len(self.labels)
+
+    def input_pipeline(self, batch_size):
+        images_tensor = tf.convert_to_tensor(self.file_paths, dtype=tf.string)
+        labels_tensor = tf.convert_to_tensor(self.labels, dtype=tf.int64)
+        input_queue = tf.train.slice_input_producer([images_tensor, labels_tensor], shuffle=False)
+        labels = input_queue[1]
+        images_content = tf.read_file(input_queue[0])
+
+        image_reader = tf.image.decode_jpeg(images_content, channels=num_channel, name="jpeg_reader")
+        float_caster = tf.cast(image_reader, tf.float32)
+        new_size = tf.constant([image_size, image_size], dtype=tf.int32)
+        images = tf.image.resize_images(float_caster, new_size)
+        images = tf.divide(tf.subtract(images, [0]), [255])
+
+        image_batch, label_batch = tf.train.batch([images, labels], batch_size=batch_size, capacity=5 * batch_size)
+        return image_batch
+
+
+def main(_):
+    # start_time = datetime.datetime.now()
+    label_file_name = os.path.join(args.label_dir, "labels.txt")
+    label_dict = get_class_label_dict(label_file_name)
+    classes_num = len(label_dict)
+    test_feeder = DataIterator(data_dir=args.dataset_path)
+    total_size = len(test_feeder.labels)
+    count = 0
+    # get model from model registry
+    model_path = Model.get_model_path(args.model_name)
+    with tf.Session() as sess:
+        test_images = test_feeder.input_pipeline(batch_size=args.batch_size)
+        with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
+            input_images = tf.placeholder(tf.float32, [args.batch_size, image_size, image_size, num_channel])
+            logits, _ = inception_v3.inception_v3(input_images,
+                                                  num_classes=classes_num,
+                                                  is_training=False)
+            probabilities = tf.argmax(logits, 1)
+
+        sess.run(tf.global_variables_initializer())
+        sess.run(tf.local_variables_initializer())
+        coord = tf.train.Coordinator()
+        threads = tf.train.start_queue_runners(sess=sess, coord=coord)
+        saver = tf.train.Saver()
+        saver.restore(sess, model_path)
+        out_filename = os.path.join(args.output_dir, "result-labels.txt")
+        with open(out_filename, "w") as result_file:
+            i = 0
+            while count < total_size and not coord.should_stop():
+                test_images_batch = sess.run(test_images)
+                file_names_batch = test_feeder.file_paths[i * args.batch_size:
+                                                          min(test_feeder.size, (i + 1) * args.batch_size)]
+                results = sess.run(probabilities, feed_dict={input_images: test_images_batch})
+                new_add = min(args.batch_size, total_size - count)
+                count += new_add
+                i += 1
+                for j in range(new_add):
+                    result_file.write(os.path.basename(file_names_batch[j]) + ": " + label_dict[results[j]] + "\n")
+                result_file.flush()
+            coord.request_stop()
+            coord.join(threads)
+
+        # copy the file to artifacts
+        shutil.copy(out_filename, "./outputs/")
+        # Move the processed data out of the blob so that the next run can process the data.
+
+
+if __name__ == "__main__":
+    tf.app.run()

pipeline/compare.py

@@ -0,0 +1,22 @@
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+import argparse
+import os
+
+print("In compare.py")
+print("As a data scientist, this is where I use my compare code.")
+parser = argparse.ArgumentParser("compare")
+parser.add_argument("--compare_data1", type=str, help="compare_data1 data")
+parser.add_argument("--compare_data2", type=str, help="compare_data2 data")
+parser.add_argument("--output_compare", type=str, help="output_compare directory")
+
+args = parser.parse_args()
+
+print("Argument 1: %s" % args.compare_data1)
+print("Argument 2: %s" % args.compare_data2)
+print("Argument 3: %s" % args.output_compare)
+
+if not (args.output_compare is None):
+    os.makedirs(args.output_compare, exist_ok=True)
+    print("%s created" % args.output_compare)

pipeline/extract.py

@@ -0,0 +1,21 @@
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+import argparse
+import os
+
+print("In extract.py")
+print("As a data scientist, this is where I use my extract code.")
+
+parser = argparse.ArgumentParser("extract")
+parser.add_argument("--input_extract", type=str, help="input_extract data")
+parser.add_argument("--output_extract", type=str, help="output_extract directory")
+
+args = parser.parse_args()
+
+print("Argument 1: %s" % args.input_extract)
+print("Argument 2: %s" % args.output_extract)
+
+if not (args.output_extract is None):
+    os.makedirs(args.output_extract, exist_ok=True)
+    print("%s created" % args.output_extract)

pipeline/pipeline-style-transfer.ipynb

@@ -0,0 +1,610 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Neural style transfer on video\n",
+    "Using modified code from `pytorch`'s neural style [example](https://pytorch.org/tutorials/advanced/neural_style_tutorial.html), we show how to setup a pipeline for doing style transfer on video. The pipeline has following steps:\n",
+    "1. Split a video into images\n",
+    "2. Run neural style on each image using one of the provided models (from `pytorch` pretrained models for this example).\n",
+    "3. Stitch the image back into a video."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "Make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize Workspace\n",
+    "\n",
+    "Initialize a workspace object from persisted configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from azureml.core import Workspace, Run, Experiment\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print('Workspace name: ' + ws.name, \n",
+    "      'Azure region: ' + ws.location, \n",
+    "      'Subscription id: ' + ws.subscription_id, \n",
+    "      'Resource group: ' + ws.resource_group, sep = '\\n')\n",
+    "\n",
+    "scripts_folder = \"scripts_folder\"\n",
+    "\n",
+    "if not os.path.isdir(scripts_folder):\n",
+    "    os.mkdir(scripts_folder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import AmlCompute, ComputeTarget\n",
+    "from azureml.core.datastore import Datastore\n",
+    "from azureml.data.data_reference import DataReference\n",
+    "from azureml.pipeline.core import Pipeline, PipelineData\n",
+    "from azureml.pipeline.steps import PythonScriptStep, MpiStep\n",
+    "from azureml.core.runconfig import CondaDependencies, RunConfiguration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create or use existing compute"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# AmlCompute\n",
+    "cpu_cluster_name = \"cpucluster\"\n",
+    "try:\n",
+    "    cpu_cluster = AmlCompute(ws, cpu_cluster_name)\n",
+    "    print(\"found existing cluster.\")\n",
+    "except:\n",
+    "    print(\"creating new cluster\")\n",
+    "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_v2\",\n",
+    "                                                                    max_nodes = 1)\n",
+    "\n",
+    "    # create the cluster\n",
+    "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, provisioning_config)\n",
+    "    cpu_cluster.wait_for_completion(show_output=True)\n",
+    "    \n",
+    "# AmlCompute\n",
+    "gpu_cluster_name = \"gpucluster\"\n",
+    "try:\n",
+    "    gpu_cluster = AmlCompute(ws, gpu_cluster_name)\n",
+    "    print(\"found existing cluster.\")\n",
+    "except:\n",
+    "    print(\"creating new cluster\")\n",
+    "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_NC6\",\n",
+    "                                                                    max_nodes = 3)\n",
+    "\n",
+    "    # create the cluster\n",
+    "    gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, provisioning_config)\n",
+    "    gpu_cluster.wait_for_completion(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Python Scripts\n",
+    "We use an edited version of `neural_style_mpi.py` (original is [here](https://github.com/pytorch/examples/blob/master/fast_neural_style/neural_style/neural_style_mpi.py)). Scripts to split and stitch the video are thin wrappers to calls to `ffmpeg`. \n",
+    "\n",
+    "We install `ffmpeg` through conda dependencies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "shutil.copy(\"neural_style_mpi.py\", scripts_folder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile $scripts_folder/process_video.py\n",
+    "import argparse\n",
+    "import glob\n",
+    "import os\n",
+    "import subprocess\n",
+    "\n",
+    "parser = argparse.ArgumentParser(description=\"Process input video\")\n",
+    "parser.add_argument('--input_video', required=True)\n",
+    "parser.add_argument('--output_audio', required=True)\n",
+    "parser.add_argument('--output_images', required=True)\n",
+    "\n",
+    "args = parser.parse_args()\n",
+    "\n",
+    "os.makedirs(args.output_audio, exist_ok=True)\n",
+    "os.makedirs(args.output_images, exist_ok=True)\n",
+    "\n",
+    "subprocess.run(\"ffmpeg -i {} {}/video.aac\"\n",
+    "              .format(args.input_video, args.output_audio),\n",
+    "               shell=True, check=True\n",
+    "              )\n",
+    "\n",
+    "subprocess.run(\"ffmpeg -i {} {}/%05d_video.jpg -hide_banner\"\n",
+    "              .format(args.input_video, args.output_images),\n",
+    "               shell=True, check=True\n",
+    "              )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile $scripts_folder/stitch_video.py\n",
+    "import argparse\n",
+    "import os\n",
+    "import subprocess\n",
+    "\n",
+    "parser = argparse.ArgumentParser(description=\"Process input video\")\n",
+    "parser.add_argument('--images_dir', required=True)\n",
+    "parser.add_argument('--input_audio', required=True)\n",
+    "parser.add_argument('--output_dir', required=True)\n",
+    "\n",
+    "args = parser.parse_args()\n",
+    "\n",
+    "os.makedirs(args.output_dir, exist_ok=True)\n",
+    "\n",
+    "subprocess.run(\"ffmpeg -framerate 30 -i {}/%05d_video.jpg -c:v libx264 -profile:v high -crf 20 -pix_fmt yuv420p \"\n",
+    "               \"-y {}/video_without_audio.mp4\"\n",
+    "               .format(args.images_dir, args.output_dir),\n",
+    "               shell=True, check=True\n",
+    "              )\n",
+    "\n",
+    "subprocess.run(\"ffmpeg -i {}/video_without_audio.mp4 -i {}/video.aac -map 0:0 -map 1:0 -vcodec \"\n",
+    "               \"copy -acodec copy -y {}/video_with_audio.mp4\"\n",
+    "               .format(args.output_dir, args.input_audio, args.output_dir),\n",
+    "               shell=True, check=True\n",
+    "              )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# datastore for input video\n",
+    "account_name = \"happypathspublic\"\n",
+    "video_ds = Datastore.register_azure_blob_container(ws, \"videos\", \"videos\",\n",
+    "                                            account_name=account_name, overwrite=True)\n",
+    "\n",
+    "# datastore for models\n",
+    "models_ds = Datastore.register_azure_blob_container(ws, \"models\", \"styletransfer\", \n",
+    "                                                        account_name=\"pipelinedata\", \n",
+    "                                                        overwrite=True)\n",
+    "                                                        \n",
+    "# downloaded models from https://pytorch.org/tutorials/advanced/neural_style_tutorial.html are kept here\n",
+    "models_dir = DataReference(data_reference_name=\"models\", datastore=models_ds, \n",
+    "                           path_on_datastore=\"saved_models\", mode=\"download\")\n",
+    "\n",
+    "# the default blob store attached to a workspace\n",
+    "default_datastore = ws.get_default_datastore()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sample video"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orangutan_video = DataReference(datastore=video_ds,\n",
+    "                            data_reference_name=\"video\",\n",
+    "                            path_on_datastore=\"orangutan.mp4\", mode=\"download\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd = CondaDependencies()\n",
+    "\n",
+    "cd.add_channel(\"conda-forge\")\n",
+    "cd.add_conda_package(\"ffmpeg\")\n",
+    "\n",
+    "cd.add_channel(\"pytorch\")\n",
+    "cd.add_conda_package(\"pytorch\")\n",
+    "cd.add_conda_package(\"torchvision\")\n",
+    "\n",
+    "# Runconfig\n",
+    "amlcompute_run_config = RunConfiguration(conda_dependencies=cd)\n",
+    "amlcompute_run_config.environment.docker.enabled = True\n",
+    "amlcompute_run_config.environment.docker.gpu_support = True\n",
+    "amlcompute_run_config.environment.docker.base_image = \"pytorch/pytorch\"\n",
+    "amlcompute_run_config.environment.spark.precache_packages = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ffmpeg_audio = PipelineData(name=\"ffmpeg_audio\", datastore=default_datastore)\n",
+    "ffmpeg_images = PipelineData(name=\"ffmpeg_images\", datastore=default_datastore)\n",
+    "processed_images = PipelineData(name=\"processed_images\", datastore=default_datastore)\n",
+    "output_video = PipelineData(name=\"output_video\", datastore=default_datastore)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Define tweakable parameters to pipeline\n",
+    "These parameters can be changed when the pipeline is published and rerun from a REST call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.pipeline.core.graph import PipelineParameter\n",
+    "# create a parameter for style (one of \"candy\", \"mosaic\", \"rain_princess\", \"udnie\") to transfer the images to\n",
+    "style_param = PipelineParameter(name=\"style\", default_value=\"mosaic\")\n",
+    "# create a parameter for the number of nodes to use in step no. 2 (style transfer)\n",
+    "nodecount_param = PipelineParameter(name=\"nodecount\", default_value=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "split_video_step = PythonScriptStep(\n",
+    "    name=\"split video\",\n",
+    "    script_name=\"process_video.py\",\n",
+    "    arguments=[\"--input_video\", orangutan_video,\n",
+    "               \"--output_audio\", ffmpeg_audio,\n",
+    "               \"--output_images\", ffmpeg_images,\n",
+    "              ],\n",
+    "    compute_target=cpu_cluster,\n",
+    "    inputs=[orangutan_video],\n",
+    "    outputs=[ffmpeg_images, ffmpeg_audio],\n",
+    "    runconfig=amlcompute_run_config,\n",
+    "    source_directory=scripts_folder\n",
+    ")\n",
+    "\n",
+    "# create a MPI step for distributing style transfer step across multiple nodes in AmlCompute \n",
+    "# using 'nodecount_param' PipelineParameter\n",
+    "distributed_style_transfer_step = MpiStep(\n",
+    "    name=\"mpi style transfer\",\n",
+    "    script_name=\"neural_style_mpi.py\",\n",
+    "    arguments=[\"--content-dir\", ffmpeg_images,\n",
+    "               \"--output-dir\", processed_images,\n",
+    "               \"--model-dir\", models_dir,\n",
+    "               \"--style\", style_param,\n",
+    "               \"--cuda\", 1\n",
+    "              ],\n",
+    "    compute_target=gpu_cluster,\n",
+    "    node_count=nodecount_param, \n",
+    "    process_count_per_node=1,\n",
+    "    inputs=[models_dir, ffmpeg_images],\n",
+    "    outputs=[processed_images],\n",
+    "    pip_packages=[\"mpi4py\", \"torch\", \"torchvision\"],\n",
+    "    runconfig=amlcompute_run_config,\n",
+    "    use_gpu=True,\n",
+    "    source_directory=scripts_folder\n",
+    ")\n",
+    "\n",
+    "stitch_video_step = PythonScriptStep(\n",
+    "    name=\"stitch\",\n",
+    "    script_name=\"stitch_video.py\",\n",
+    "    arguments=[\"--images_dir\", processed_images, \n",
+    "               \"--input_audio\", ffmpeg_audio, \n",
+    "               \"--output_dir\", output_video],\n",
+    "    compute_target=cpu_cluster,\n",
+    "    inputs=[processed_images, ffmpeg_audio],\n",
+    "    outputs=[output_video],\n",
+    "    runconfig=amlcompute_run_config,\n",
+    "    source_directory=scripts_folder\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Run the pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipeline = Pipeline(workspace=ws, steps=[stitch_video_step])\n",
+    "# submit the pipeline and provide values for the PipelineParameters used in the pipeline\n",
+    "pipeline_run = Experiment(ws, 'style_transfer').submit(pipeline, pipeline_params={\"style\": \"mosaic\", \"nodecount\": 3})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Monitor using widget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(pipeline_run).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Downloads the video in `output_video` folder"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Download output video"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def download_video(run, target_dir=None):\n",
+    "    stitch_run = run.find_step_run(\"stitch\")[0]\n",
+    "    port_data = stitch_run.get_output_data(\"output_video\")\n",
+    "    port_data.download(target_dir, show_progress=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipeline_run.wait_for_completion()\n",
+    "download_video(pipeline_run, \"output_video_mosaic\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Publish pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "published_pipeline = pipeline_run.publish_pipeline(\n",
+    "    name=\"batch score style transfer\", description=\"style transfer\", version=\"1.0\")\n",
+    "\n",
+    "published_id = published_pipeline.id"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#  Re-run pipeline through REST calls for other styles"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get AAD token"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.authentication import AzureCliAuthentication\n",
+    "import requests\n",
+    "\n",
+    "cli_auth = AzureCliAuthentication()\n",
+    "aad_token = cli_auth.get_authentication_header()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get endpoint URL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rest_endpoint = published_pipeline.endpoint"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Send request and monitor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run the pipeline using PipelineParameter values style='candy' and nodecount=2\n",
+    "response = requests.post(rest_endpoint, \n",
+    "                         headers=aad_token,\n",
+    "                         json={\"ExperimentName\": \"style_transfer\",\n",
+    "                               \"ParameterAssignments\": {\"style\": \"candy\", \"nodecount\": 2}})                         \n",
+    "run_id = response.json()[\"Id\"]\n",
+    "\n",
+    "from azureml.pipeline.core.run import PipelineRun\n",
+    "published_pipeline_run_candy = PipelineRun(ws.experiments[\"style_transfer\"], run_id)\n",
+    "\n",
+    "RunDetails(published_pipeline_run_candy).show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run the pipeline using PipelineParameter values style='rain_princess' and nodecount=3\n",
+    "response = requests.post(rest_endpoint, \n",
+    "                         headers=aad_token,\n",
+    "                         json={\"ExperimentName\": \"style_transfer\",\n",
+    "                               \"ParameterAssignments\": {\"style\": \"rain_princess\", \"nodecount\": 3}})    \n",
+    "run_id = response.json()[\"Id\"]\n",
+    "\n",
+    "from azureml.pipeline.core.run import PipelineRun\n",
+    "published_pipeline_run_rain = PipelineRun(ws.experiments[\"style_transfer\"], run_id)\n",
+    "\n",
+    "RunDetails(published_pipeline_run_rain).show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run the pipeline using PipelineParameter values style='udnie' and nodecount=4\n",
+    "response = requests.post(rest_endpoint, \n",
+    "                         headers=aad_token,\n",
+    "                         json={\"ExperimentName\": \"style_transfer\",\n",
+    "                               \"ParameterAssignments\": {\"style\": \"udnie\", \"nodecount\": 4}})   \n",
+    "run_id = response.json()[\"Id\"]\n",
+    "\n",
+    "from azureml.pipeline.core.run import PipelineRun\n",
+    "published_pipeline_run_udnie = PipelineRun(ws.experiments[\"style_transfer\"], run_id)\n",
+    "\n",
+    "RunDetails(published_pipeline_run_udnie).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download output from re-run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "published_pipeline_run_candy.wait_for_completion()\n",
+    "published_pipeline_run_rain.wait_for_completion()\n",
+    "published_pipeline_run_udnie.wait_for_completion()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "download_video(published_pipeline_run_candy, target_dir=\"output_video_candy\")\n",
+    "download_video(published_pipeline_run_rain, target_dir=\"output_video_rain_princess\")\n",
+    "download_video(published_pipeline_run_udnie, target_dir=\"output_video_udnie\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "hichando"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

pipeline/test_adla_script.usql

@@ -0,0 +1,12 @@
+CREATE DATABASE IF NOT EXISTS oneboxtest01;
+
+
+@resourcereader =
+    EXTRACT query string
+    FROM "@@script_input@@"
+USING Extractors.Csv();
+
+
+OUTPUT @resourcereader
+TO "@@script_output@@"
+USING Outputters.Csv();

pipeline/testdata.txt

@@ -0,0 +1 @@
+Test1

pipeline/train-db-dbfs.py

@@ -0,0 +1,5 @@
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+print("In train.py")
+print("As a data scientist, this is where I use my training code.")

pipeline/train-db-local.py

@@ -0,0 +1,5 @@
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+print("In train.py")
+print("As a data scientist, this is where I use my training code.")

pipeline/train.py

@@ -0,0 +1,22 @@
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+import argparse
+import os
+
+print("In train.py")
+print("As a data scientist, this is where I use my training code.")
+
+parser = argparse.ArgumentParser("train")
+
+parser.add_argument("--input_data", type=str, help="input data")
+parser.add_argument("--output_train", type=str, help="output_train directory")
+
+args = parser.parse_args()
+
+print("Argument 1: %s" % args.input_data)
+print("Argument 2: %s" % args.output_train)
+
+if not (args.output_train is None):
+    os.makedirs(args.output_train, exist_ok=True)
+    print("%s created" % args.output_train)

pr.md

@@ -12,6 +12,18 @@
 ## Community Blogs
 - [Power Bat – How Spektacom is Powering the Game of Cricket with Microsoft AI](https://blogs.technet.microsoft.com/machinelearning/2018/10/11/power-bat-how-spektacom-is-powering-the-game-of-cricket-with-microsoft-ai/)
 
+## Ignite 2018 Public Preview Launch Sessions
+- [AI with Azure Machine Learning services: Simplifying the data science process](https://myignite.techcommunity.microsoft.com/sessions/66248)
+- [AI TechTalk: Azure Machine Learning SDK - a walkthrough](https://myignite.techcommunity.microsoft.com/sessions/66265) 
+- [AI for an intelligent cloud and intelligent edge: Discover, deploy, and manage with Azure ML services](https://myignite.techcommunity.microsoft.com/sessions/65389)
+- [Generating high quality models efficiently using Automated ML and Hyperparameter Tuning](https://myignite.techcommunity.microsoft.com/sessions/66245)
+- [AI for pros: Deep learning with PyTorch using the Azure Data Science Virtual Machine and scaling training with Azure ML](https://myignite.techcommunity.microsoft.com/sessions/66244)
+
+## Get-started Videos on YouTube
+- [Get started with Python SDK](https://youtu.be/VIsXeTuW3FU)
+- [Get started from Azure Portal](https://youtu.be/lCkYUHV86Mk)
+
+
 ## Third Party Articles
 - [Azure’s new machine learning features embrace Python](https://www.infoworld.com/article/3306840/azure/azures-new-machine-learning-features-embrace-python.html) (InfoWorld)
 - [How to use Azure ML in Windows 10](https://www.infoworld.com/article/3308381/azure/how-to-use-azure-ml-in-windows-10.html) (InfoWorld)
@@ -24,7 +36,7 @@
 ## Community Projects
 - [Fashion MNIST](https://github.com/amynic/azureml-sdk-fashion)
 - Keras on Databricks
-- Samples from CSS
+- [Samples from CSS](https://github.com/Azure/AMLSamples)
 
 
 ## Azure Machine Learning Studio Resources

project-brainwave/project-brainwave-quickstart.ipynb

@@ -1,314 +1,312 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Azure ML Hardware Accelerated Models Quickstart"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This tutorial will show you how to deploy an image recognition service based on the ResNet 50 classifier in just a few minutes using the Azure Machine Learning Accelerated AI service.  Get more help from our [documentation](https://aka.ms/aml-real-time-ai) or [forum](https://aka.ms/aml-forum).\n",
-        "\n",
-        "We will use an accelerated ResNet50 featurizer running on an FPGA. This functionality is powered by Project Brainwave, which handles translating deep neural networks (DNN) into an FPGA program.\n",
-        "\n",
-        "## Request Quota\n",
-        "**IMPORTANT:** You must [request quota](https://aka.ms/aml-real-time-ai-request) and be approved before you can successfully run this notebook. Notebook 00 will show you how to create a workspace which you can use to request quota."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Imports"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "import tensorflow as tf"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Image preprocessing\n",
-        "We'd like our service to accept JPEG images as input. However the input to ResNet50 is a tensor. So we need code that decodes JPEG images and does the preprocessing required by ResNet50. The Accelerated AI service can execute TensorFlow graphs as part of the service and we'll use that ability to do the image preprocessing. This code defines a TensorFlow graph that preprocesses an array of JPEG images (as strings) and produces a tensor that is ready to be featurized by ResNet50."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Input images as a two-dimensional tensor containing an arbitrary number of images represented a strings\n",
-        "import azureml.contrib.brainwave.models.utils as utils\n",
-        "in_images = tf.placeholder(tf.string)\n",
-        "image_tensors = utils.preprocess_array(in_images)\n",
-        "print(image_tensors.shape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Featurizer\n",
-        "We use ResNet50 as a featurizer. In this step we initialize the model. This downloads a TensorFlow checkpoint of the quantized ResNet50."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.contrib.brainwave.models import QuantizedResnet50, Resnet50\n",
-        "model_path = os.path.expanduser('~/models')\n",
-        "model = QuantizedResnet50(model_path, is_frozen = True)\n",
-        "feature_tensor = model.import_graph_def(image_tensors)\n",
-        "print(model.version)\n",
-        "print(feature_tensor.name)\n",
-        "print(feature_tensor.shape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Classifier\n",
-        "The model we downloaded includes a classifier which takes the output of the ResNet50 and identifies an image. This classifier is trained on the ImageNet dataset. We are going to use this classifier for our service. The next [notebook](project-brainwave-trainsfer-learning.ipynb) shows how to train a classifier for a different data set. The input to the classifier is a tensor matching the output of our ResNet50 featurizer."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "classifier_output = model.get_default_classifier(feature_tensor)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Service Definition\n",
-        "Now that we've definied the image preprocessing, featurizer, and classifier that we will execute on our service we can create a service definition. The service definition is a set of files generated from the model that allow us to deploy to the FPGA service. The service definition consists of a pipeline. The pipeline is a series of stages that are executed in order. We support TensorFlow stages, Keras stages, and BrainWave stages. The stages will be executed in order on the service, with the output of each stage input into the subsequent stage.\n",
-        "\n",
-        "To create a TensorFlow stage we specify a session containing the graph (in this case we are using the default graph) and the input and output tensors to this stage. We use this information to save the graph so that we can execute it on the service."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.contrib.brainwave.pipeline import ModelDefinition, TensorflowStage, BrainWaveStage\n",
-        "\n",
-        "save_path = os.path.expanduser('~/models/save')\n",
-        "model_def_path = os.path.join(save_path, 'model_def.zip')\n",
-        "\n",
-        "model_def = ModelDefinition()\n",
-        "with tf.Session() as sess:\n",
-        "    model_def.pipeline.append(TensorflowStage(sess, in_images, image_tensors))\n",
-        "    model_def.pipeline.append(BrainWaveStage(sess, model))\n",
-        "    model_def.pipeline.append(TensorflowStage(sess, feature_tensor, classifier_output))\n",
-        "    model_def.save(model_def_path)\n",
-        "    print(model_def_path)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Deploy\n",
-        "Time to create a service from the service definition. You need a Workspace in the **East US 2** location. In the previous notebooks, you've created this Workspace. The code below will load that Workspace from a configuration file."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Upload the model to the workspace."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.model import Model\n",
-        "model_name = \"resnet-50-rtai\"\n",
-        "registered_model = Model.register(ws, model_def_path, model_name)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Create a service from the model that we registered. If this is a new service then we create it. If you already have a service with this name then the existing service will be updated to use this model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import Webservice\n",
-        "from azureml.exceptions import WebserviceException\n",
-        "from azureml.contrib.brainwave import BrainwaveWebservice, BrainwaveImage\n",
-        "service_name = \"imagenet-infer\"\n",
-        "service = None\n",
-        "try:\n",
-        "    service = Webservice(ws, service_name)\n",
-        "except WebserviceException:\n",
-        "    image_config = BrainwaveImage.image_configuration()\n",
-        "    deployment_config = BrainwaveWebservice.deploy_configuration()\n",
-        "    service = Webservice.deploy_from_model(ws, service_name, [registered_model], image_config, deployment_config)\n",
-        "    service.wait_for_deployment(true)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Client\n",
-        "The service supports gRPC and the TensorFlow Serving \"predict\" API. We provide a client that can call the service to get predictions on aka.ms/rtai. You can also invoke the service like any other web service."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To understand the results we need a mapping to the human readable imagenet classes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import requests\n",
-        "classes_entries = requests.get(\"https://raw.githubusercontent.com/Lasagne/Recipes/master/examples/resnet50/imagenet_classes.txt\").text.splitlines()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can now send an image to the service and get the predictions. Let's see if it can identify a snow leopard.\n",
-        "![title](snowleopardgaze.jpg)\n",
-        "Snow leopard in a zoo. Photo by Peter Bolliger.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "results = service.run('snowleopardgaze.jpg')\n",
-        "# map results [class_id] => [confidence]\n",
-        "results = enumerate(results)\n",
-        "# sort results by confidence\n",
-        "sorted_results = sorted(results, key=lambda x: x[1], reverse=True)\n",
-        "# print top 5 results\n",
-        "for top in sorted_results[:5]:\n",
-        "    print(classes_entries[top[0]], 'confidence:', top[1])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Cleanup\n",
-        "Run the cell below to delete your service."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "service.delete()\n",
-        "    \n",
-        "registered_model.delete()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Congratulations! You've just created a service that does predictions using an FPGA. The next [notebook](project-brainwave-trainsfer-learning.ipynb) shows how to customize the service using transfer learning to classify different types of images."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "coverste"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.5.2"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Azure ML Hardware Accelerated Models Quickstart"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This tutorial will show you how to deploy an image recognition service based on the ResNet 50 classifier in just a few minutes using the Azure Machine Learning Accelerated AI service.  Get more help from our [documentation](https://aka.ms/aml-real-time-ai) or [forum](https://aka.ms/aml-forum).\n",
+    "\n",
+    "We will use an accelerated ResNet50 featurizer running on an FPGA. This functionality is powered by Project Brainwave, which handles translating deep neural networks (DNN) into an FPGA program.\n",
+    "\n",
+    "## Request Quota\n",
+    "**IMPORTANT:** You must [request quota](https://aka.ms/aml-real-time-ai-request) and be approved before you can successfully run this notebook. Notebook 00 will show you how to create a workspace which you can use to request quota."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Image preprocessing\n",
+    "We'd like our service to accept JPEG images as input. However the input to ResNet50 is a tensor. So we need code that decodes JPEG images and does the preprocessing required by ResNet50. The Accelerated AI service can execute TensorFlow graphs as part of the service and we'll use that ability to do the image preprocessing. This code defines a TensorFlow graph that preprocesses an array of JPEG images (as strings) and produces a tensor that is ready to be featurized by ResNet50."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Input images as a two-dimensional tensor containing an arbitrary number of images represented a strings\n",
+    "import azureml.contrib.brainwave.models.utils as utils\n",
+    "in_images = tf.placeholder(tf.string)\n",
+    "image_tensors = utils.preprocess_array(in_images)\n",
+    "print(image_tensors.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Featurizer\n",
+    "We use ResNet50 as a featurizer. In this step we initialize the model. This downloads a TensorFlow checkpoint of the quantized ResNet50."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.contrib.brainwave.models import QuantizedResnet50\n",
+    "model_path = os.path.expanduser('~/models')\n",
+    "model = QuantizedResnet50(model_path, is_frozen = True)\n",
+    "feature_tensor = model.import_graph_def(image_tensors)\n",
+    "print(model.version)\n",
+    "print(feature_tensor.name)\n",
+    "print(feature_tensor.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Classifier\n",
+    "The model we downloaded includes a classifier which takes the output of the ResNet50 and identifies an image. This classifier is trained on the ImageNet dataset. We are going to use this classifier for our service. The next [notebook](project-brainwave-trainsfer-learning.ipynb) shows how to train a classifier for a different data set. The input to the classifier is a tensor matching the output of our ResNet50 featurizer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classifier_output = model.get_default_classifier(feature_tensor)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Service Definition\n",
+    "Now that we've definied the image preprocessing, featurizer, and classifier that we will execute on our service we can create a service definition. The service definition is a set of files generated from the model that allow us to deploy to the FPGA service. The service definition consists of a pipeline. The pipeline is a series of stages that are executed in order. We support TensorFlow stages, Keras stages, and BrainWave stages. The stages will be executed in order on the service, with the output of each stage input into the subsequent stage.\n",
+    "\n",
+    "To create a TensorFlow stage we specify a session containing the graph (in this case we are using the default graph) and the input and output tensors to this stage. We use this information to save the graph so that we can execute it on the service."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.contrib.brainwave.pipeline import ModelDefinition, TensorflowStage, BrainWaveStage\n",
+    "\n",
+    "save_path = os.path.expanduser('~/models/save')\n",
+    "model_def_path = os.path.join(save_path, 'model_def.zip')\n",
+    "\n",
+    "model_def = ModelDefinition()\n",
+    "with tf.Session() as sess:\n",
+    "    model_def.pipeline.append(TensorflowStage(sess, in_images, image_tensors))\n",
+    "    model_def.pipeline.append(BrainWaveStage(sess, model))\n",
+    "    model_def.pipeline.append(TensorflowStage(sess, feature_tensor, classifier_output))\n",
+    "    model_def.save(model_def_path)\n",
+    "    print(model_def_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deploy\n",
+    "Time to create a service from the service definition. You need a Workspace in the **East US 2** location. In the previous notebooks, you've created this Workspace. The code below will load that Workspace from a configuration file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Upload the model to the workspace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.model import Model\n",
+    "model_name = \"resnet-50-rtai\"\n",
+    "registered_model = Model.register(ws, model_def_path, model_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a service from the model that we registered. If this is a new service then we create it. If you already have a service with this name then the existing service will be updated to use this model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.webservice import Webservice\n",
+    "from azureml.exceptions import WebserviceException\n",
+    "from azureml.contrib.brainwave import BrainwaveWebservice, BrainwaveImage\n",
+    "service_name = \"imagenet-infer\"\n",
+    "service = None\n",
+    "try:\n",
+    "    service = Webservice(ws, service_name)\n",
+    "except WebserviceException:\n",
+    "    image_config = BrainwaveImage.image_configuration()\n",
+    "    deployment_config = BrainwaveWebservice.deploy_configuration()\n",
+    "    service = Webservice.deploy_from_model(ws, service_name, [registered_model], image_config, deployment_config)\n",
+    "    service.wait_for_deployment(True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Client\n",
+    "The service supports gRPC and the TensorFlow Serving \"predict\" API. We provide a client that can call the service to get predictions on aka.ms/rtai. You can also invoke the service like any other web service."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To understand the results we need a mapping to the human readable imagenet classes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "classes_entries = requests.get(\"https://raw.githubusercontent.com/Lasagne/Recipes/master/examples/resnet50/imagenet_classes.txt\").text.splitlines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now send an image to the service and get the predictions. Let's see if it can identify a snow leopard.\n",
+    "![title](snowleopardgaze.jpg)\n",
+    "Snow leopard in a zoo. Photo by Peter Bolliger.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = service.run('snowleopardgaze.jpg')\n",
+    "# map results [class_id] => [confidence]\n",
+    "results = enumerate(results)\n",
+    "# sort results by confidence\n",
+    "sorted_results = sorted(results, key=lambda x: x[1], reverse=True)\n",
+    "# print top 5 results\n",
+    "for top in sorted_results[:5]:\n",
+    "    print(classes_entries[top[0]], 'confidence:', top[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleanup\n",
+    "Run the cell below to delete your service."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "service.delete()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Congratulations! You've just created a service that does predictions using an FPGA. The next [notebook](project-brainwave-trainsfer-learning.ipynb) shows how to customize the service using transfer learning to classify different types of images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "coverste"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

training/01.train-hyperparameter-tune-deploy-with-pytorch/pytorch_score.py

@@ -5,35 +5,10 @@ import torch
 import torch.nn as nn
 from torchvision import transforms
 import json
-import base64
-from io import BytesIO
-from PIL import Image
 
 from azureml.core.model import Model
 
 
-def preprocess_image(image_file):
-    """Preprocess the input image."""
-    data_transforms = transforms.Compose([
-        transforms.Resize(256),
-        transforms.CenterCrop(224),
-        transforms.ToTensor(),
-        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-    ])
-
-    image = Image.open(image_file)
-    image = data_transforms(image).float()
-    image = torch.tensor(image)
-    image = image.unsqueeze(0)
-    return image
-
-
-def base64ToImg(base64ImgString):
-    base64Img = base64ImgString.encode('utf-8')
-    decoded_img = base64.b64decode(base64Img)
-    return BytesIO(decoded_img)
-
-
 def init():
     global model
     model_path = Model.get_model_path('pytorch-hymenoptera')
@@ -42,16 +17,15 @@ def init():
 
 
 def run(input_data):
-    img = base64ToImg(json.loads(input_data)['data'])
-    img = preprocess_image(img)
+    input_data = torch.tensor(json.loads(input_data)['data'])
 
     # get prediction
-    output = model(img)
+    with torch.no_grad():
+        output = model(input_data)
+        classes = ['ants', 'bees']
+        softmax = nn.Softmax(dim=1)
+        pred_probs = softmax(output).numpy()[0]
+        index = torch.argmax(output, 1)
 
-    classes = ['ants', 'bees']
-    softmax = nn.Softmax(dim=1)
-    pred_probs = softmax(model(img)).detach().numpy()[0]
-    index = torch.argmax(output, 1)
-
-    result = json.dumps({"label": classes[index], "probability": str(pred_probs[index])})
+    result = {"label": classes[index], "probability": str(pred_probs[index])}
     return result

training/01.train-hyperparameter-tune-deploy-with-pytorch/pytorch_train.py

@@ -59,6 +59,7 @@ def train_model(model, criterion, optimizer, scheduler, num_epochs, data_dir):
     dataloaders, dataset_sizes, class_names = load_data(data_dir)
 
     device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+
     since = time.time()
 
     best_model_wts = copy.deepcopy(model.state_dict())
@@ -146,12 +147,15 @@ def fine_tune_model(num_epochs, data_dir, learning_rate, momentum):
     criterion = nn.CrossEntropyLoss()
 
     # Observe that all parameters are being optimized
-    optimizer_ft = optim.SGD(model_ft.parameters(), lr=learning_rate, momentum=momentum)
+    optimizer_ft = optim.SGD(model_ft.parameters(),
+                             lr=learning_rate, momentum=momentum)
 
     # Decay LR by a factor of 0.1 every 7 epochs
-    exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
+    exp_lr_scheduler = lr_scheduler.StepLR(
+        optimizer_ft, step_size=7, gamma=0.1)
 
-    model = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs, data_dir)
+    model = train_model(model_ft, criterion, optimizer_ft,
+                        exp_lr_scheduler, num_epochs, data_dir)
 
     return model
 
@@ -159,15 +163,19 @@ def fine_tune_model(num_epochs, data_dir, learning_rate, momentum):
 def main():
     # get command-line arguments
     parser = argparse.ArgumentParser()
-    parser.add_argument('--data_dir', type=str, help='directory of training data')
-    parser.add_argument('--num_epochs', type=int, default=25, help='number of epochs to train')
+    parser.add_argument('--data_dir', type=str,
+                        help='directory of training data')
+    parser.add_argument('--num_epochs', type=int, default=25,
+                        help='number of epochs to train')
     parser.add_argument('--output_dir', type=str, help='output directory')
-    parser.add_argument('--learning_rate', type=float, default=0.001, help='learning rate')
+    parser.add_argument('--learning_rate', type=float,
+                        default=0.001, help='learning rate')
     parser.add_argument('--momentum', type=float, default=0.9, help='momentum')
     args = parser.parse_args()
 
     print("data directory is: " + args.data_dir)
-    model = fine_tune_model(args.num_epochs, args.data_dir, args.learning_rate, args.momentum)
+    model = fine_tune_model(args.num_epochs, args.data_dir,
+                            args.learning_rate, args.momentum)
     os.makedirs(args.output_dir, exist_ok=True)
     torch.save(model, os.path.join(args.output_dir, 'model.pt'))
 

training/02.distributed-pytorch-with-horovod/02.distributed-pytorch-with-horovod.ipynb

@@ -1,315 +1,313 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 02. Distributed PyTorch with Horovod\n",
-        "In this tutorial, you will train a PyTorch model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using distributed training via [Horovod](https://github.com/uber/horovod)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning (AML)\n",
-        "* Go through the [00.configuration.ipynb](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) notebook to:\n",
-        "    * install the AML SDK\n",
-        "    * create a workspace and its configuration file (`config.json`)\n",
-        "* Review the [tutorial](https://aka.ms/aml-notebook-pytorch) on single-node PyTorch training using the SDK"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Check core SDK version number\n",
-        "import azureml.core\n",
-        "\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Diagnostics\n",
-        "Opt-in diagnostics for better experience, quality, and security of future releases."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "Diagnostics"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.telemetry import set_diagnostics_collection\n",
-        "set_diagnostics_collection(send_diagnostics = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize workspace\n",
-        "\n",
-        "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.workspace import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print('Workspace name: ' + ws.name, \n",
-        "      'Azure region: ' + ws.location, \n",
-        "      'Subscription id: ' + ws.subscription_id, \n",
-        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create a remote compute target\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an [Azure Batch AI](https://docs.microsoft.com/azure/batch-ai/overview) cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n",
-        "\n",
-        "**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import ComputeTarget, BatchAiCompute\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "# choose a name for your cluster\n",
-        "cluster_name = \"gpucluster\"\n",
-        "\n",
-        "try:\n",
-        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
-        "    print('Found existing compute target.')\n",
-        "except ComputeTargetException:\n",
-        "    print('Creating a new compute target...')\n",
-        "    compute_config = BatchAiCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
-        "                                                                autoscale_enabled=True,\n",
-        "                                                                cluster_min_nodes=0, \n",
-        "                                                                cluster_max_nodes=4)\n",
-        "\n",
-        "    # create the cluster\n",
-        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
-        "\n",
-        "    compute_target.wait_for_completion(show_output=True)\n",
-        "\n",
-        "    # Use the 'status' property to get a detailed status for the current cluster. \n",
-        "    print(compute_target.status.serialize())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train model on the remote compute\n",
-        "Now that we have the cluster ready to go, let's run our distributed training job."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create a project directory\n",
-        "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "project_folder = './pytorch-distr-hvd'\n",
-        "os.makedirs(project_folder, exist_ok=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copy the training script `pytorch_horovod_mnist.py` into this project directory."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import shutil\n",
-        "shutil.copy('pytorch_horovod_mnist.py', project_folder)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create an experiment\n",
-        "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed PyTorch tutorial. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Experiment\n",
-        "\n",
-        "experiment_name = 'pytorch-distr-hvd'\n",
-        "experiment = Experiment(ws, name=experiment_name)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create a PyTorch estimator\n",
-        "The AML SDK's PyTorch estimator enables you to easily submit PyTorch training jobs for both single-node and distributed runs. For more information on the PyTorch estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-pytorch)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.dnn import PyTorch\n",
-        "\n",
-        "estimator = PyTorch(source_directory=project_folder,\n",
-        "                    compute_target=compute_target,\n",
-        "                    entry_script='pytorch_horovod_mnist.py',\n",
-        "                    node_count=2,\n",
-        "                    process_count_per_node=1,\n",
-        "                    distributed_backend='mpi',\n",
-        "                    use_gpu=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The above code specifies that we will run our training script on `2` nodes, with one worker per node. In order to execute a distributed run using MPI/Horovod, you must provide the argument `distributed_backend='mpi'`. Using this estimator with these settings, PyTorch, Horovod and their dependencies will be installed for you. However, if your script also uses other packages, make sure to install them via the `PyTorch` constructor's `pip_packages` or `conda_packages` parameters."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Submit job\n",
-        "Run your experiment by submitting your estimator object. Note that this call is asynchronous."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run = experiment.submit(estimator)\n",
-        "print(run.get_details())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Monitor your run\n",
-        "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.widgets import RunDetails\n",
-        "RunDetails(run).show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Alternatively, you can block until the script has completed training before running more code."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run.wait_for_completion(show_output=True) # this provides a verbose log"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "minxia"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    },
-    "msauthor": "minxia"
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 02. Distributed PyTorch with Horovod\n",
+    "In this tutorial, you will train a PyTorch model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using distributed training via [Horovod](https://github.com/uber/horovod)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning (AML)\n",
+    "* Go through the [00.configuration.ipynb](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) notebook to:\n",
+    "    * install the AML SDK\n",
+    "    * create a workspace and its configuration file (`config.json`)\n",
+    "* Review the [tutorial](https://aka.ms/aml-notebook-pytorch) on single-node PyTorch training using the SDK"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check core SDK version number\n",
+    "import azureml.core\n",
+    "\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "Diagnostics"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize workspace\n",
+    "\n",
+    "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.workspace import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print('Workspace name: ' + ws.name, \n",
+    "      'Azure region: ' + ws.location, \n",
+    "      'Subscription id: ' + ws.subscription_id, \n",
+    "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a remote compute target\n",
+    "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an [Azure Batch AI](https://docs.microsoft.com/azure/batch-ai/overview) cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n",
+    "\n",
+    "**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+    "from azureml.core.compute_target import ComputeTargetException\n",
+    "\n",
+    "# choose a name for your cluster\n",
+    "cluster_name = \"gpucluster\"\n",
+    "\n",
+    "try:\n",
+    "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
+    "    print('Found existing compute target.')\n",
+    "except ComputeTargetException:\n",
+    "    print('Creating a new compute target...')\n",
+    "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
+    "                                                                max_nodes=6)\n",
+    "\n",
+    "    # create the cluster\n",
+    "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
+    "\n",
+    "compute_target.wait_for_completion(show_output=True)\n",
+    "\n",
+    "# Use the 'status' property to get a detailed status for the current cluster. \n",
+    "print(compute_target.status.serialize())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train model on the remote compute\n",
+    "Now that we have the cluster ready to go, let's run our distributed training job."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a project directory\n",
+    "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "project_folder = './pytorch-distr-hvd'\n",
+    "os.makedirs(project_folder, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copy the training script `pytorch_horovod_mnist.py` into this project directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "shutil.copy('pytorch_horovod_mnist.py', project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create an experiment\n",
+    "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed PyTorch tutorial. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Experiment\n",
+    "\n",
+    "experiment_name = 'pytorch-distr-hvd'\n",
+    "experiment = Experiment(ws, name=experiment_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a PyTorch estimator\n",
+    "The AML SDK's PyTorch estimator enables you to easily submit PyTorch training jobs for both single-node and distributed runs. For more information on the PyTorch estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-pytorch)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.train.dnn import PyTorch\n",
+    "\n",
+    "estimator = PyTorch(source_directory=project_folder,\n",
+    "                    compute_target=compute_target,\n",
+    "                    entry_script='pytorch_horovod_mnist.py',\n",
+    "                    node_count=2,\n",
+    "                    process_count_per_node=1,\n",
+    "                    distributed_backend='mpi',\n",
+    "                    use_gpu=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above code specifies that we will run our training script on `2` nodes, with one worker per node. In order to execute a distributed run using MPI/Horovod, you must provide the argument `distributed_backend='mpi'`. Using this estimator with these settings, PyTorch, Horovod and their dependencies will be installed for you. However, if your script also uses other packages, make sure to install them via the `PyTorch` constructor's `pip_packages` or `conda_packages` parameters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Submit job\n",
+    "Run your experiment by submitting your estimator object. Note that this call is asynchronous."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run = experiment.submit(estimator)\n",
+    "print(run.get_details())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Monitor your run\n",
+    "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(run).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, you can block until the script has completed training before running more code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run.wait_for_completion(show_output=True) # this provides a verbose log"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "minxia"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  },
+  "msauthor": "minxia"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

training/03.train-hyperparameter-tune-deploy-with-tensorflow/.gitignore

@@ -0,0 +1 @@
+/data/

training/03.train-hyperparameter-tune-deploy-with-tensorflow/tf_mnist.py

@@ -39,7 +39,7 @@ n_h1 = args.n_hidden_1
 n_h2 = args.n_hidden_2
 n_outputs = 10
 learning_rate = args.learning_rate
-n_epochs = 50
+n_epochs = 20
 batch_size = args.batch_size
 
 with tf.name_scope('network'):

training/04.distributed-tensorflow-with-horovod/.gitignore

@@ -0,0 +1,2 @@
+/data/
+/tf-distr-hvd/

training/04.distributed-tensorflow-with-horovod/04.distributed-tensorflow-with-horovod.ipynb

@@ -1,386 +1,400 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 04. Distributed Tensorflow with Horovod\n",
-        "In this tutorial, you will train a word2vec model in TensorFlow using distributed training via [Horovod](https://github.com/uber/horovod)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning (AML)\n",
-        "* Go through the [00.configuration.ipynb](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) notebook to:\n",
-        "    * install the AML SDK\n",
-        "    * create a workspace and its configuration file (`config.json`)\n",
-        "* Review the [tutorial](https://aka.ms/aml-notebook-hyperdrive) on single-node TensorFlow training using the SDK"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Check core SDK version number\n",
-        "import azureml.core\n",
-        "\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Diagnostics\n",
-        "Opt-in diagnostics for better experience, quality, and security of future releases."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "Diagnostics"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.telemetry import set_diagnostics_collection\n",
-        "set_diagnostics_collection(send_diagnostics = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize workspace\n",
-        "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.workspace import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print('Workspace name: ' + ws.name, \n",
-        "      'Azure region: ' + ws.location, \n",
-        "      'Subscription id: ' + ws.subscription_id, \n",
-        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create a remote compute target\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an [Azure Batch AI](https://docs.microsoft.com/azure/batch-ai/overview) cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n",
-        "\n",
-        "**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import ComputeTarget, BatchAiCompute\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "# choose a name for your cluster\n",
-        "cluster_name = \"gpucluster\"\n",
-        "\n",
-        "try:\n",
-        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
-        "    print('Found existing compute target')\n",
-        "except ComputeTargetException:\n",
-        "    print('Creating a new compute target...')\n",
-        "    compute_config = BatchAiCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
-        "                                                                autoscale_enabled=True,\n",
-        "                                                                cluster_min_nodes=0, \n",
-        "                                                                cluster_max_nodes=4)\n",
-        "\n",
-        "    # create the cluster\n",
-        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
-        "\n",
-        "    compute_target.wait_for_completion(show_output=True)\n",
-        "\n",
-        "    # Use the 'status' property to get a detailed status for the current cluster. \n",
-        "    print(compute_target.status.serialize())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Upload data to datastore\n",
-        "To make data accessible for remote training, AML provides a convenient way to do so via a [Datastore](https://docs.microsoft.com/azure/machine-learning/service/how-to-access-data). The datastore provides a mechanism for you to upload/download data to Azure Storage, and interact with it from your remote compute targets. \n",
-        "\n",
-        "If your data is already stored in Azure, or you download the data as part of your training script, you will not need to do this step. For this tutorial, although you can download the data in your training script, we will demonstrate how to upload the training data to a datastore and access it during training to illustrate the datastore functionality."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "First, download the training data from [here](http://mattmahoney.net/dc/text8.zip) to your local machine:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "import urllib\n",
-        "\n",
-        "os.makedirs('./data', exist_ok=True)\n",
-        "download_url = 'http://mattmahoney.net/dc/text8.zip'\n",
-        "urllib.request.urlretrieve(download_url, filename='./data/text8.zip')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Each workspace is associated with a default datastore. In this tutorial, we will upload the training data to this default datastore. The below code will upload the contents of the data directory to the path `./data` on the default datastore."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ds = ws.get_default_datastore()\n",
-        "print(ds.datastore_type, ds.account_name, ds.container_name)\n",
-        "\n",
-        "ds.upload(src_dir='data', target_path='data', overwrite=True, show_progress=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "For convenience, let's get a reference to the path on the datastore with the zip file of training data. We can do so using the `path` method. In the next section, we can then pass this reference to our training script's `--input_data` argument. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "path_on_datastore = 'data/text8.zip'\n",
-        "ds_data = ds.path(path_on_datastore)\n",
-        "print(ds_data)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train model on the remote compute"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create a project directory\n",
-        "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "project_folder = './tf-distr-hvd'\n",
-        "os.makedirs(project_folder, exist_ok=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copy the training script `tf_horovod_word2vec.py` into this project directory."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import shutil\n",
-        "shutil.copy('tf_horovod_word2vec.py', project_folder)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create an experiment\n",
-        "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed TensorFlow tutorial. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Experiment\n",
-        "\n",
-        "experiment_name = 'tf-distr-hvd'\n",
-        "experiment = Experiment(ws, name=experiment_name)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create a TensorFlow estimator\n",
-        "The AML SDK's TensorFlow estimator enables you to easily submit TensorFlow training jobs for both single-node and distributed runs. For more information on the TensorFlow estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-tensorflow)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.dnn import TensorFlow\n",
-        "\n",
-        "script_params={\n",
-        "    '--input_data': ds_data\n",
-        "}\n",
-        "\n",
-        "estimator= TensorFlow(source_directory=project_folder,\n",
-        "                      compute_target=compute_target,\n",
-        "                      script_params=script_params,\n",
-        "                      entry_script='tf_horovod_word2vec.py',\n",
-        "                      node_count=2,\n",
-        "                      process_count_per_node=1,\n",
-        "                      distributed_backend='mpi',\n",
-        "                      use_gpu=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The above code specifies that we will run our training script on `2` nodes, with one worker per node. In order to execute a distributed run using MPI/Horovod, you must provide the argument `distributed_backend='mpi'`. Using this estimator with these settings, TensorFlow, Horovod and their dependencies will be installed for you. However, if your script also uses other packages, make sure to install them via the `TensorFlow` constructor's `pip_packages` or `conda_packages` parameters.\n",
-        "\n",
-        "Note that we passed our training data reference `ds_data` to our script's `--input_data` argument. This will 1) mount our datastore on the remote compute and 2) provide the path to the data zip file on our datastore."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Submit job\n",
-        "Run your experiment by submitting your estimator object. Note that this call is asynchronous."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run = experiment.submit(estimator)\n",
-        "print(run)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Monitor your run\n",
-        "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.widgets import RunDetails\n",
-        "RunDetails(run).show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Alternatively, you can block until the script has completed training before running more code."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run.wait_for_completion(show_output=True)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "roastala"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    },
-    "msauthor": "minxia"
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 04. Distributed Tensorflow with Horovod\n",
+    "In this tutorial, you will train a word2vec model in TensorFlow using distributed training via [Horovod](https://github.com/uber/horovod)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning (AML)\n",
+    "* Go through the [00.configuration.ipynb](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) notebook to:\n",
+    "    * install the AML SDK\n",
+    "    * create a workspace and its configuration file (`config.json`)\n",
+    "* Review the [tutorial](https://aka.ms/aml-notebook-hyperdrive) on single-node TensorFlow training using the SDK"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check core SDK version number\n",
+    "import azureml.core\n",
+    "\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "Diagnostics"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize workspace\n",
+    "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.workspace import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print('Workspace name: ' + ws.name, \n",
+    "      'Azure region: ' + ws.location, \n",
+    "      'Subscription id: ' + ws.subscription_id, \n",
+    "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a remote compute target\n",
+    "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an [Azure Batch AI](https://docs.microsoft.com/azure/batch-ai/overview) cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n",
+    "\n",
+    "**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+    "from azureml.core.compute_target import ComputeTargetException\n",
+    "\n",
+    "# choose a name for your cluster\n",
+    "cluster_name = \"gpucluster\"\n",
+    "\n",
+    "try:\n",
+    "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
+    "    print('Found existing compute target')\n",
+    "except ComputeTargetException:\n",
+    "    print('Creating a new compute target...')\n",
+    "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
+    "                                                                max_nodes=6)\n",
+    "\n",
+    "    # create the cluster\n",
+    "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
+    "\n",
+    "compute_target.wait_for_completion(show_output=True)\n",
+    "\n",
+    "# Use the 'status' property to get a detailed status for the current cluster. \n",
+    "print(compute_target.status.serialize())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Upload data to datastore\n",
+    "To make data accessible for remote training, AML provides a convenient way to do so via a [Datastore](https://docs.microsoft.com/azure/machine-learning/service/how-to-access-data). The datastore provides a mechanism for you to upload/download data to Azure Storage, and interact with it from your remote compute targets. \n",
+    "\n",
+    "If your data is already stored in Azure, or you download the data as part of your training script, you will not need to do this step. For this tutorial, although you can download the data in your training script, we will demonstrate how to upload the training data to a datastore and access it during training to illustrate the datastore functionality."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, download the training data from [here](http://mattmahoney.net/dc/text8.zip) to your local machine:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import urllib\n",
+    "\n",
+    "os.makedirs('./data', exist_ok=True)\n",
+    "download_url = 'http://mattmahoney.net/dc/text8.zip'\n",
+    "urllib.request.urlretrieve(download_url, filename='./data/text8.zip')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each workspace is associated with a default datastore. In this tutorial, we will upload the training data to this default datastore."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = ws.get_default_datastore()\n",
+    "print(ds.datastore_type, ds.account_name, ds.container_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Upload the contents of the data directory to the path `./data` on the default datastore."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.upload(src_dir='data', target_path='data', overwrite=True, show_progress=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For convenience, let's get a reference to the path on the datastore with the zip file of training data. We can do so using the `path` method. In the next section, we can then pass this reference to our training script's `--input_data` argument. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_on_datastore = 'data/text8.zip'\n",
+    "ds_data = ds.path(path_on_datastore)\n",
+    "print(ds_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train model on the remote compute"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a project directory\n",
+    "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "project_folder = './tf-distr-hvd'\n",
+    "os.makedirs(project_folder, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copy the training script `tf_horovod_word2vec.py` into this project directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "shutil.copy('tf_horovod_word2vec.py', project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create an experiment\n",
+    "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed TensorFlow tutorial. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Experiment\n",
+    "\n",
+    "experiment_name = 'tf-distr-hvd'\n",
+    "experiment = Experiment(ws, name=experiment_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a TensorFlow estimator\n",
+    "The AML SDK's TensorFlow estimator enables you to easily submit TensorFlow training jobs for both single-node and distributed runs. For more information on the TensorFlow estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-tensorflow)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.train.dnn import TensorFlow\n",
+    "\n",
+    "script_params={\n",
+    "    '--input_data': ds_data\n",
+    "}\n",
+    "\n",
+    "estimator= TensorFlow(source_directory=project_folder,\n",
+    "                      compute_target=compute_target,\n",
+    "                      script_params=script_params,\n",
+    "                      entry_script='tf_horovod_word2vec.py',\n",
+    "                      node_count=2,\n",
+    "                      process_count_per_node=1,\n",
+    "                      distributed_backend='mpi',\n",
+    "                      use_gpu=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above code specifies that we will run our training script on `2` nodes, with one worker per node. In order to execute a distributed run using MPI/Horovod, you must provide the argument `distributed_backend='mpi'`. Using this estimator with these settings, TensorFlow, Horovod and their dependencies will be installed for you. However, if your script also uses other packages, make sure to install them via the `TensorFlow` constructor's `pip_packages` or `conda_packages` parameters.\n",
+    "\n",
+    "Note that we passed our training data reference `ds_data` to our script's `--input_data` argument. This will 1) mount our datastore on the remote compute and 2) provide the path to the data zip file on our datastore."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Submit job\n",
+    "Run your experiment by submitting your estimator object. Note that this call is asynchronous."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run = experiment.submit(estimator)\n",
+    "print(run)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Monitor your run\n",
+    "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(run).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, you can block until the script has completed training before running more code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run.wait_for_completion(show_output=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "roastala"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  },
+  "msauthor": "minxia"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

training/05.distributed-tensorflow-with-parameter-server/.gitignore

@@ -0,0 +1 @@
+/tf-distr-ps/

training/05.distributed-tensorflow-with-parameter-server/05.distributed-tensorflow-with-parameter-server.ipynb

@@ -1,313 +1,312 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 05. Distributed TensorFlow with parameter server\n",
-        "In this tutorial, you will train a TensorFlow model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using native [distributed TensorFlow](https://www.tensorflow.org/deploy/distributed)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning (AML)\n",
-        "* Go through the [00.configuration.ipynb](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) notebook to:\n",
-        "    * install the AML SDK\n",
-        "    * create a workspace and its configuration file (`config.json`)\n",
-        "* Review the [tutorial](https://aka.ms/aml-notebook-hyperdrive) on single-node TensorFlow training using the SDK"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Check core SDK version number\n",
-        "import azureml.core\n",
-        "\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Diagnostics\n",
-        "Opt-in diagnostics for better experience, quality, and security of future releases."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "Diagnostics"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.telemetry import set_diagnostics_collection\n",
-        "set_diagnostics_collection(send_diagnostics = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize workspace\n",
-        "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.workspace import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print('Workspace name: ' + ws.name, \n",
-        "      'Azure region: ' + ws.location, \n",
-        "      'Subscription id: ' + ws.subscription_id, \n",
-        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create a remote compute target\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an [Azure Batch AI](https://docs.microsoft.com/azure/batch-ai/overview) cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n",
-        "\n",
-        "**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import ComputeTarget, BatchAiCompute\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "# choose a name for your cluster\n",
-        "cluster_name = \"gpucluster\"\n",
-        "\n",
-        "try:\n",
-        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
-        "    print('Found existing compute target.')\n",
-        "except ComputeTargetException:\n",
-        "    print('Creating a new compute target...')\n",
-        "    compute_config = BatchAiCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
-        "                                                                autoscale_enabled=True,\n",
-        "                                                                cluster_min_nodes=0, \n",
-        "                                                                cluster_max_nodes=4)\n",
-        "\n",
-        "    # create the cluster\n",
-        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
-        "\n",
-        "    compute_target.wait_for_completion(show_output=True)\n",
-        "\n",
-        "    # Use the 'status' property to get a detailed status for the current cluster. \n",
-        "    print(compute_target.status.serialize())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train model on the remote compute\n",
-        "Now that we have the cluster ready to go, let's run our distributed training job."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create a project directory\n",
-        "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "project_folder = './tf-distr-ps'\n",
-        "os.makedirs(project_folder, exist_ok=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copy the training script `tf_mnist_replica.py` into this project directory."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import shutil\n",
-        "shutil.copy('tf_mnist_replica.py', project_folder)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create an experiment\n",
-        "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed TensorFlow tutorial. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Experiment\n",
-        "\n",
-        "experiment_name = 'tf-distr-ps'\n",
-        "experiment = Experiment(ws, name=experiment_name)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create a TensorFlow estimator\n",
-        "The AML SDK's TensorFlow estimator enables you to easily submit TensorFlow training jobs for both single-node and distributed runs. For more information on the TensorFlow estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-tensorflow)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.dnn import TensorFlow\n",
-        "\n",
-        "script_params={\n",
-        "    '--num_gpus': 1\n",
-        "}\n",
-        "\n",
-        "estimator = TensorFlow(source_directory=project_folder,\n",
-        "                       compute_target=compute_target,\n",
-        "                       script_params=script_params,\n",
-        "                       entry_script='tf_mnist_replica.py',\n",
-        "                       node_count=2,\n",
-        "                       worker_count=2,\n",
-        "                       parameter_server_count=1,   \n",
-        "                       distributed_backend='ps',\n",
-        "                       use_gpu=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The above code specifies that we will run our training script on `2` nodes, with two workers and one parameter server. In order to execute a native distributed TensorFlow run, you must provide the argument `distributed_backend='ps'`. Using this estimator with these settings, TensorFlow and its dependencies will be installed for you. However, if your script also uses other packages, make sure to install them via the `TensorFlow` constructor's `pip_packages` or `conda_packages` parameters."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Submit job\n",
-        "Run your experiment by submitting your estimator object. Note that this call is asynchronous."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run = experiment.submit(estimator)\n",
-        "print(run.get_details())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Monitor your run\n",
-        "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.widgets import RunDetails\n",
-        "RunDetails(run).show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Alternatively, you can block until the script has completed training before running more code."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run.wait_for_completion(show_output=True) # this provides a verbose log"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "minxia"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    },
-    "msauthor": "minxia"
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 05. Distributed TensorFlow with parameter server\n",
+    "In this tutorial, you will train a TensorFlow model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using native [distributed TensorFlow](https://www.tensorflow.org/deploy/distributed)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning (AML)\n",
+    "* Go through the [00.configuration.ipynb](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) notebook to:\n",
+    "    * install the AML SDK\n",
+    "    * create a workspace and its configuration file (`config.json`)\n",
+    "* Review the [tutorial](https://aka.ms/aml-notebook-hyperdrive) on single-node TensorFlow training using the SDK"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check core SDK version number\n",
+    "import azureml.core\n",
+    "\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "Diagnostics"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize workspace\n",
+    "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.workspace import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print('Workspace name: ' + ws.name, \n",
+    "      'Azure region: ' + ws.location, \n",
+    "      'Subscription id: ' + ws.subscription_id, \n",
+    "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a remote compute target\n",
+    "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an [Azure Batch AI](https://docs.microsoft.com/azure/batch-ai/overview) cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n",
+    "\n",
+    "**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+    "from azureml.core.compute_target import ComputeTargetException\n",
+    "\n",
+    "# choose a name for your cluster\n",
+    "cluster_name = \"gpucluster\"\n",
+    "\n",
+    "try:\n",
+    "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
+    "    print('Found existing compute target.')\n",
+    "except ComputeTargetException:\n",
+    "    print('Creating a new compute target...')\n",
+    "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
+    "                                                                max_nodes=6)\n",
+    "\n",
+    "    # create the cluster\n",
+    "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
+    "\n",
+    "compute_target.wait_for_completion(show_output=True)\n",
+    "\n",
+    "# Use the 'status' property to get a detailed status for the current cluster. \n",
+    "print(compute_target.status.serialize())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train model on the remote compute\n",
+    "Now that we have the cluster ready to go, let's run our distributed training job."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a project directory\n",
+    "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "project_folder = './tf-distr-ps'\n",
+    "os.makedirs(project_folder, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copy the training script `tf_mnist_replica.py` into this project directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "shutil.copy('tf_mnist_replica.py', project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create an experiment\n",
+    "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed TensorFlow tutorial. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Experiment\n",
+    "\n",
+    "experiment_name = 'tf-distr-ps'\n",
+    "experiment = Experiment(ws, name=experiment_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a TensorFlow estimator\n",
+    "The AML SDK's TensorFlow estimator enables you to easily submit TensorFlow training jobs for both single-node and distributed runs. For more information on the TensorFlow estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-tensorflow)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.train.dnn import TensorFlow\n",
+    "\n",
+    "script_params={\n",
+    "    '--num_gpus': 1,\n",
+    "    '--train_steps': 500\n",
+    "}\n",
+    "\n",
+    "estimator = TensorFlow(source_directory=project_folder,\n",
+    "                       compute_target=compute_target,\n",
+    "                       script_params=script_params,\n",
+    "                       entry_script='tf_mnist_replica.py',\n",
+    "                       node_count=2,\n",
+    "                       worker_count=2,\n",
+    "                       parameter_server_count=1,   \n",
+    "                       distributed_backend='ps',\n",
+    "                       use_gpu=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above code specifies that we will run our training script on `2` nodes, with two workers and one parameter server. In order to execute a native distributed TensorFlow run, you must provide the argument `distributed_backend='ps'`. Using this estimator with these settings, TensorFlow and its dependencies will be installed for you. However, if your script also uses other packages, make sure to install them via the `TensorFlow` constructor's `pip_packages` or `conda_packages` parameters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Submit job\n",
+    "Run your experiment by submitting your estimator object. Note that this call is asynchronous."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run = experiment.submit(estimator)\n",
+    "print(run)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Monitor your run\n",
+    "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(run).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, you can block until the script has completed training before running more code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run.wait_for_completion(show_output=True) # this provides a verbose log"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "minxia"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  },
+  "msauthor": "minxia"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

training/06.distributed-cntk-with-custom-docker/06.distributed-cntk-with-custom-docker.ipynb

@@ -1,391 +1,389 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 06. Distributed CNTK using custom docker images\n",
-        "In this tutorial, you will train a CNTK model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using a custom docker image and distributed training."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
-        "* Go through the [00.configuration.ipynb]() notebook to:\n",
-        "    * install the AML SDK\n",
-        "    * create a workspace and its configuration file (`config.json`)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Check core SDK version number\n",
-        "import azureml.core\n",
-        "\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Diagnostics\n",
-        "Opt-in diagnostics for better experience, quality, and security of future releases."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "Diagnostics"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.telemetry import set_diagnostics_collection\n",
-        "set_diagnostics_collection(send_diagnostics = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize workspace\n",
-        "\n",
-        "Initialize a [Workspace](https://review.docs.microsoft.com/en-us/azure/machine-learning/service/concept-azure-machine-learning-architecture?branch=release-ignite-aml#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.workspace import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print('Workspace name: ' + ws.name, \n",
-        "      'Azure region: ' + ws.location, \n",
-        "      'Subscription id: ' + ws.subscription_id, \n",
-        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create a remote compute target\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an [Azure Batch AI](https://docs.microsoft.com/azure/batch-ai/overview) cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n",
-        "\n",
-        "**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import ComputeTarget, BatchAiCompute\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "# choose a name for your cluster\n",
-        "cluster_name = \"gpucluster\"\n",
-        "\n",
-        "try:\n",
-        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
-        "    print('Found existing compute target.')\n",
-        "except ComputeTargetException:\n",
-        "    print('Creating a new compute target...')\n",
-        "    compute_config = BatchAiCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
-        "                                                                autoscale_enabled=True,\n",
-        "                                                                cluster_min_nodes=0, \n",
-        "                                                                cluster_max_nodes=4)\n",
-        "\n",
-        "    # create the cluster\n",
-        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
-        "\n",
-        "    compute_target.wait_for_completion(show_output=True)\n",
-        "\n",
-        "    # Use the 'status' property to get a detailed status for the current cluster. \n",
-        "    print(compute_target.status.serialize())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Upload training data\n",
-        "For this tutorial, we will be using the MNIST dataset.\n",
-        "\n",
-        "First, let's download the dataset. We've included the `install_mnist.py` script to download the data and convert it to a CNTK-supported format. Our data files will get written to a directory named `'mnist'`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import install_mnist\n",
-        "\n",
-        "install_mnist.main('mnist')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To make the data accessible for remote training, you will need to upload the data from your local machine to the cloud. AML provides a convenient way to do so via a [Datastore](https://docs.microsoft.com/azure/machine-learning/service/how-to-access-data). The datastore provides a mechanism for you to upload/download data, and interact with it from your remote compute targets. \n",
-        "\n",
-        "Each workspace is associated with a default datastore. In this tutorial, we will upload the training data to this default datastore, which we will then mount on the remote compute for training in the next section."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ds = ws.get_default_datastore()\n",
-        "print(ds.datastore_type, ds.account_name, ds.container_name)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The following code will upload the training data to the path `./mnist` on the default datastore."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ds.upload(src_dir='./mnist', target_path='./mnist')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now let's get a reference to the path on the datastore with the training data. We can do so using the `path` method. In the next section, we can then pass this reference to our training script's `--data_dir` argument. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "path_on_datastore = 'mnist'\n",
-        "ds_data = ds.path(path_on_datastore)\n",
-        "print(ds_data)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train model on the remote compute\n",
-        "Now that we have the cluster ready to go, let's run our distributed training job."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create a project directory\n",
-        "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "project_folder = './cntk-distr'\n",
-        "os.makedirs(project_folder, exist_ok=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copy the training script `cntk_distr_mnist.py` into this project directory."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import shutil\n",
-        "shutil.copy('cntk_distr_mnist.py', project_folder)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create an experiment\n",
-        "Create an [experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed CNTK tutorial. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Experiment\n",
-        "\n",
-        "experiment_name = 'cntk-distr'\n",
-        "experiment = Experiment(ws, name=experiment_name)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create an Estimator\n",
-        "The AML SDK's base Estimator enables you to easily submit custom scripts for both single-node and distributed runs. You should this generic estimator for training code using frameworks such as sklearn or CNTK that don't have corresponding custom estimators. For more information on using the generic estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-ml-models)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.estimator import *\n",
-        "\n",
-        "script_params = {\n",
-        "    '--num_epochs': 50,\n",
-        "    '--data_dir': ds_data.as_mount(),\n",
-        "    '--output_dir': './outputs'\n",
-        "}\n",
-        "\n",
-        "estimator = Estimator(source_directory=project_folder,\n",
-        "                      compute_target=compute_target,\n",
-        "                      entry_script='cntk_distr_mnist.py',\n",
-        "                      script_params=script_params,\n",
-        "                      node_count=2,\n",
-        "                      process_count_per_node=1,\n",
-        "                      distributed_backend='mpi',     \n",
-        "                      pip_packages=['cntk-gpu==2.6'],\n",
-        "                      custom_docker_base_image='microsoft/mmlspark:gpu-0.12',\n",
-        "                      use_gpu=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We would like to train our model using a [pre-built Docker container](https://hub.docker.com/r/microsoft/mmlspark/). To do so, specify the name of the docker image to the argument `custom_docker_base_image`. You can only provide images available in public docker repositories such as Docker Hub using this argument. To use an image from a private docker repository, use the constructor's `environment_definition` parameter instead. Finally, we provide the `cntk` package to `pip_packages` to install CNTK 2.6 on our custom image.\n",
-        "\n",
-        "The above code specifies that we will run our training script on `2` nodes, with one worker per node. In order to run distributed CNTK, which uses MPI, you must provide the argument `distributed_backend='mpi'`."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Submit job\n",
-        "Run your experiment by submitting your estimator object. Note that this call is asynchronous."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run = experiment.submit(estimator)\n",
-        "print(run.get_details())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Monitor your run\n",
-        "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.widgets import RunDetails\n",
-        "RunDetails(run).show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Alternatively, you can block until the script has completed training before running more code."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run.wait_for_completion(show_output=True)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "minxia"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 06. Distributed CNTK using custom docker images\n",
+    "In this tutorial, you will train a CNTK model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using a custom docker image and distributed training."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
+    "* Go through the [00.configuration.ipynb]() notebook to:\n",
+    "    * install the AML SDK\n",
+    "    * create a workspace and its configuration file (`config.json`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check core SDK version number\n",
+    "import azureml.core\n",
+    "\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "Diagnostics"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize workspace\n",
+    "\n",
+    "Initialize a [Workspace](https://review.docs.microsoft.com/en-us/azure/machine-learning/service/concept-azure-machine-learning-architecture?branch=release-ignite-aml#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.workspace import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print('Workspace name: ' + ws.name, \n",
+    "      'Azure region: ' + ws.location, \n",
+    "      'Subscription id: ' + ws.subscription_id, \n",
+    "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a remote compute target\n",
+    "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an [Azure Batch AI](https://docs.microsoft.com/azure/batch-ai/overview) cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n",
+    "\n",
+    "**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+    "from azureml.core.compute_target import ComputeTargetException\n",
+    "\n",
+    "# choose a name for your cluster\n",
+    "cluster_name = \"gpucluster\"\n",
+    "\n",
+    "try:\n",
+    "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
+    "    print('Found existing compute target.')\n",
+    "except ComputeTargetException:\n",
+    "    print('Creating a new compute target...')\n",
+    "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
+    "                                                                max_nodes=6)\n",
+    "\n",
+    "    # create the cluster\n",
+    "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
+    "\n",
+    "compute_target.wait_for_completion(show_output=True)\n",
+    "\n",
+    "# Use the 'status' property to get a detailed status for the current cluster. \n",
+    "print(compute_target.status.serialize())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Upload training data\n",
+    "For this tutorial, we will be using the MNIST dataset.\n",
+    "\n",
+    "First, let's download the dataset. We've included the `install_mnist.py` script to download the data and convert it to a CNTK-supported format. Our data files will get written to a directory named `'mnist'`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import install_mnist\n",
+    "\n",
+    "install_mnist.main('mnist')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To make the data accessible for remote training, you will need to upload the data from your local machine to the cloud. AML provides a convenient way to do so via a [Datastore](https://docs.microsoft.com/azure/machine-learning/service/how-to-access-data). The datastore provides a mechanism for you to upload/download data, and interact with it from your remote compute targets. \n",
+    "\n",
+    "Each workspace is associated with a default datastore. In this tutorial, we will upload the training data to this default datastore, which we will then mount on the remote compute for training in the next section."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = ws.get_default_datastore()\n",
+    "print(ds.datastore_type, ds.account_name, ds.container_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following code will upload the training data to the path `./mnist` on the default datastore."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.upload(src_dir='./mnist', target_path='./mnist')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's get a reference to the path on the datastore with the training data. We can do so using the `path` method. In the next section, we can then pass this reference to our training script's `--data_dir` argument. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_on_datastore = 'mnist'\n",
+    "ds_data = ds.path(path_on_datastore)\n",
+    "print(ds_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train model on the remote compute\n",
+    "Now that we have the cluster ready to go, let's run our distributed training job."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a project directory\n",
+    "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "project_folder = './cntk-distr'\n",
+    "os.makedirs(project_folder, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copy the training script `cntk_distr_mnist.py` into this project directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "shutil.copy('cntk_distr_mnist.py', project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create an experiment\n",
+    "Create an [experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed CNTK tutorial. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Experiment\n",
+    "\n",
+    "experiment_name = 'cntk-distr'\n",
+    "experiment = Experiment(ws, name=experiment_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create an Estimator\n",
+    "The AML SDK's base Estimator enables you to easily submit custom scripts for both single-node and distributed runs. You should this generic estimator for training code using frameworks such as sklearn or CNTK that don't have corresponding custom estimators. For more information on using the generic estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-ml-models)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.train.estimator import *\n",
+    "\n",
+    "script_params = {\n",
+    "    '--num_epochs': 20,\n",
+    "    '--data_dir': ds_data.as_mount(),\n",
+    "    '--output_dir': './outputs'\n",
+    "}\n",
+    "\n",
+    "estimator = Estimator(source_directory=project_folder,\n",
+    "                      compute_target=compute_target,\n",
+    "                      entry_script='cntk_distr_mnist.py',\n",
+    "                      script_params=script_params,\n",
+    "                      node_count=2,\n",
+    "                      process_count_per_node=1,\n",
+    "                      distributed_backend='mpi',     \n",
+    "                      pip_packages=['cntk-gpu==2.6'],\n",
+    "                      custom_docker_base_image='microsoft/mmlspark:gpu-0.12',\n",
+    "                      use_gpu=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We would like to train our model using a [pre-built Docker container](https://hub.docker.com/r/microsoft/mmlspark/). To do so, specify the name of the docker image to the argument `custom_docker_base_image`. You can only provide images available in public docker repositories such as Docker Hub using this argument. To use an image from a private docker repository, use the constructor's `environment_definition` parameter instead. Finally, we provide the `cntk` package to `pip_packages` to install CNTK 2.6 on our custom image.\n",
+    "\n",
+    "The above code specifies that we will run our training script on `2` nodes, with one worker per node. In order to run distributed CNTK, which uses MPI, you must provide the argument `distributed_backend='mpi'`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Submit job\n",
+    "Run your experiment by submitting your estimator object. Note that this call is asynchronous."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run = experiment.submit(estimator)\n",
+    "print(run.get_details())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Monitor your run\n",
+    "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(run).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, you can block until the script has completed training before running more code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run.wait_for_completion(show_output=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "minxia"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

training/06.distributed-cntk-with-custom-docker/cntk_mnist.py

@@ -1,321 +0,0 @@
-# Copyright (c) Microsoft Corporation. All rights reserved.
-# Licensed under the MIT License.
-# Script adapted from:
-# 1. https://github.com/Microsoft/CNTK/blob/v2.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb
-# 2. https://github.com/Microsoft/CNTK/blob/v2.0/Tutorials/CNTK_103C_MNIST_MultiLayerPerceptron.ipynb
-# ===================================================================================================
-"""Train a CNTK multi-layer perceptron on the MNIST dataset."""
-
-from __future__ import print_function
-import gzip
-import numpy as np
-import os
-import shutil
-import struct
-import sys
-import time
-
-import cntk as C
-from azureml.core.run import Run
-import argparse
-
-run = Run.get_submitted_run()
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--learning_rate', type=float, default=0.001, help='learning rate')
-parser.add_argument('--num_hidden_layers', type=int, default=2, help='number of hidden layers')
-parser.add_argument('--minibatch_size', type=int, default=64, help='minibatchsize')
-
-args = parser.parse_args()
-
-# Functions to load MNIST images and unpack into train and test set.
-# - loadData reads image data and formats into a 28x28 long array
-# - loadLabels reads the corresponding labels data, 1 for each image
-# - load packs the downloaded image and labels data into a combined format to be read later by
-#   CNTK text reader
-
-
-def loadData(src, cimg):
-    print('Downloading ' + src)
-    gzfname, h = urlretrieve(src, './delete.me')
-    print('Done.')
-    try:
-        with gzip.open(gzfname) as gz:
-            n = struct.unpack('I', gz.read(4))
-            # Read magic number.
-            if n[0] != 0x3080000:
-                raise Exception('Invalid file: unexpected magic number.')
-            # Read number of entries.
-            n = struct.unpack('>I', gz.read(4))[0]
-            if n != cimg:
-                raise Exception('Invalid file: expected {0} entries.'.format(cimg))
-            crow = struct.unpack('>I', gz.read(4))[0]
-            ccol = struct.unpack('>I', gz.read(4))[0]
-            if crow != 28 or ccol != 28:
-                raise Exception('Invalid file: expected 28 rows/cols per image.')
-            # Read data.
-            res = np.fromstring(gz.read(cimg * crow * ccol), dtype=np.uint8)
-    finally:
-        os.remove(gzfname)
-    return res.reshape((cimg, crow * ccol))
-
-
-def loadLabels(src, cimg):
-    print('Downloading ' + src)
-    gzfname, h = urlretrieve(src, './delete.me')
-    print('Done.')
-    try:
-        with gzip.open(gzfname) as gz:
-            n = struct.unpack('I', gz.read(4))
-            # Read magic number.
-            if n[0] != 0x1080000:
-                raise Exception('Invalid file: unexpected magic number.')
-            # Read number of entries.
-            n = struct.unpack('>I', gz.read(4))
-            if n[0] != cimg:
-                raise Exception('Invalid file: expected {0} rows.'.format(cimg))
-            # Read labels.
-            res = np.fromstring(gz.read(cimg), dtype=np.uint8)
-    finally:
-        os.remove(gzfname)
-    return res.reshape((cimg, 1))
-
-
-def try_download(dataSrc, labelsSrc, cimg):
-    data = loadData(dataSrc, cimg)
-    labels = loadLabels(labelsSrc, cimg)
-    return np.hstack((data, labels))
-
-# Save the data files into a format compatible with CNTK text reader
-
-
-def savetxt(filename, ndarray):
-    dir = os.path.dirname(filename)
-
-    if not os.path.exists(dir):
-        os.makedirs(dir)
-
-    if not os.path.isfile(filename):
-        print("Saving", filename)
-        with open(filename, 'w') as f:
-            labels = list(map(' '.join, np.eye(10, dtype=np.uint).astype(str)))
-            for row in ndarray:
-                row_str = row.astype(str)
-                label_str = labels[row[-1]]
-                feature_str = ' '.join(row_str[:-1])
-                f.write('|labels {} |features {}\n'.format(label_str, feature_str))
-    else:
-        print("File already exists", filename)
-
-# Read a CTF formatted text (as mentioned above) using the CTF deserializer from a file
-
-
-def create_reader(path, is_training, input_dim, num_label_classes):
-    return C.io.MinibatchSource(C.io.CTFDeserializer(path, C.io.StreamDefs(
-        labels=C.io.StreamDef(field='labels', shape=num_label_classes, is_sparse=False),
-        features=C.io.StreamDef(field='features', shape=input_dim, is_sparse=False)
-    )), randomize=is_training, max_sweeps=C.io.INFINITELY_REPEAT if is_training else 1)
-
-# Defines a utility that prints the training progress
-
-
-def print_training_progress(trainer, mb, frequency, verbose=1):
-    training_loss = "NA"
-    eval_error = "NA"
-
-    if mb % frequency == 0:
-        training_loss = trainer.previous_minibatch_loss_average
-        eval_error = trainer.previous_minibatch_evaluation_average
-        if verbose:
-            print("Minibatch: {0}, Loss: {1:.4f}, Error: {2:.2f}%".format(mb, training_loss, eval_error * 100))
-
-    return mb, training_loss, eval_error
-
-# Create the network architecture
-
-
-def create_model(features):
-    with C.layers.default_options(init=C.layers.glorot_uniform(), activation=C.ops.relu):
-        h = features
-        for _ in range(num_hidden_layers):
-            h = C.layers.Dense(hidden_layers_dim)(h)
-        r = C.layers.Dense(num_output_classes, activation=None)(h)
-        return r
-
-
-if __name__ == '__main__':
-    run = Run.get_submitted_run()
-
-    try:
-        from urllib.request import urlretrieve
-    except ImportError:
-        from urllib import urlretrieve
-
-    # Select the right target device when this script is being used:
-    if 'TEST_DEVICE' in os.environ:
-        if os.environ['TEST_DEVICE'] == 'cpu':
-            C.device.try_set_default_device(C.device.cpu())
-        else:
-            C.device.try_set_default_device(C.device.gpu(0))
-
-    # URLs for the train image and labels data
-    url_train_image = 'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz'
-    url_train_labels = 'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz'
-    num_train_samples = 60000
-
-    print("Downloading train data")
-    train = try_download(url_train_image, url_train_labels, num_train_samples)
-
-    url_test_image = 'http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz'
-    url_test_labels = 'http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz'
-    num_test_samples = 10000
-
-    print("Downloading test data")
-    test = try_download(url_test_image, url_test_labels, num_test_samples)
-
-    # Save the train and test files (prefer our default path for the data
-    rank = os.environ.get("OMPI_COMM_WORLD_RANK")
-    data_dir = os.path.join("outputs", "MNIST")
-    sentinel_path = os.path.join(data_dir, "complete.txt")
-    if rank == '0':
-        print('Writing train text file...')
-        savetxt(os.path.join(data_dir, "Train-28x28_cntk_text.txt"), train)
-
-        print('Writing test text file...')
-        savetxt(os.path.join(data_dir, "Test-28x28_cntk_text.txt"), test)
-        with open(sentinel_path, 'w+') as f:
-            f.write("download complete")
-
-        print('Done with downloading data.')
-    else:
-        while not os.path.exists(sentinel_path):
-            time.sleep(0.01)
-
-    # Ensure we always get the same amount of randomness
-    np.random.seed(0)
-
-    # Define the data dimensions
-    input_dim = 784
-    num_output_classes = 10
-
-    # Ensure the training and test data is generated and available for this tutorial.
-    # We search in two locations in the toolkit for the cached MNIST data set.
-    data_found = False
-    for data_dir in [os.path.join("..", "Examples", "Image", "DataSets", "MNIST"),
-                     os.path.join("data_" + str(rank), "MNIST"),
-                     os.path.join("outputs", "MNIST")]:
-        train_file = os.path.join(data_dir, "Train-28x28_cntk_text.txt")
-        test_file = os.path.join(data_dir, "Test-28x28_cntk_text.txt")
-        if os.path.isfile(train_file) and os.path.isfile(test_file):
-            data_found = True
-            break
-    if not data_found:
-        raise ValueError("Please generate the data by completing CNTK 103 Part A")
-    print("Data directory is {0}".format(data_dir))
-
-    num_hidden_layers = args.num_hidden_layers
-    hidden_layers_dim = 400
-
-    input = C.input_variable(input_dim)
-    label = C.input_variable(num_output_classes)
-
-    z = create_model(input)
-    # Scale the input to 0-1 range by dividing each pixel by 255.
-    z = create_model(input / 255.0)
-
-    loss = C.cross_entropy_with_softmax(z, label)
-    label_error = C.classification_error(z, label)
-
-    # Instantiate the trainer object to drive the model training
-    learning_rate = args.learning_rate
-    lr_schedule = C.learning_rate_schedule(learning_rate, C.UnitType.minibatch)
-    learner = C.sgd(z.parameters, lr_schedule)
-    trainer = C.Trainer(z, (loss, label_error), [learner])
-
-    # Initialize the parameters for the trainer
-    minibatch_size = args.minibatch_size
-    num_samples_per_sweep = 60000
-    num_sweeps_to_train_with = 10
-    num_minibatches_to_train = (num_samples_per_sweep * num_sweeps_to_train_with) / minibatch_size
-
-    # Create the reader to training data set
-    reader_train = create_reader(train_file, True, input_dim, num_output_classes)
-
-    # Map the data streams to the input and labels.
-    input_map = {
-        label: reader_train.streams.labels,
-        input: reader_train.streams.features
-    }
-
-    # Run the trainer on and perform model training
-    training_progress_output_freq = 500
-
-    errors = []
-    losses = []
-    for i in range(0, int(num_minibatches_to_train)):
-        # Read a mini batch from the training data file
-        data = reader_train.next_minibatch(minibatch_size, input_map=input_map)
-
-        trainer.train_minibatch(data)
-        batchsize, loss, error = print_training_progress(trainer, i, training_progress_output_freq, verbose=1)
-        if (error != 'NA') and (loss != 'NA'):
-            errors.append(float(error))
-            losses.append(float(loss))
-
-    # log the losses
-    if rank == '0':
-        run.log_list("Loss", losses)
-        run.log_list("Error", errors)
-
-    # Read the training data
-    reader_test = create_reader(test_file, False, input_dim, num_output_classes)
-
-    test_input_map = {
-        label: reader_test.streams.labels,
-        input: reader_test.streams.features,
-    }
-
-    # Test data for trained model
-    test_minibatch_size = 512
-    num_samples = 10000
-    num_minibatches_to_test = num_samples // test_minibatch_size
-    test_result = 0.0
-
-    for i in range(num_minibatches_to_test):
-        # We are loading test data in batches specified by test_minibatch_size
-        # Each data point in the minibatch is a MNIST digit image of 784 dimensions
-        # with one pixel per dimension that we will encode / decode with the
-        # trained model.
-        data = reader_test.next_minibatch(test_minibatch_size,
-                                          input_map=test_input_map)
-
-        eval_error = trainer.test_minibatch(data)
-        test_result = test_result + eval_error
-
-    # Average of evaluation errors of all test minibatches
-    print("Average test error: {0:.2f}%".format((test_result * 100) / num_minibatches_to_test))
-
-    out = C.softmax(z)
-
-    # Read the data for evaluation
-    reader_eval = create_reader(test_file, False, input_dim, num_output_classes)
-
-    eval_minibatch_size = 25
-    eval_input_map = {input: reader_eval.streams.features}
-
-    data = reader_test.next_minibatch(eval_minibatch_size, input_map=test_input_map)
-
-    img_label = data[label].asarray()
-    img_data = data[input].asarray()
-    predicted_label_prob = [out.eval(img_data[i]) for i in range(len(img_data))]
-
-    # Find the index with the maximum value for both predicted as well as the ground truth
-    pred = [np.argmax(predicted_label_prob[i]) for i in range(len(predicted_label_prob))]
-    gtlabel = [np.argmax(img_label[i]) for i in range(len(img_label))]
-
-    print("Label    :", gtlabel[:25])
-    print("Predicted:", pred)
-
-    # save model to outputs folder
-    z.save('outputs/cntk.model')

training/08.export-run-history-to-tensorboard/08.export-run-history-to-tensorboard.ipynb

@@ -1,248 +1,248 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# 41. Export Run History as Tensorboard logs\n",
-        "\n",
-        "1. Run some training and log some metrics into Run History\n",
-        "2. Export the run history to some directory as Tensorboard logs\n",
-        "3. Launch a local Tensorboard to view the run history"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Check core SDK version number\n",
-        "import azureml.core\n",
-        "\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize Workspace\n",
-        "\n",
-        "Initialize a workspace object from persisted configuration."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace, Run, Experiment\n",
-        "\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print('Workspace name: ' + ws.name, \n",
-        "      'Azure region: ' + ws.location, \n",
-        "      'Subscription id: ' + ws.subscription_id, \n",
-        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Set experiment name and start the run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "experiment_name = 'export-to-tensorboard'\n",
-        "exp = Experiment(ws, experiment_name)\n",
-        "root_run = exp.start_logging()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# load diabetes dataset, a well-known built-in small dataset that comes with scikit-learn\n",
-        "from sklearn.datasets import load_diabetes\n",
-        "from sklearn.linear_model import Ridge\n",
-        "from sklearn.metrics import mean_squared_error\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "\n",
-        "X, y = load_diabetes(return_X_y=True)\n",
-        "\n",
-        "columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
-        "\n",
-        "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
-        "data = {\n",
-        "    \"train\":{\"x\":x_train, \"y\":y_train},        \n",
-        "    \"test\":{\"x\":x_test, \"y\":y_test}\n",
-        "}"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Example experiment\n",
-        "from tqdm import tqdm\n",
-        "\n",
-        "alphas = [.1, .2, .3, .4, .5, .6 , .7]\n",
-        "\n",
-        "# try a bunch of alpha values in a Linear Regression (Ridge) model\n",
-        "for alpha in tqdm(alphas):\n",
-        "    # create a bunch of child runs\n",
-        "    with root_run.child_run(\"alpha\" + str(alpha)) as run:\n",
-        "        # More data science stuff\n",
-        "        reg = Ridge(alpha=alpha)\n",
-        "        reg.fit(data[\"train\"][\"x\"], data[\"train\"][\"y\"])\n",
-        "        # TODO save model\n",
-        "        preds = reg.predict(data[\"test\"][\"x\"])\n",
-        "        mse = mean_squared_error(preds, data[\"test\"][\"y\"])\n",
-        "        # End train and eval\n",
-        "\n",
-        "        # log alpha, mean_squared_error and feature names in run history\n",
-        "        root_run.log(\"alpha\", alpha)\n",
-        "        root_run.log(\"mse\", mse)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Export Run History to Tensorboard logs"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Export Run History to Tensorboard logs\n",
-        "from azureml.contrib.tensorboard.export import export_to_tensorboard\n",
-        "import os\n",
-        "import tensorflow as tf\n",
-        "\n",
-        "logdir = 'exportedTBlogs'\n",
-        "log_path = os.path.join(os.getcwd(), logdir)\n",
-        "try:\n",
-        "    os.stat(log_path)\n",
-        "except os.error:\n",
-        "    os.mkdir(log_path)\n",
-        "print(logdir)\n",
-        "\n",
-        "# export run history for the project\n",
-        "export_to_tensorboard(root_run, logdir)\n",
-        "\n",
-        "# or export a particular run\n",
-        "# export_to_tensorboard(run, logdir)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "root_run.complete()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Start Tensorboard\n",
-        "\n",
-        "Or you can start the Tensorboard outside this notebook to view the result"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.contrib.tensorboard import Tensorboard\n",
-        "\n",
-        "# The Tensorboard constructor takes an array of runs, so be sure and pass it in as a single-element array here\n",
-        "tb = Tensorboard([], local_root=logdir, port=6006)\n",
-        "\n",
-        "# If successful, start() returns a string with the URI of the instance.\n",
-        "tb.start()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Stop Tensorboard\n",
-        "\n",
-        "When you're done, make sure to call the `stop()` method of the Tensorboard object."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "tb.stop()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "roastala"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.5"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 41. Export Run History as Tensorboard logs\n",
+    "\n",
+    "1. Run some training and log some metrics into Run History\n",
+    "2. Export the run history to some directory as Tensorboard logs\n",
+    "3. Launch a local Tensorboard to view the run history"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check core SDK version number\n",
+    "import azureml.core\n",
+    "\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize Workspace\n",
+    "\n",
+    "Initialize a workspace object from persisted configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace, Run, Experiment\n",
+    "\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print('Workspace name: ' + ws.name, \n",
+    "      'Azure region: ' + ws.location, \n",
+    "      'Subscription id: ' + ws.subscription_id, \n",
+    "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set experiment name and start the run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment_name = 'export-to-tensorboard'\n",
+    "exp = Experiment(ws, experiment_name)\n",
+    "root_run = exp.start_logging()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load diabetes dataset, a well-known built-in small dataset that comes with scikit-learn\n",
+    "from sklearn.datasets import load_diabetes\n",
+    "from sklearn.linear_model import Ridge\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X, y = load_diabetes(return_X_y=True)\n",
+    "\n",
+    "columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
+    "\n",
+    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "data = {\n",
+    "    \"train\":{\"x\":x_train, \"y\":y_train},        \n",
+    "    \"test\":{\"x\":x_test, \"y\":y_test}\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Example experiment\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "alphas = [.1, .2, .3, .4, .5, .6 , .7]\n",
+    "\n",
+    "# try a bunch of alpha values in a Linear Regression (Ridge) model\n",
+    "for alpha in tqdm(alphas):\n",
+    "    # create a bunch of child runs\n",
+    "    with root_run.child_run(\"alpha\" + str(alpha)) as run:\n",
+    "        # More data science stuff\n",
+    "        reg = Ridge(alpha=alpha)\n",
+    "        reg.fit(data[\"train\"][\"x\"], data[\"train\"][\"y\"])\n",
+    "        # TODO save model\n",
+    "        preds = reg.predict(data[\"test\"][\"x\"])\n",
+    "        mse = mean_squared_error(preds, data[\"test\"][\"y\"])\n",
+    "        # End train and eval\n",
+    "\n",
+    "        # log alpha, mean_squared_error and feature names in run history\n",
+    "        root_run.log(\"alpha\", alpha)\n",
+    "        root_run.log(\"mse\", mse)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Export Run History to Tensorboard logs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Export Run History to Tensorboard logs\n",
+    "from azureml.contrib.tensorboard.export import export_to_tensorboard\n",
+    "import os\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "logdir = 'exportedTBlogs'\n",
+    "log_path = os.path.join(os.getcwd(), logdir)\n",
+    "try:\n",
+    "    os.stat(log_path)\n",
+    "except os.error:\n",
+    "    os.mkdir(log_path)\n",
+    "print(logdir)\n",
+    "\n",
+    "# export run history for the project\n",
+    "export_to_tensorboard(root_run, logdir)\n",
+    "\n",
+    "# or export a particular run\n",
+    "# export_to_tensorboard(run, logdir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "root_run.complete()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Start Tensorboard\n",
+    "\n",
+    "Or you can start the Tensorboard outside this notebook to view the result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.contrib.tensorboard import Tensorboard\n",
+    "\n",
+    "# The Tensorboard constructor takes an array of runs, so be sure and pass it in as a single-element array here\n",
+    "tb = Tensorboard([], local_root=logdir, port=6006)\n",
+    "\n",
+    "# If successful, start() returns a string with the URI of the instance.\n",
+    "tb.start()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stop Tensorboard\n",
+    "\n",
+    "When you're done, make sure to call the `stop()` method of the Tensorboard object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tb.stop()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "roastala"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

training/readme.md

@@ -1,52 +0,0 @@
-# Training ML models with Azure ML SDK
-These notebook tutorials cover the various scenarios for training machine learning and deep learning models with Azure Machine Learning.
-
-## Sample notebooks
-- [01.train-hyperparameter-tune-deploy-with-pytorch](./01.train-hyperparameter-tune-deploy-with-pytorch/01.train-hyperparameter-tune-deploy-with-pytorch.ipynb)  
-Train, hyperparameter tune, and deploy a PyTorch image classification model that distinguishes bees vs. ants using transfer learning. Azure ML concepts covered:
-  - Create a remote compute target (Batch AI cluster)
-  - Upload training data using `Datastore`
-  - Run a single-node `PyTorch` training job
-  - Hyperparameter tune model with HyperDrive
-  - Find and register the best model
-  - Deploy model to ACI
-- [02.distributed-pytorch-with-horovod](./02.distributed-pytorch-with-horovod/02.distributed-pytorch-with-horovod.ipynb)  
-Train a PyTorch model on the MNIST dataset using distributed training with Horovod. Azure ML concepts covered:
-  - Create a remote compute target (Batch AI cluster)
-  - Run a two-node distributed `PyTorch` training job using Horovod
-- [03.train-hyperparameter-tun-deploy-with-tensorflow](./03.train-hyperparameter-tune-deploy-with-tensorflow/03.train-hyperparameter-tune-deploy-with-tensorflow.ipynb)  
-Train, hyperparameter tune, and deploy a TensorFlow model on the MNIST dataset. Azure ML concepts covered:
-  - Create a remote compute target (Batch AI cluster)
-  - Upload training data using `Datastore`
-  - Run a single-node `TensorFlow` training job
-  - Leverage features of the `Run` object
-  - Download the trained model
-  - Hyperparameter tune model with HyperDrive
-  - Find and register the best model
-  - Deploy model to ACI
-- [04.distributed-tensorflow-with-horovod](./04.distributed-tensorflow-with-horovod/04.distributed-tensorflow-with-horovod.ipynb)  
-Train a TensorFlow word2vec model using distributed training with Horovod. Azure ML concepts covered:
-  - Create a remote compute target (Batch AI cluster)
-  - Upload training data using `Datastore`
-  - Run a two-node distributed `TensorFlow` training job using Horovod
-- [05.distributed-tensorflow-with-parameter-server](./05.distributed-tensorflow-with-parameter-server/05.distributed-tensorflow-with-parameter-server.ipynb)  
-Train a TensorFlow model on the MNIST dataset using native distributed TensorFlow (parameter server). Azure ML concepts covered:
-  - Create a remote compute target (Batch AI cluster)
-  - Run a two workers, one parameter server distributed `TensorFlow` training job
-- [06.distributed-cntk-with-custom-docker](./06.distributed-cntk-with-custom-docker/06.distributed-cntk-with-custom-docker.ipynb)  
-Train a CNTK model on the MNIST dataset using the Azure ML base `Estimator` with custom Docker image and distributed training. Azure ML concepts covered:
-  - Create a remote compute target (Batch AI cluster)
-  - Upload training data using `Datastore`
-  - Run a base `Estimator` training job using a custom Docker image from Docker Hub
-  - Distributed CNTK two-node training job via MPI using base `Estimator`
-  
-- [07.tensorboard](./07.tensorboard/07.tensorboard.ipynb)  
-Train a TensorFlow MNIST model locally, on a DSVM, and on Batch AI and view the logs live on TensorBoard. Azure ML concepts covered:
-  - Run the training job locally with Azure ML and run TensorBoard locally. Start (and stop) an Azure ML `TensorBoard` object to stream and view the logs
-  - Run the training job on a remote DSVM and stream the logs to TensorBoard
-  - Run the training job on a remote Batch AI cluster and stream the logs to TensorBoard
-  - Start a `Tensorboard` instance that displays the logs from all three above runs in one
-- [08.export-run-history-to-tensorboard](./08.export-run-history-to-tensorboard/08.export-run-history-to-tensorboard.ipynb)
-  - Start an Azure ML `Experiment` and log metrics to `Run` history
-  - Export the `Run` history logs to TensorBoard logs
-  - View the logs in TensorBoard

tutorials/03.auto-train-models.ipynb

@@ -1,427 +1,427 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Tutorial: Train a classification model with automated machine learning\n",
-        "\n",
-        "In this tutorial, you'll learn how to generate a  machine learning model using automated machine learning (automated ML).  Azure Machine Learning can perform algorithm selection and hyperparameter selection in an automated way for you. The final model can then be deployed following the workflow in the [Deploy a model](02.deploy-models.ipynb) tutorial.\n",
-        "\n",
-        "[flow diagram](./imgs/flow2.png)\n",
-        "\n",
-        "Similar to the [train models tutorial](01.train-models.ipynb), this tutorial classifies handwritten images of digits (0-9) from the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset. But this time you don't to specify an algorithm or tune hyperparameters. The automated ML technique iterates over many combinations of algorithms and hyperparameters until it finds the best model based on your criterion.\n",
-        "\n",
-        "You'll learn how to:\n",
-        "\n",
-        "> * Set up your development environment\n",
-        "> * Access and examine the data\n",
-        "> * Train using an automated classifier locally with custom parameters\n",
-        "> * Explore the results\n",
-        "> * Review training results\n",
-        "> * Register the best model\n",
-        "\n",
-        "## Prerequisites\n",
-        "\n",
-        "Use [these instructions](https://aka.ms/aml-how-to-configure-environment) to:  \n",
-        "* Create a workspace and its configuration file (**config.json**)  \n",
-        "* Upload your **config.json** to the same folder as this notebook"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Start a notebook\n",
-        "\n",
-        "To follow along, start a new notebook from the same directory as **config.json** and copy the code from the sections below.\n",
-        "\n",
-        "\n",
-        "## Set up your development environment\n",
-        "\n",
-        "All the setup for your development work can be accomplished in the Python notebook.  Setup includes:\n",
-        "\n",
-        "* Import Python packages\n",
-        "* Configure a workspace to enable communication between your local computer and remote resources\n",
-        "* Create a directory to store training scripts\n",
-        "\n",
-        "### Import packages\n",
-        "Import Python packages you need in this tutorial."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.core\n",
-        "import pandas as pd\n",
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl.run import AutoMLRun\n",
-        "import time\n",
-        "import logging\n",
-        "from sklearn import datasets\n",
-        "from matplotlib import pyplot as plt\n",
-        "from matplotlib.pyplot import imshow\n",
-        "import random\n",
-        "import numpy as np"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Configure workspace\n",
-        "\n",
-        "Create a workspace object from the existing workspace. `Workspace.from_config()` reads the file **aml_config/config.json** and loads the details into an object named `ws`.  `ws` is used throughout the rest of the code in this tutorial.\n",
-        "\n",
-        "Once you have a workspace object, specify a name for the experiment and create and register a local directory with the workspace. The history of all runs is recorded under the specified experiment."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "# choose a name for the run history container in the workspace\n",
-        "experiment_name = 'automl-classifier'\n",
-        "# project folder\n",
-        "project_folder = './automl-classifier'\n",
-        "\n",
-        "import os\n",
-        "\n",
-        "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "pd.DataFrame(data=output, index=['']).T"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explore data\n",
-        "\n",
-        "The initial training tutorial used a high-resolution version  of the MNIST dataset (28x28 pixels).  Since auto training requires many iterations, this tutorial uses a smaller resolution version  of the images (8x8 pixels) to demonstrate the concepts while speeding up the time needed for each iteration."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn import datasets\n",
-        "\n",
-        "digits = datasets.load_digits()\n",
-        "\n",
-        "# Exclude the first 100 rows from training so that they can be used for test.\n",
-        "X_train = digits.data[100:,:]\n",
-        "y_train = digits.target[100:]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Display some sample images\n",
-        "\n",
-        "Load the data into `numpy` arrays. Then use `matplotlib` to plot 30 random images from the dataset with their labels above them."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "count = 0\n",
-        "sample_size = 30\n",
-        "plt.figure(figsize = (16, 6))\n",
-        "for i in np.random.permutation(X_train.shape[0])[:sample_size]:\n",
-        "    count = count + 1\n",
-        "    plt.subplot(1, sample_size, count)\n",
-        "    plt.axhline('')\n",
-        "    plt.axvline('')\n",
-        "    plt.text(x = 2, y = -2, s = y_train[i], fontsize = 18)\n",
-        "    plt.imshow(X_train[i].reshape(8, 8), cmap = plt.cm.Greys)\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You now have the necessary packages and data ready for auto training for your model. \n",
-        "\n",
-        "## Auto train a model \n",
-        "\n",
-        "To auto train a model, first define settings for autogeneration and tuning and then run the automatic classifier.\n",
-        "\n",
-        "\n",
-        "### Define settings for autogeneration and tuning\n",
-        "\n",
-        "Define the experiment parameters and models settings for autogeneration and tuning.  \n",
-        "\n",
-        "\n",
-        "|Property| Value in this tutorial |Description|\n",
-        "|----|----|---|\n",
-        "|**primary_metric**|AUC Weighted | Metric that you want to optimize.|\n",
-        "|**max_time_sec**|12,000|Time limit in seconds for each iteration|\n",
-        "|**iterations**|20|Number of iterations. In each iteration, the model trains with the data with a specific pipeline|\n",
-        "|**n_cross_validations**|3|Number of cross validation splits|\n",
-        "|**exit_score**|0.9985|*double* value indicating the target for *primary_metric*. Once the target is surpassed the run terminates|\n",
-        "|**blacklist_algos**|['kNN','LinearSVM']|*Array* of *strings* indicating algorithms to ignore.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "configure automl"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.train.automl import AutoMLConfig\n",
-        "\n",
-        "##Local compute \n",
-        "Automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             primary_metric = 'AUC_weighted',\n",
-        "                             max_time_sec = 12000,\n",
-        "                             iterations = 20,\n",
-        "                             n_cross_validations = 3,\n",
-        "                             exit_score = 0.9985,\n",
-        "                             blacklist_algos = ['kNN','LinearSVM'],\n",
-        "                             X = X_train,\n",
-        "                             y = y_train,\n",
-        "                             path=project_folder)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Run the automatic classifier\n",
-        "\n",
-        "Start the experiment to run locally. Define the compute target as local and set the output to true to view progress on the experiment."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "local submitted run",
-          "automl"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.experiment import Experiment\n",
-        "experiment=Experiment(ws, experiment_name)\n",
-        "local_run = experiment.submit(Automl_config, show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explore the results\n",
-        "\n",
-        "Explore the results of automatic training with a Jupyter widget or by examining the experiment history.\n",
-        "\n",
-        "### Jupyter widget\n",
-        "\n",
-        "Use the Jupyter notebook widget to see a graph and a table of all results."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "use notebook widget"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.train.widgets import RunDetails\n",
-        "RunDetails(local_run).show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Retrieve all iterations\n",
-        "\n",
-        "View the experiment history and see individual metrics for each iteration run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "get metrics",
-          "query history"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "children = list(local_run.get_children())\n",
-        "metricslist = {}\n",
-        "for run in children:\n",
-        "    properties = run.get_properties()\n",
-        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
-        "    metricslist[int(properties['iteration'])] = metrics\n",
-        "\n",
-        "import pandas as pd\n",
-        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
-        "rundata"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Register the best model \n",
-        "\n",
-        "Use the `local_run` object to get the best model and register it into the workspace. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "query history",
-          "register model from history"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "# find the run with the highest accuracy value.\n",
-        "best_run, fitted_model = local_run.get_output()\n",
-        "\n",
-        "# register model in workspace\n",
-        "description = 'Automated Machine Learning Model'\n",
-        "tags = None\n",
-        "local_run.register_model(description=description, tags=tags)\n",
-        "local_run.model_id # Use this id to deploy the model as a web service in Azure"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Test the best model\n",
-        "\n",
-        "Use the model to predict a few random digits.  Display the predicted and the image.  Red font and inverse image (white on black) is used to highlight the misclassified samples.\n",
-        "\n",
-        "Since the model accuracy is high, you might have to run the following code a few times before you can see a misclassified sample."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# find 30 random samples from test set\n",
-        "n = 30\n",
-        "X_test = digits.data[:100, :]\n",
-        "y_test = digits.target[:100]\n",
-        "sample_indices = np.random.permutation(X_test.shape[0])[0:n]\n",
-        "test_samples = X_test[sample_indices]\n",
-        "\n",
-        "\n",
-        "# predict using the  model\n",
-        "result = fitted_model.predict(test_samples)\n",
-        "\n",
-        "# compare actual value vs. the predicted values:\n",
-        "i = 0\n",
-        "plt.figure(figsize = (20, 1))\n",
-        "\n",
-        "for s in sample_indices:\n",
-        "    plt.subplot(1, n, i + 1)\n",
-        "    plt.axhline('')\n",
-        "    plt.axvline('')\n",
-        "    \n",
-        "    # use different color for misclassified sample\n",
-        "    font_color = 'red' if y_test[s] != result[i] else 'black'\n",
-        "    clr_map = plt.cm.gray if y_test[s] != result[i] else plt.cm.Greys\n",
-        "    \n",
-        "    plt.text(x = 2, y = -2, s = result[i], fontsize = 18, color = font_color)\n",
-        "    plt.imshow(X_test[s].reshape(8, 8), cmap = clr_map)\n",
-        "    \n",
-        "    i = i + 1\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Next steps\n",
-        "\n",
-        "In this Azure Machine Learning tutorial, you used Python to:\n",
-        "\n",
-        "> * Set up your development environment\n",
-        "> * Access and examine the data\n",
-        "> * Train using an automated classifier locally with custom parameters\n",
-        "> * Explore the results\n",
-        "> * Review training results\n",
-        "> * Register the best model\n",
-        "\n",
-        "Learn more about [how to configure settings for automatic training](https://aka.ms/aml-how-configure-auto) or [how to use automatic training on a remote resource](https://aka.ms/aml-how-to-auto-remote)."
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "jeffshep"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    },
-    "msauthor": "sgilley"
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial: Train a classification model with automated machine learning\n",
+    "\n",
+    "In this tutorial, you'll learn how to generate a  machine learning model using automated machine learning (automated ML).  Azure Machine Learning can perform algorithm selection and hyperparameter selection in an automated way for you. The final model can then be deployed following the workflow in the [Deploy a model](02.deploy-models.ipynb) tutorial.\n",
+    "\n",
+    "[flow diagram](./imgs/flow2.png)\n",
+    "\n",
+    "Similar to the [train models tutorial](01.train-models.ipynb), this tutorial classifies handwritten images of digits (0-9) from the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset. But this time you don't to specify an algorithm or tune hyperparameters. The automated ML technique iterates over many combinations of algorithms and hyperparameters until it finds the best model based on your criterion.\n",
+    "\n",
+    "You'll learn how to:\n",
+    "\n",
+    "> * Set up your development environment\n",
+    "> * Access and examine the data\n",
+    "> * Train using an automated classifier locally with custom parameters\n",
+    "> * Explore the results\n",
+    "> * Review training results\n",
+    "> * Register the best model\n",
+    "\n",
+    "## Prerequisites\n",
+    "\n",
+    "Use [these instructions](https://aka.ms/aml-how-to-configure-environment) to:  \n",
+    "* Create a workspace and its configuration file (**config.json**)  \n",
+    "* Upload your **config.json** to the same folder as this notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Start a notebook\n",
+    "\n",
+    "To follow along, start a new notebook from the same directory as **config.json** and copy the code from the sections below.\n",
+    "\n",
+    "\n",
+    "## Set up your development environment\n",
+    "\n",
+    "All the setup for your development work can be accomplished in the Python notebook.  Setup includes:\n",
+    "\n",
+    "* Import Python packages\n",
+    "* Configure a workspace to enable communication between your local computer and remote resources\n",
+    "* Create a directory to store training scripts\n",
+    "\n",
+    "### Import packages\n",
+    "Import Python packages you need in this tutorial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import azureml.core\n",
+    "import pandas as pd\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl.run import AutoMLRun\n",
+    "import time\n",
+    "import logging\n",
+    "from sklearn import datasets\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import random\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Configure workspace\n",
+    "\n",
+    "Create a workspace object from the existing workspace. `Workspace.from_config()` reads the file **aml_config/config.json** and loads the details into an object named `ws`.  `ws` is used throughout the rest of the code in this tutorial.\n",
+    "\n",
+    "Once you have a workspace object, specify a name for the experiment and create and register a local directory with the workspace. The history of all runs is recorded under the specified experiment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "# choose a name for the run history container in the workspace\n",
+    "experiment_name = 'automl-classifier'\n",
+    "# project folder\n",
+    "project_folder = './automl-classifier'\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data=output, index=['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore data\n",
+    "\n",
+    "The initial training tutorial used a high-resolution version  of the MNIST dataset (28x28 pixels).  Since auto training requires many iterations, this tutorial uses a smaller resolution version  of the images (8x8 pixels) to demonstrate the concepts while speeding up the time needed for each iteration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "\n",
+    "digits = datasets.load_digits()\n",
+    "\n",
+    "# Exclude the first 100 rows from training so that they can be used for test.\n",
+    "X_train = digits.data[100:,:]\n",
+    "y_train = digits.target[100:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Display some sample images\n",
+    "\n",
+    "Load the data into `numpy` arrays. Then use `matplotlib` to plot 30 random images from the dataset with their labels above them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "count = 0\n",
+    "sample_size = 30\n",
+    "plt.figure(figsize = (16, 6))\n",
+    "for i in np.random.permutation(X_train.shape[0])[:sample_size]:\n",
+    "    count = count + 1\n",
+    "    plt.subplot(1, sample_size, count)\n",
+    "    plt.axhline('')\n",
+    "    plt.axvline('')\n",
+    "    plt.text(x = 2, y = -2, s = y_train[i], fontsize = 18)\n",
+    "    plt.imshow(X_train[i].reshape(8, 8), cmap = plt.cm.Greys)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You now have the necessary packages and data ready for auto training for your model. \n",
+    "\n",
+    "## Auto train a model \n",
+    "\n",
+    "To auto train a model, first define settings for autogeneration and tuning and then run the automatic classifier.\n",
+    "\n",
+    "\n",
+    "### Define settings for autogeneration and tuning\n",
+    "\n",
+    "Define the experiment parameters and models settings for autogeneration and tuning.  \n",
+    "\n",
+    "\n",
+    "|Property| Value in this tutorial |Description|\n",
+    "|----|----|---|\n",
+    "|**primary_metric**|AUC Weighted | Metric that you want to optimize.|\n",
+    "|**max_time_sec**|12,000|Time limit in seconds for each iteration|\n",
+    "|**iterations**|20|Number of iterations. In each iteration, the model trains with the data with a specific pipeline|\n",
+    "|**n_cross_validations**|3|Number of cross validation splits|\n",
+    "|**exit_score**|0.9985|*double* value indicating the target for *primary_metric*. Once the target is surpassed the run terminates|\n",
+    "|**blacklist_algos**|['kNN','LinearSVM']|*Array* of *strings* indicating algorithms to ignore.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "configure automl"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from azureml.train.automl import AutoMLConfig\n",
+    "\n",
+    "##Local compute \n",
+    "Automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             primary_metric = 'AUC_weighted',\n",
+    "                             max_time_sec = 12000,\n",
+    "                             iterations = 20,\n",
+    "                             n_cross_validations = 3,\n",
+    "                             exit_score = 0.9985,\n",
+    "                             blacklist_algos = ['kNN','LinearSVM'],\n",
+    "                             X = X_train,\n",
+    "                             y = y_train,\n",
+    "                             path=project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run the automatic classifier\n",
+    "\n",
+    "Start the experiment to run locally. Define the compute target as local and set the output to true to view progress on the experiment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "local submitted run",
+     "automl"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from azureml.core.experiment import Experiment\n",
+    "experiment=Experiment(ws, experiment_name)\n",
+    "local_run = experiment.submit(Automl_config, show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the results\n",
+    "\n",
+    "Explore the results of automatic training with a Jupyter widget or by examining the experiment history.\n",
+    "\n",
+    "### Jupyter widget\n",
+    "\n",
+    "Use the Jupyter notebook widget to see a graph and a table of all results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "use notebook widget"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(local_run).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve all iterations\n",
+    "\n",
+    "View the experiment history and see individual metrics for each iteration run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "get metrics",
+     "query history"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "children = list(local_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "import pandas as pd\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Register the best model \n",
+    "\n",
+    "Use the `local_run` object to get the best model and register it into the workspace. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "query history",
+     "register model from history"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# find the run with the highest accuracy value.\n",
+    "best_run, fitted_model = local_run.get_output()\n",
+    "\n",
+    "# register model in workspace\n",
+    "description = 'Automated Machine Learning Model'\n",
+    "tags = None\n",
+    "local_run.register_model(description=description, tags=tags)\n",
+    "local_run.model_id # Use this id to deploy the model as a web service in Azure"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test the best model\n",
+    "\n",
+    "Use the model to predict a few random digits.  Display the predicted and the image.  Red font and inverse image (white on black) is used to highlight the misclassified samples.\n",
+    "\n",
+    "Since the model accuracy is high, you might have to run the following code a few times before you can see a misclassified sample."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# find 30 random samples from test set\n",
+    "n = 30\n",
+    "X_test = digits.data[:100, :]\n",
+    "y_test = digits.target[:100]\n",
+    "sample_indices = np.random.permutation(X_test.shape[0])[0:n]\n",
+    "test_samples = X_test[sample_indices]\n",
+    "\n",
+    "\n",
+    "# predict using the  model\n",
+    "result = fitted_model.predict(test_samples)\n",
+    "\n",
+    "# compare actual value vs. the predicted values:\n",
+    "i = 0\n",
+    "plt.figure(figsize = (20, 1))\n",
+    "\n",
+    "for s in sample_indices:\n",
+    "    plt.subplot(1, n, i + 1)\n",
+    "    plt.axhline('')\n",
+    "    plt.axvline('')\n",
+    "    \n",
+    "    # use different color for misclassified sample\n",
+    "    font_color = 'red' if y_test[s] != result[i] else 'black'\n",
+    "    clr_map = plt.cm.gray if y_test[s] != result[i] else plt.cm.Greys\n",
+    "    \n",
+    "    plt.text(x = 2, y = -2, s = result[i], fontsize = 18, color = font_color)\n",
+    "    plt.imshow(X_test[s].reshape(8, 8), cmap = clr_map)\n",
+    "    \n",
+    "    i = i + 1\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next steps\n",
+    "\n",
+    "In this Azure Machine Learning tutorial, you used Python to:\n",
+    "\n",
+    "> * Set up your development environment\n",
+    "> * Access and examine the data\n",
+    "> * Train using an automated classifier locally with custom parameters\n",
+    "> * Explore the results\n",
+    "> * Review training results\n",
+    "> * Register the best model\n",
+    "\n",
+    "Learn more about [how to configure settings for automatic training](https://aka.ms/aml-how-to-configure-auto) or [how to use automatic training on a remote resource](https://aka.ms/aml-how-to-auto-remote)."
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "jeffshep"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  },
+  "msauthor": "sgilley"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}