Update 12/2 2

Update 12/2
Update 12-1-19
2025-12-20 09:37:04 -05:00 · 2018-12-02 19:21:15 -05:00 · 2018-12-02 14:46:52 -05:00 · 2018-12-01 20:37:45 -05:00 · 2018-11-29 11:32:16 -05:00
145 changed files with 168543 additions and 8942 deletions
--- a/00.configuration.ipynb
+++ b/00.configuration.ipynb
@@ -1,313 +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": [
    "# 00. Installation and configuration\n",
    "This notebook configures your library of notebooks to connect to an Azure Machine Learning Workspace.  In this case, a library contains all of the notebooks in the current folder and any nested folders.  You can configure this notebook to use an existing workspace or create a new workspace.\n",
    "\n",
    "## What is an Azure ML Workspace and why do I need one?\n",
    "\n",
    "An AML 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 AML Workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, operationalization, and the monitoring of operationalized models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Access Azure Subscription\n",
    "\n",
    "In order to create an AML Workspace, first you need access to an Azure Subscription.  You can [create your own](https://azure.microsoft.com/en-us/free/) or get your existing subscription information from the [Azure portal](https://portal.azure.com).\n",
    "\n",
    "### 2. If you're running on your own local environment, install Azure ML SDK and other libraries\n",
    "\n",
    "If you are running in your own environment, follow [SDK installation instructions](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment).  If you are running in Azure Notebooks or another Microsoft managed environment, the SDK is already installed.\n",
    "\n",
    "Also install following libraries to your environment. Many of the example notebooks depend on them\n",
    "\n",
    "```\n",
    "(myenv) $ conda install -y matplotlib tqdm scikit-learn\n",
    "```\n",
    "\n",
    "Once installation is complete, check the Azure ML SDK version:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "install"
    ]
   },
   "outputs": [],
   "source": [
    "import azureml.core\n",
    "\n",
    "print(\"SDK Version:\", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Make sure your subscription is registered to use ACI\n",
    "Azure Machine Learning makes use of Azure Container Instance (ACI). You need to ensure your subscription has been registered to use ACI in order be able to deploy a dev/test web service.  If you have run through the quickstart experience you have already performed this step.  Otherwise you will need to use the [Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest) and execute the following commands.\n",
    "\n",
    "```shell\n",
    "# check to see if ACI is already registered\n",
    "(myenv) $ az provider show -n Microsoft.ContainerInstance -o table\n",
    "\n",
    "# if ACI is not registered, run this command.\n",
    "# note you need to be the subscription owner in order to execute this command successfully.\n",
    "(myenv) $ az provider register -n Microsoft.ContainerInstance\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up your Azure Machine Learning workspace\n",
    "\n",
    "### Option 1: You have workspace already\n",
    "If you ran the Azure Machine Learning [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) in Azure Notebooks, you already have a configured workspace!  You can go to your Azure Machine Learning Getting Started library, view *config.json* file, and copy-paste the values for subscription ID, resource group and workspace name below.\n",
    "\n",
    "If you have a workspace created another way, [these instructions](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#create-workspace-configuration-file) describe how to get your subscription and workspace information.\n",
    "\n",
    "If this cell succeeds, you're done configuring this library!  Otherwise continue to follow the instructions in the rest of the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\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",
    "   ws.write_config()\n",
    "   print('Workspace configuration succeeded. You are all set!')\n",
    "except:\n",
    "   print('Workspace not found. Run the cells below.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Option 2: You don't have workspace yet\n",
    "\n",
    "\n",
    "#### Requirements\n",
    "\n",
    "Inside your Azure subscription, you will need access to a _resource group_, which organizes Azure resources and provides a default region for the resources in a group.  You can see what resource groups to which you have access, or create a new one in the [Azure portal](https://portal.azure.com).  If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n",
    "\n",
    "To create or access an Azure ML Workspace, you will need to import the AML library and the following information:\n",
    "* A name for your workspace\n",
    "* Your subscription id\n",
    "* The resource group name\n",
    "\n",
    "**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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Supported Azure Regions\n",
    "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": [
    "import os\n",
    "\n",
    "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\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create the workspace\n",
    "This cell will create an AML workspace for you in a subscription provided you have the correct permissions.\n",
    "\n",
    "This will fail when:\n",
    "1. You do not have permission to create a workspace in the resource group\n",
    "2. You do not have permission to create a resource group if it's non-existing.\n",
    "2. 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, please work with your IT admin to provide you with the appropriate permissions or to provision the required resources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "create workspace"
    ]
   },
   "outputs": [],
   "source": [
    "# import the Workspace class and check the azureml 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",
    "                      create_resource_group = True,\n",
    "                      exist_ok = True)\n",
    "ws.get_details()\n",
    "ws.write_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 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",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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   "pygments_lexer": "ipython3",
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 }
--- a/01.getting-started/03.train-on-aci/03.train-on-aci.ipynb
+++ b/01.getting-started/03.train-on-aci/03.train-on-aci.ipynb
@@ -1,289 +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": [
    "# 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"
   }
  ],
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   "display_name": "Python 3.6",
   "language": "python",
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--- a/01.getting-started/03.train-on-aci/train.py
+++ b/01.getting-started/03.train-on-aci/train.py
@@ -1,44 +0,0 @@
 # Copyright (c) Microsoft. All rights reserved.
 # Licensed under the MIT license.
 from sklearn.datasets import load_diabetes
 from sklearn.linear_model import Ridge
 from sklearn.metrics import mean_squared_error
 from sklearn.model_selection import train_test_split
 from azureml.core.run import Run
 from sklearn.externals import joblib
 import os
 import numpy as np
 os.makedirs('./outputs', exist_ok=True)
 X, y = load_diabetes(return_X_y=True)
 run = Run.get_context()
 X_train, X_test, y_train, y_test = train_test_split(X, y,
                                                    test_size=0.2,
                                                    random_state=0)
 data = {"train": {"X": X_train, "y": y_train},
        "test": {"X": X_test, "y": y_test}}
 # list of numbers from 0.0 to 1.0 with a 0.05 interval
 alphas = np.arange(0.0, 1.0, 0.05)
 for alpha in alphas:
    # Use Ridge algorithm to create a regression model
    reg = Ridge(alpha=alpha)
    reg.fit(data["train"]["X"], data["train"]["y"])
    preds = reg.predict(data["test"]["X"])
    mse = mean_squared_error(preds, data["test"]["y"])
    run.log('alpha', alpha)
    run.log('mse', mse)
    model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)
    # save model in the outputs folder so it automatically get uploaded
    with open(model_file_name, "wb") as file:
        joblib.dump(value=reg, filename=os.path.join('./outputs/',
                                                     model_file_name))
    print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))
--- a/01.getting-started/05.train-in-spark/05.train-in-spark.ipynb
+++ b/01.getting-started/05.train-in-spark/05.train-in-spark.ipynb
@@ -1,331 +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": [
    "# 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
 }
--- a/01.getting-started/05.train-in-spark/iris.csv
+++ b/01.getting-started/05.train-in-spark/iris.csv
@@ -1,150 +0,0 @@
 5.1,3.5,1.4,0.2,Iris-setosa
 4.9,3.0,1.4,0.2,Iris-setosa
 4.7,3.2,1.3,0.2,Iris-setosa
 4.6,3.1,1.5,0.2,Iris-setosa
 5.0,3.6,1.4,0.2,Iris-setosa
 5.4,3.9,1.7,0.4,Iris-setosa
 4.6,3.4,1.4,0.3,Iris-setosa
 5.0,3.4,1.5,0.2,Iris-setosa
 4.4,2.9,1.4,0.2,Iris-setosa
 4.9,3.1,1.5,0.1,Iris-setosa
 5.4,3.7,1.5,0.2,Iris-setosa
 4.8,3.4,1.6,0.2,Iris-setosa
 4.8,3.0,1.4,0.1,Iris-setosa
 4.3,3.0,1.1,0.1,Iris-setosa
 5.8,4.0,1.2,0.2,Iris-setosa
 5.7,4.4,1.5,0.4,Iris-setosa
 5.4,3.9,1.3,0.4,Iris-setosa
 5.1,3.5,1.4,0.3,Iris-setosa
 5.7,3.8,1.7,0.3,Iris-setosa
 5.1,3.8,1.5,0.3,Iris-setosa
 5.4,3.4,1.7,0.2,Iris-setosa
 5.1,3.7,1.5,0.4,Iris-setosa
 4.6,3.6,1.0,0.2,Iris-setosa
 5.1,3.3,1.7,0.5,Iris-setosa
 4.8,3.4,1.9,0.2,Iris-setosa
 5.0,3.0,1.6,0.2,Iris-setosa
 5.0,3.4,1.6,0.4,Iris-setosa
 5.2,3.5,1.5,0.2,Iris-setosa
 5.2,3.4,1.4,0.2,Iris-setosa
 4.7,3.2,1.6,0.2,Iris-setosa
 4.8,3.1,1.6,0.2,Iris-setosa
 5.4,3.4,1.5,0.4,Iris-setosa
 5.2,4.1,1.5,0.1,Iris-setosa
 5.5,4.2,1.4,0.2,Iris-setosa
 4.9,3.1,1.5,0.1,Iris-setosa
 5.0,3.2,1.2,0.2,Iris-setosa
 5.5,3.5,1.3,0.2,Iris-setosa
 4.9,3.1,1.5,0.1,Iris-setosa
 4.4,3.0,1.3,0.2,Iris-setosa
 5.1,3.4,1.5,0.2,Iris-setosa
 5.0,3.5,1.3,0.3,Iris-setosa
 4.5,2.3,1.3,0.3,Iris-setosa
 4.4,3.2,1.3,0.2,Iris-setosa
 5.0,3.5,1.6,0.6,Iris-setosa
 5.1,3.8,1.9,0.4,Iris-setosa
 4.8,3.0,1.4,0.3,Iris-setosa
 5.1,3.8,1.6,0.2,Iris-setosa
 4.6,3.2,1.4,0.2,Iris-setosa
 5.3,3.7,1.5,0.2,Iris-setosa
 5.0,3.3,1.4,0.2,Iris-setosa
 7.0,3.2,4.7,1.4,Iris-versicolor
 6.4,3.2,4.5,1.5,Iris-versicolor
 6.9,3.1,4.9,1.5,Iris-versicolor
 5.5,2.3,4.0,1.3,Iris-versicolor
 6.5,2.8,4.6,1.5,Iris-versicolor
 5.7,2.8,4.5,1.3,Iris-versicolor
 6.3,3.3,4.7,1.6,Iris-versicolor
 4.9,2.4,3.3,1.0,Iris-versicolor
 6.6,2.9,4.6,1.3,Iris-versicolor
 5.2,2.7,3.9,1.4,Iris-versicolor
 5.0,2.0,3.5,1.0,Iris-versicolor
 5.9,3.0,4.2,1.5,Iris-versicolor
 6.0,2.2,4.0,1.0,Iris-versicolor
 6.1,2.9,4.7,1.4,Iris-versicolor
 5.6,2.9,3.6,1.3,Iris-versicolor
 6.7,3.1,4.4,1.4,Iris-versicolor
 5.6,3.0,4.5,1.5,Iris-versicolor
 5.8,2.7,4.1,1.0,Iris-versicolor
 6.2,2.2,4.5,1.5,Iris-versicolor
 5.6,2.5,3.9,1.1,Iris-versicolor
 5.9,3.2,4.8,1.8,Iris-versicolor
 6.1,2.8,4.0,1.3,Iris-versicolor
 6.3,2.5,4.9,1.5,Iris-versicolor
 6.1,2.8,4.7,1.2,Iris-versicolor
 6.4,2.9,4.3,1.3,Iris-versicolor
 6.6,3.0,4.4,1.4,Iris-versicolor
 6.8,2.8,4.8,1.4,Iris-versicolor
 6.7,3.0,5.0,1.7,Iris-versicolor
 6.0,2.9,4.5,1.5,Iris-versicolor
 5.7,2.6,3.5,1.0,Iris-versicolor
 5.5,2.4,3.8,1.1,Iris-versicolor
 5.5,2.4,3.7,1.0,Iris-versicolor
 5.8,2.7,3.9,1.2,Iris-versicolor
 6.0,2.7,5.1,1.6,Iris-versicolor
 5.4,3.0,4.5,1.5,Iris-versicolor
 6.0,3.4,4.5,1.6,Iris-versicolor
 6.7,3.1,4.7,1.5,Iris-versicolor
 6.3,2.3,4.4,1.3,Iris-versicolor
 5.6,3.0,4.1,1.3,Iris-versicolor
 5.5,2.5,4.0,1.3,Iris-versicolor
 5.5,2.6,4.4,1.2,Iris-versicolor
 6.1,3.0,4.6,1.4,Iris-versicolor
 5.8,2.6,4.0,1.2,Iris-versicolor
 5.0,2.3,3.3,1.0,Iris-versicolor
 5.6,2.7,4.2,1.3,Iris-versicolor
 5.7,3.0,4.2,1.2,Iris-versicolor
 5.7,2.9,4.2,1.3,Iris-versicolor
 6.2,2.9,4.3,1.3,Iris-versicolor
 5.1,2.5,3.0,1.1,Iris-versicolor
 5.7,2.8,4.1,1.3,Iris-versicolor
 6.3,3.3,6.0,2.5,Iris-virginica
 5.8,2.7,5.1,1.9,Iris-virginica
 7.1,3.0,5.9,2.1,Iris-virginica
 6.3,2.9,5.6,1.8,Iris-virginica
 6.5,3.0,5.8,2.2,Iris-virginica
 7.6,3.0,6.6,2.1,Iris-virginica
 4.9,2.5,4.5,1.7,Iris-virginica
 7.3,2.9,6.3,1.8,Iris-virginica
 6.7,2.5,5.8,1.8,Iris-virginica
 7.2,3.6,6.1,2.5,Iris-virginica
 6.5,3.2,5.1,2.0,Iris-virginica
 6.4,2.7,5.3,1.9,Iris-virginica
 6.8,3.0,5.5,2.1,Iris-virginica
 5.7,2.5,5.0,2.0,Iris-virginica
 5.8,2.8,5.1,2.4,Iris-virginica
 6.4,3.2,5.3,2.3,Iris-virginica
 6.5,3.0,5.5,1.8,Iris-virginica
 7.7,3.8,6.7,2.2,Iris-virginica
 7.7,2.6,6.9,2.3,Iris-virginica
 6.0,2.2,5.0,1.5,Iris-virginica
 6.9,3.2,5.7,2.3,Iris-virginica
 5.6,2.8,4.9,2.0,Iris-virginica
 7.7,2.8,6.7,2.0,Iris-virginica
 6.3,2.7,4.9,1.8,Iris-virginica
 6.7,3.3,5.7,2.1,Iris-virginica
 7.2,3.2,6.0,1.8,Iris-virginica
 6.2,2.8,4.8,1.8,Iris-virginica
 6.1,3.0,4.9,1.8,Iris-virginica
 6.4,2.8,5.6,2.1,Iris-virginica
 7.2,3.0,5.8,1.6,Iris-virginica
 7.4,2.8,6.1,1.9,Iris-virginica
 7.9,3.8,6.4,2.0,Iris-virginica
 6.4,2.8,5.6,2.2,Iris-virginica
 6.3,2.8,5.1,1.5,Iris-virginica
 6.1,2.6,5.6,1.4,Iris-virginica
 7.7,3.0,6.1,2.3,Iris-virginica
 6.3,3.4,5.6,2.4,Iris-virginica
 6.4,3.1,5.5,1.8,Iris-virginica
 6.0,3.0,4.8,1.8,Iris-virginica
 6.9,3.1,5.4,2.1,Iris-virginica
 6.7,3.1,5.6,2.4,Iris-virginica
 6.9,3.1,5.1,2.3,Iris-virginica
 5.8,2.7,5.1,1.9,Iris-virginica
 6.8,3.2,5.9,2.3,Iris-virginica
 6.7,3.3,5.7,2.5,Iris-virginica
 6.7,3.0,5.2,2.3,Iris-virginica
 6.3,2.5,5.0,1.9,Iris-virginica
 6.5,3.0,5.2,2.0,Iris-virginica
 6.2,3.4,5.4,2.3,Iris-virginica
 5.9,3.0,5.1,1.8,Iris-virginica
--- a/01.getting-started/05.train-in-spark/train-spark.py
+++ b/01.getting-started/05.train-in-spark/train-spark.py
@@ -1,94 +0,0 @@
 # Copyright (c) Microsoft. All rights reserved.
 # Licensed under the MIT license.
 import numpy as np
 import pyspark
 import os
 import urllib
 import sys
 from pyspark.sql.functions import *
 from pyspark.ml.classification import *
 from pyspark.ml.evaluation import *
 from pyspark.ml.feature import *
 from pyspark.sql.types import StructType, StructField
 from pyspark.sql.types import DoubleType, IntegerType, StringType
 from azureml.core.run import Run
 # initialize logger
 run = Run.get_context()
 # start Spark session
 spark = pyspark.sql.SparkSession.builder.appName('Iris').getOrCreate()
 # print runtime versions
 print('****************')
 print('Python version: {}'.format(sys.version))
 print('Spark version: {}'.format(spark.version))
 print('****************')
 # load iris.csv into Spark dataframe
 schema = StructType([
    StructField("sepal-length", DoubleType()),
    StructField("sepal-width", DoubleType()),
    StructField("petal-length", DoubleType()),
    StructField("petal-width", DoubleType()),
    StructField("class", StringType())
 ])
 data = spark.read.csv('iris.csv', header=False, schema=schema)
 print("First 10 rows of Iris dataset:")
 data.show(10)
 # vectorize all numerical columns into a single feature column
 feature_cols = data.columns[:-1]
 assembler = pyspark.ml.feature.VectorAssembler(
    inputCols=feature_cols, outputCol='features')
 data = assembler.transform(data)
 # convert text labels into indices
 data = data.select(['features', 'class'])
 label_indexer = pyspark.ml.feature.StringIndexer(
    inputCol='class', outputCol='label').fit(data)
 data = label_indexer.transform(data)
 # only select the features and label column
 data = data.select(['features', 'label'])
 print("Reading for machine learning")
 data.show(10)
 # change regularization rate and you will likely get a different accuracy.
 reg = 0.01
 # load regularization rate from argument if present
 if len(sys.argv) > 1:
    reg = float(sys.argv[1])
 # log regularization rate
 run.log("Regularization Rate", reg)
 # use Logistic Regression to train on the training set
 train, test = data.randomSplit([0.70, 0.30])
 lr = pyspark.ml.classification.LogisticRegression(regParam=reg)
 model = lr.fit(train)
 # predict on the test set
 prediction = model.transform(test)
 print("Prediction")
 prediction.show(10)
 # evaluate the accuracy of the model using the test set
 evaluator = pyspark.ml.evaluation.MulticlassClassificationEvaluator(
    metricName='accuracy')
 accuracy = evaluator.evaluate(prediction)
 print()
 print('#####################################')
 print('Regularization rate is {}'.format(reg))
 print("Accuracy is {}".format(accuracy))
 print('#####################################')
 print()
 # log accuracy
 run.log('Accuracy', accuracy)
--- a/automl/00.configuration.ipynb
+++ b/automl/00.configuration.ipynb
@@ -1,224 +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 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"
  },
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   "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
 }
--- a/configuration.ipynb
+++ b/configuration.ipynb
@@ -0,0 +1,376 @@
 {
  "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": [
        "# Configuration\n",
        "\n",
        "_**Setting up your Azure Machine Learning services workspace and configuring your notebook library**_\n",
        "\n",
        "---\n",
        "---\n",
        "\n",
        "## Table of Contents\n",
        "\n",
        "1. [Introduction](#Introduction)\n",
        "    1. What is an Azure Machine Learning workspace\n",
        "1. [Setup](#Setup)\n",
        "    1. Azure subscription\n",
        "    1. Azure ML SDK and other library installation\n",
        "    1. Azure Container Instance registration\n",
        "1. [Configure your Azure ML Workspace](#Configure%20your%20Azure%20ML%20workspace)\n",
        "    1. Workspace parameters\n",
        "    1. Access your workspace\n",
        "    1. Create a new workspace\n",
        "    1. Create compute resources\n",
        "1. [Next steps](#Next%20steps)\n",
        "\n",
        "---\n",
        "\n",
        "## Introduction\n",
        "\n",
        "This notebook configures your library of notebooks to connect to an Azure Machine Learning (ML) workspace.  In this case, a library contains all of the notebooks in the current folder and any nested folders.  You can configure this notebook library to use an existing workspace or create a new workspace.\n",
        "\n",
        "Typically you will need to run this notebook only once per notebook library as all other notebooks will use connection information that is written here.  If you want to redirect your notebook library to work with a different workspace, then you should re-run this notebook.\n",
        "\n",
        "In this notebook you will\n",
        "* Learn about getting an Azure subscription\n",
        "* Specify your workspace parameters\n",
        "* Access or create your workspace\n",
        "* Add a default compute cluster for your workspace\n",
        "\n",
        "### What is an Azure Machine Learning workspace\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, deployment, inferencing, and the monitoring of deployed models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\n",
        "\n",
        "This section describes activities required before you can access any Azure ML services functionality."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 1. Azure Subscription\n",
        "\n",
        "In order to create an Azure ML Workspace, first you need access to an Azure subscription.  An Azure subscription allows you to manage storage, compute, and other assets in the Azure cloud.  You can [create a new subscription](https://azure.microsoft.com/en-us/free/) or access existing subscription information from the [Azure portal](https://portal.azure.com).  Later in this notebook you will need information such as your subscription ID in order to create and access AML workspaces.\n",
        "\n",
        "### 2. Azure ML SDK and other library installation\n",
        "\n",
        "If you are running in your own environment, follow [SDK installation instructions](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment).  If you are running in Azure Notebooks or another Microsoft managed environment, the SDK is already installed.\n",
        "\n",
        "Also install following libraries to your environment. Many of the example notebooks depend on them\n",
        "\n",
        "```\n",
        "(myenv) $ conda install -y matplotlib tqdm scikit-learn\n",
        "```\n",
        "\n",
        "Once installation is complete, the following cell checks the Azure ML SDK version:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "install"
        ]
      },
      "outputs": [],
      "source": [
        "import azureml.core\n",
        "\n",
        "print(\"This notebook was created using version 1.0.2 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "If you are using an older version of the SDK then this notebook was created using, you should upgrade your SDK.\n",
        "\n",
        "### 3. Azure Container Instance registration\n",
        "Azure Machine Learning uses of [Azure Container Instance (ACI)](https://azure.microsoft.com/services/container-instances) to deploy dev/test web services. An Azure subscription needs to be registered to use ACI.  If you or the subscription owner have not yet registered ACI on your subscription, you will need to use the [Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest) and execute the following commands.  Note that if you ran through the AML [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) you have already registered ACI. \n",
        "\n",
        "```shell\n",
        "# check to see if ACI is already registered\n",
        "(myenv) $ az provider show -n Microsoft.ContainerInstance -o table\n",
        "\n",
        "# if ACI is not registered, run this command.\n",
        "# note you need to be the subscription owner in order to execute this command successfully.\n",
        "(myenv) $ az provider register -n Microsoft.ContainerInstance\n",
        "```\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Configure your Azure ML workspace\n",
        "\n",
        "### Workspace parameters\n",
        "\n",
        "To use an AML Workspace, you will need to import the Azure ML SDK and supply the following information:\n",
        "* Your subscription id\n",
        "* A resource group name\n",
        "* (optional) The region that will host your workspace\n",
        "* A name for your workspace\n",
        "\n",
        "You can get your subscription ID from the [Azure portal](https://portal.azure.com).\n",
        "\n",
        "You will also need access to a [_resource group_](https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-group-overview#resource-groups), which organizes Azure resources and provides a default region for the resources in a group.  You can see what resource groups to which you have access, or create a new one in the [Azure portal](https://portal.azure.com).  If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n",
        "\n",
        "The region to host your workspace will be used if you are creating a new workspace.  You do not need to specify this if you are using an existing workspace. You can find the list of supported regions [here](https://azure.microsoft.com/en-us/global-infrastructure/services/?products=machine-learning-service).  You should pick a region that is close to your location or that contains your data.\n",
        "\n",
        "The name for your workspace is unique within the subscription and should be descriptive enough to discern among other AML Workspaces.  The subscription may be used only by you, or it may be used by your department or your entire enterprise, so choose a name that makes sense for your situation.\n",
        "\n",
        "The following cell allows you to specify your workspace parameters.  This cell uses the python method `os.getenv` to read values from environment variables which is useful for automation.  If no environment variable exists, the parameters will be set to the specified default values.  \n",
        "\n",
        "If you ran the Azure Machine Learning [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) in Azure Notebooks, you already have a configured workspace!  You can go to your Azure Machine Learning Getting Started library, view *config.json* file, and copy-paste the values for subscription ID, resource group and workspace name below.\n",
        "\n",
        "Replace the default values in the cell below with your workspace parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "subscription_id = os.getenv(\"SUBSCRIPTION_ID\", default=\"<my-subscription-id>\")\n",
        "resource_group = os.getenv(\"RESOURCE_GROUP\", default=\"<my-resource-group>\")\n",
        "workspace_name = os.getenv(\"WORKSPACE_NAME\", default=\"<my-workspace-name>\")\n",
        "workspace_region = os.getenv(\"WORKSPACE_REGION\", default=\"eastus2\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Access your workspace\n",
        "\n",
        "The following cell uses the Azure ML SDK to attempt to load the workspace specified by your parameters.  If this cell succeeds, your notebook library will be configured to access the workspace from all notebooks using the `Workspace.from_config()` method.  The cell can fail if the specified workspace doesn't exist or you don't have permissions to access it. "
      ]
    },
    {
      "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",
        "    # write the details of the workspace to a configuration file to the notebook library\n",
        "    ws.write_config()\n",
        "    print(\"Workspace configuration succeeded. Skip the workspace creation steps below\")\n",
        "except:\n",
        "    print(\"Workspace not accessible. Change your parameters or create a new workspace below\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create a new workspace\n",
        "\n",
        "If you don't have an existing workspace and are the owner of the subscription or resource group, you can create a new workspace.  If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n",
        "\n",
        "**Note**: As with other Azure services, there are limits on certain resources (for example AmlCompute quota) associated with the Azure ML 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.\n",
        "\n",
        "This cell will create an Azure ML workspace for you in a subscription provided you have the correct permissions.\n",
        "\n",
        "This will fail if:\n",
        "* You do not have permission to create a workspace in the resource group\n",
        "* You do not have permission to create a resource group if it's non-existing.\n",
        "* 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, please work with your IT admin to provide you with the appropriate permissions or to provision the required resources."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "create workspace"
        ]
      },
      "outputs": [],
      "source": [
        "from azureml.core import Workspace\n",
        "\n",
        "# Create the workspace using the specified parameters\n",
        "ws = Workspace.create(name = workspace_name,\n",
        "                      subscription_id = subscription_id,\n",
        "                      resource_group = resource_group, \n",
        "                      location = workspace_region,\n",
        "                      create_resource_group = True,\n",
        "                      exist_ok = True)\n",
        "ws.get_details()\n",
        "\n",
        "# write the details of the workspace to a configuration file to the notebook library\n",
        "ws.write_config()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create compute resources for your training experiments\n",
        "\n",
        "Many of the sample notebooks use Azure ML managed compute (AmlCompute) to train models using a dynamically scalable pool of compute. In this section you will create default compute clusters for use by the other notebooks and any other operations you choose.\n",
        "\n",
        "To create a cluster, you need to specify a compute configuration that specifies the type of machine to be used and the scalability behaviors.  Then you choose a name for the cluster that is unique within the workspace that can be used to address the cluster later.\n",
        "\n",
        "The cluster parameters are:\n",
        "* vm_size - this describes the virtual machine type and size used in the cluster.  All machines in the cluster are the same type.  You can get the list of vm sizes available in your region by using the CLI command\n",
        "\n",
        "```shell\n",
        "az vm list-skus -o tsv\n",
        "```\n",
        "* min_nodes - this sets the minimum size of the cluster.  If you set the minimum to 0 the cluster will shut down all nodes while note in use.  Setting this number to a value higher than 0 will allow for faster start-up times, but you will also be billed when the cluster is not in use.\n",
        "* max_nodes - this sets the maximum size of the cluster.  Setting this to a larger number allows for more concurrency and a greater distributed processing of scale-out jobs.\n",
        "\n",
        "\n",
        "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 cpucluster\")\n",
        "except ComputeTargetException:\n",
        "    print(\"Creating new cpucluster\")\n",
        "    \n",
        "    # Specify the configuration for the new cluster\n",
        "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_D2_V2\",\n",
        "                                                           min_nodes=0,\n",
        "                                                           max_nodes=4)\n",
        "\n",
        "    # Create the cluster with the specified name and configuration\n",
        "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
        "    \n",
        "    # Wait for the cluster to complete, show the output log\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",
        "# Verify that cluster does not exist already\n",
        "try:\n",
        "    gpu_cluster = ComputeTarget(workspace=ws, name=gpu_cluster_name)\n",
        "    print(\"Found existing gpu cluster\")\n",
        "except ComputeTargetException:\n",
        "    print(\"Creating new gpucluster\")\n",
        "    \n",
        "    # Specify the configuration for the new cluster\n",
        "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_NC6\",\n",
        "                                                           min_nodes=0,\n",
        "                                                           max_nodes=4)\n",
        "    # Create the cluster with the specified name and configuration\n",
        "    gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, compute_config)\n",
        "\n",
        "    # Wait for the cluster to complete, show the output log\n",
        "    gpu_cluster.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "---\n",
        "\n",
        "## Next steps\n",
        "\n",
        "In this notebook you configured this notebook library to connect easily to an Azure ML workspace.  You can copy this notebook to your own libraries to connect them to you workspace, or use it to bootstrap new workspaces completely.\n",
        "\n",
        "If you came here from another notebook, you can return there and complete that exercise, or you can try out the [Tutorials](./tutorials) or jump into \"how-to\" notebooks and start creating and deploying models.  A good place to start is the [train in notebook](./how-to-use-azureml/training/train-in-notebook) example that walks through a simplified but complete end to end machine learning process."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "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": "3.6.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/databricks/01.Installation_and_Configuration.ipynb
+++ b/databricks/01.Installation_and_Configuration.ipynb
@@ -1 +0,0 @@
 {"cells":[{"cell_type":"markdown","source":["Azure ML & Azure Databricks notebooks by Parashar Shah.\n\nCopyright (c) Microsoft Corporation. All rights reserved.\n\nLicensed under the MIT License."],"metadata":{}},{"cell_type":"markdown","source":["Please ensure you have run this notebook before proceeding."],"metadata":{}},{"cell_type":"markdown","source":["Now we support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package (during private preview). You can select the option to attach the library to all clusters or just one cluster.\n\nProvide this full string to install the SDK:\n\nazureml-sdk[databricks]"],"metadata":{}},{"cell_type":"code","source":["import azureml.core\n\n# Check core SDK version number - based on build number of preview/master.\nprint(\"SDK version:\", azureml.core.VERSION)"],"metadata":{},"outputs":[],"execution_count":4},{"cell_type":"code","source":["subscription_id = \"<your-subscription-id>\"\nresource_group = \"<your-existing-resource-group>\"\nworkspace_name = \"<a-new-or-existing-workspace; it is unrelated to Databricks workspace>\"\nworkspace_region = \"<your-resource group-region>\""],"metadata":{},"outputs":[],"execution_count":5},{"cell_type":"code","source":["# import the Workspace class and check the azureml SDK version\n# exist_ok checks if workspace exists or not.\n\nfrom azureml.core import Workspace\n\nws = Workspace.create(name = workspace_name,\n                      subscription_id = subscription_id,\n                      resource_group = resource_group, \n                      location = workspace_region,\n                      exist_ok=True)\n\nws.get_details()"],"metadata":{},"outputs":[],"execution_count":6},{"cell_type":"code","source":["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.\nws.write_config()"],"metadata":{},"outputs":[],"execution_count":7},{"cell_type":"code","source":["%sh\ncat /databricks/driver/aml_config/config.json"],"metadata":{},"outputs":[],"execution_count":8},{"cell_type":"code","source":["# import the Workspace class and check the azureml SDK version\nfrom azureml.core import Workspace\n\nws = Workspace.from_config()\nprint('Workspace name: ' + ws.name, \n      'Azure region: ' + ws.location, \n      'Subscription id: ' + ws.subscription_id, \n      'Resource group: ' + ws.resource_group, sep = '\\n')"],"metadata":{},"outputs":[],"execution_count":9},{"cell_type":"code","source":["dbutils.notebook.exit(\"success\")"],"metadata":{},"outputs":[],"execution_count":10},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":11}],"metadata":{"name":"01.Installation_and_Configuration","notebookId":3874566296719377},"nbformat":4,"nbformat_minor":0}
--- a/databricks/02.Ingest_data.ipynb
+++ b/databricks/02.Ingest_data.ipynb
@@ -1 +0,0 @@
 {"cells":[{"cell_type":"markdown","source":["Azure ML & Azure Databricks notebooks by Parashar Shah.\n\nCopyright (c) Microsoft Corporation. All rights reserved.\n\nLicensed under the MIT License."],"metadata":{}},{"cell_type":"markdown","source":["Please ensure you have run all previous notebooks in sequence before running this."],"metadata":{}},{"cell_type":"markdown","source":["#Data Ingestion"],"metadata":{}},{"cell_type":"code","source":["import os\nimport urllib"],"metadata":{},"outputs":[],"execution_count":4},{"cell_type":"code","source":["# Download AdultCensusIncome.csv from Azure CDN. This file has 32,561 rows.\nbasedataurl = \"https://amldockerdatasets.azureedge.net\"\ndatafile = \"AdultCensusIncome.csv\"\ndatafile_dbfs = os.path.join(\"/dbfs\", datafile)\n\nif os.path.isfile(datafile_dbfs):\n    print(\"found {} at {}\".format(datafile, datafile_dbfs))\nelse:\n    print(\"downloading {} to {}\".format(datafile, datafile_dbfs))\n    urllib.request.urlretrieve(os.path.join(basedataurl, datafile), datafile_dbfs)"],"metadata":{},"outputs":[],"execution_count":5},{"cell_type":"code","source":["# Create a Spark dataframe out of the csv file.\ndata_all = sqlContext.read.format('csv').options(header='true', inferSchema='true', ignoreLeadingWhiteSpace='true', ignoreTrailingWhiteSpace='true').load(datafile)\nprint(\"({}, {})\".format(data_all.count(), len(data_all.columns)))\ndata_all.printSchema()"],"metadata":{},"outputs":[],"execution_count":6},{"cell_type":"code","source":["#renaming columns\ncolumns_new = [col.replace(\"-\", \"_\") for col in data_all.columns]\ndata_all = data_all.toDF(*columns_new)\ndata_all.printSchema()"],"metadata":{},"outputs":[],"execution_count":7},{"cell_type":"code","source":["display(data_all.limit(5))"],"metadata":{},"outputs":[],"execution_count":8},{"cell_type":"markdown","source":["#Data Preparation"],"metadata":{}},{"cell_type":"code","source":["# Choose feature columns and the label column.\nlabel = \"income\"\nxvals_all = set(data_all.columns) - {label}\n\n#dbutils.widgets.remove(\"xvars_multiselect\")\ndbutils.widgets.removeAll()\n\ndbutils.widgets.multiselect('xvars_multiselect', 'hours_per_week', xvals_all)\nxvars_multiselect = dbutils.widgets.get(\"xvars_multiselect\")\nxvars = xvars_multiselect.split(\",\")\n\nprint(\"label = {}\".format(label))\nprint(\"features = {}\".format(xvars))\n\ndata = data_all.select([*xvars, label])\n\n# Split data into train and test.\ntrain, test = data.randomSplit([0.75, 0.25], seed=123)\n\nprint(\"train ({}, {})\".format(train.count(), len(train.columns)))\nprint(\"test ({}, {})\".format(test.count(), len(test.columns)))"],"metadata":{},"outputs":[],"execution_count":10},{"cell_type":"markdown","source":["#Data Persistence"],"metadata":{}},{"cell_type":"code","source":["# Write the train and test data sets to intermediate storage\ntrain_data_path = \"AdultCensusIncomeTrain\"\ntest_data_path = \"AdultCensusIncomeTest\"\n\ntrain_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTrain\")\ntest_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTest\")\n\ntrain.write.mode('overwrite').parquet(train_data_path)\ntest.write.mode('overwrite').parquet(test_data_path)\nprint(\"train and test datasets saved to {} and {}\".format(train_data_path_dbfs, test_data_path_dbfs))"],"metadata":{},"outputs":[],"execution_count":12},{"cell_type":"code","source":["dbutils.notebook.exit(\"success\")"],"metadata":{},"outputs":[],"execution_count":13},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":14}],"metadata":{"name":"02.Ingest_data","notebookId":3874566296719393},"nbformat":4,"nbformat_minor":0}
--- a/databricks/03a.Build_model.ipynb
+++ b/databricks/03a.Build_model.ipynb
--- a/databricks/03b.Build_model_runHistory.ipynb
+++ b/databricks/03b.Build_model_runHistory.ipynb
--- a/databricks/04.Deploy_to_ACI.ipynb
+++ b/databricks/04.Deploy_to_ACI.ipynb
--- a/databricks/05.Deploy_to_AKS_existingImage.ipynb
+++ b/databricks/05.Deploy_to_AKS_existingImage.ipynb
@@ -1 +0,0 @@
 {"cells":[{"cell_type":"markdown","source":["Azure ML & Azure Databricks notebooks by Parashar Shah.\n\nCopyright (c) Microsoft Corporation. All rights reserved.\n\nLicensed under the MIT License."],"metadata":{}},{"cell_type":"markdown","source":["Please ensure you have run all previous notebooks in sequence before running this. This notebook uses image from ACI notebook for deploying to AKS."],"metadata":{}},{"cell_type":"code","source":["from azureml.core import Workspace\nimport azureml.core\n\n# Check core SDK version number\nprint(\"SDK version:\", azureml.core.VERSION)\n\n#'''\nws = Workspace.from_config()\nprint('Workspace name: ' + ws.name, \n      'Azure region: ' + ws.location, \n      'Subscription id: ' + ws.subscription_id, \n      'Resource group: ' + ws.resource_group, sep = '\\n')\n#'''"],"metadata":{},"outputs":[],"execution_count":3},{"cell_type":"code","source":["# List images by ws\n\nfrom azureml.core.image import ContainerImage\nfor i in ContainerImage.list(workspace = ws):\n    print('{}(v.{} [{}]) stored at {} with build log {}'.format(i.name, i.version, i.creation_state, i.image_location, i.image_build_log_uri))"],"metadata":{},"outputs":[],"execution_count":4},{"cell_type":"code","source":["from azureml.core.image import Image\nmyimage = Image(workspace=ws, id=\"aciws:25\")"],"metadata":{},"outputs":[],"execution_count":5},{"cell_type":"code","source":["#create AKS compute\n#it may take 20-25 minutes to create a new cluster\n\nfrom azureml.core.compute import AksCompute, ComputeTarget\n\n# Use the default configuration (can also provide parameters to customize)\nprov_config = AksCompute.provisioning_configuration()\n\naks_name = 'ps-aks-clus2' \n\n# Create the cluster\naks_target = ComputeTarget.create(workspace = ws, \n                                  name = aks_name, \n                                  provisioning_configuration = prov_config)\n\naks_target.wait_for_completion(show_output = True)\n\nprint(aks_target.provisioning_state)\nprint(aks_target.provisioning_errors)"],"metadata":{},"outputs":[],"execution_count":6},{"cell_type":"code","source":["from azureml.core.webservice import Webservice\nhelp( Webservice.deploy_from_image)"],"metadata":{},"outputs":[],"execution_count":7},{"cell_type":"code","source":["from azureml.core.webservice import Webservice, AksWebservice\nfrom azureml.core.image import ContainerImage\n\n#Set the web service configuration (using default here)\naks_config = AksWebservice.deploy_configuration()\n\n#unique service name\nservice_name ='ps-aks-service'\n\n# Webservice creation using single command, there is a variant to use image directly as well.\naks_service = Webservice.deploy_from_image(\n  workspace=ws, \n  name=service_name,\n  deployment_config = aks_config,\n  image = myimage,\n  deployment_target = aks_target\n    )\n\naks_service.wait_for_deployment(show_output=True)"],"metadata":{},"outputs":[],"execution_count":8},{"cell_type":"code","source":["#for using the Web HTTP API \nprint(aks_service.scoring_uri)\nprint(aks_service.get_keys())"],"metadata":{},"outputs":[],"execution_count":9},{"cell_type":"code","source":["import json\n\n#get the some sample data\ntest_data_path = \"AdultCensusIncomeTest\"\ntest = spark.read.parquet(test_data_path).limit(5)\n\ntest_json = json.dumps(test.toJSON().collect())\n\nprint(test_json)"],"metadata":{},"outputs":[],"execution_count":10},{"cell_type":"code","source":["#using data defined above predict if income is >50K (1) or <=50K (0)\naks_service.run(input_data=test_json)"],"metadata":{},"outputs":[],"execution_count":11},{"cell_type":"code","source":["#comment to not delete the web service\naks_service.delete()\n#image.delete()\n#model.delete()\n#aks_target.delete()"],"metadata":{},"outputs":[],"execution_count":12},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":13}],"metadata":{"name":"04.DeploytoACI","notebookId":3874566296719318},"nbformat":4,"nbformat_minor":0}
--- a/databricks/Databricks_AMLSDK_github.dbc
+++ b/databricks/Databricks_AMLSDK_github.dbc
--- a/databricks/readme.md
+++ b/databricks/readme.md
@@ -1,29 +0,0 @@
 # Azure Databricks - Azure Machine Learning SDK Sample Notebooks
 **NOTE**: With the latest version of Azure Machine Learning SDK, there are some API changes due to which previous version of notebooks will not work.
 Please remove the previous SDK version and install the latest SDK by installing **azureml-sdk[databricks]** as a PyPi library in Azure Databricks workspace. 
 **NOTE**: Please create your Azure Databricks cluster as v4.x (high concurrency preferred) with **Python 3** (dropdown).
 **NOTE**: Some packages like psutil upgrade libs that can cause a conflict, please install such packages by freezing lib version. Eg. "pstuil **cryptography==1.5 pyopenssl==16.0.0 ipython=2.2.0**" to avoid install error. This issue is related to Databricks and not related to AML SDK.
 **NOTE**: You should at least have contributor access to your Azure subcription to run some of the notebooks.
 The iPython Notebooks have to be run sequentially after making changes based on your subscription. The corresponding DBC archive contains all the notebooks and can be imported into your Databricks workspace. You can the run notebooks after importing .dbc instead of downloading individually.  
 This set of notebooks are related to Income prediction experiment based on this [dataset](https://archive.ics.uci.edu/ml/datasets/adult) and demonstrate how to data prep, train and operationalize a Spark ML model with Azure ML Python SDK from within Azure Databricks. For details on SDK concepts, please refer to [notebooks](https://github.com/Azure/MachineLearningNotebooks)
 (Recommended) [Azure Databricks AML SDK notebooks](Databricks_AMLSDK_github.dbc) A single DBC package to import all notebooks in your Azure Databricks workspace.
 01. [Installation and Configuration](01.Installation_and_Configuration.ipynb): Install the Azure ML Python SDK and Initialize an Azure ML Workspace and save the Workspace configuration file.
 02. [Ingest data](02.Ingest_data.ipynb): Download the Adult Census Income dataset and split it into train and test sets.
 03. [Build model](03a.Build_model.ipynb): Train a binary classification model in Azure Databricks with a Spark ML Pipeline.
 04. [Build model with Run History](03b.Build_model_runHistory.ipynb): Train model and also capture run history (tracking) with Azure ML Python SDK.
 05. [Deploy to ACI](04.Deploy_to_ACI.ipynb): Deploy model to Azure Container Instance (ACI) with Azure ML Python SDK.
 06. [Deploy to AKS](04.Deploy_to_AKS_existingImage.ipynb): Deploy model to Azure Kubernetis Service (AKS) with Azure ML Python SDK from an existing Image with model, conda and score file.
 Copyright (c) Microsoft Corporation. All rights reserved.
 All notebooks in this folder are licensed under the MIT License. 
 Apache®, Apache Spark, and Spark® are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries.
--- a/dataprep/getting-started.ipynb
+++ b/dataprep/getting-started.ipynb
--- a/how-to-use-azureml/README.md
+++ b/how-to-use-azureml/README.md
@@ -0,0 +1,16 @@
 ## Examples to get started with Azure Machine Learning service
 Learn how to use Azure Machine Learning services for experimentation and model management.
 As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) notebook first to set up your Azure ML Workspace. Then, run the notebooks in following recommended order.
 * [train-within-notebook](train-within-notebook/train-within-notebook.ipynb): Train a model hile tracking run history, and learn how to deploy the model as web service to Azure Container Instance.
 * [train-on-local](train-on-local/train-on-local.ipynb): Learn how to submit a run and use Azure ML managed run configuration.
 * [train-on-aci](train-on-aci/train-on-aci.ipynb): Submit a remote run on serverless Docker-based compute.
 * [train-on-remote-vm](train-on-remote-vm/train-on-remote-vm.ipynb): Use Data Science Virtual Machine as a target for remote runs.
 * [logging-api](logging-api/logging-api.ipynb): Learn about the details of logging metrics to run history.
 * [register-model-create-image-deploy-service](register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb): Learn about the details of model management.
 * [production-deploy-to-aks](production-deploy-to-aks/production-deploy-to-aks.ipynb) Deploy a model to production at scale on Azure Kubernetes Service.
 * [enable-data-collection-for-models-in-aks](enable-data-collection-for-models-in-aks/enable-data-collection-for-models-in-aks.ipynb) Learn about data collection APIs for deployed model.
 * [enable-app-insights-in-production-service](enable-app-insights-in-production-serviceenable-app-insights-in-production-service.ipynb) Learn how to use App Insights with production web service.
--- a/how-to-use-azureml/automated-machine-learning/01.auto-ml-classification.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/01.auto-ml-classification.ipynb
@@ -139,7 +139,7 @@
    "|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",
+    "|**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>average_precision_score_weighted</i><br><i>norm_macro_recall</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",
--- a/how-to-use-azureml/automated-machine-learning/02.auto-ml-regression.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/02.auto-ml-regression.ipynb
--- a/how-to-use-azureml/automated-machine-learning/03.auto-ml-remote-execution.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/03.auto-ml-remote-execution.ipynb
@@ -53,6 +53,7 @@
    "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",
@@ -136,7 +137,28 @@
    "    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)"
+    "    dsvm_compute.wait_for_completion(show_output = True)\n",
    "    print(\"Waiting one minute for ssh to be accessible\")\n",
    "    time.sleep(60) # Wait for ssh to be accessible"
   ]
  },
  {
   "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",
    "\n",
    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
    "conda_run_config.environment.python.conda_dependencies = cd"
   ]
  },
  {
@@ -191,7 +213,7 @@
    "\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",
+    "|**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>average_precision_score_weighted</i><br><i>norm_macro_recall</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",
@@ -217,7 +239,7 @@
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             path = project_folder, \n",
-    "                             compute_target = dsvm_compute,\n",
+    "                             run_configuration=conda_run_config,\n",
    "                             data_script = project_folder + \"/get_data.py\",\n",
    "                             **automl_settings\n",
    "                            )\n"
--- a/how-to-use-azureml/automated-machine-learning/03b.auto-ml-remote-batchai.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/03b.auto-ml-remote-batchai.ipynb
@@ -134,7 +134,7 @@
    "from azureml.core.compute import ComputeTarget\n",
    "\n",
    "# Choose a name for your cluster.\n",
-    "batchai_cluster_name = \"cpucluster\"\n",
+    "batchai_cluster_name = \"automlcl\"\n",
    "\n",
    "found = False\n",
    "# Check if this compute target already exists in the workspace.\n",
@@ -160,6 +160,27 @@
    "     # For a more detailed view of current Batch AI cluster status, use the 'status' property."
   ]
  },
  {
   "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 Batch AI cluster\n",
    "conda_run_config.target = compute_target\n",
    "conda_run_config.environment.docker.enabled = True\n",
    "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\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": {},
@@ -212,7 +233,7 @@
    "\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",
+    "|**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>average_precision_score_weighted</i><br><i>norm_macro_recall</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",
@@ -238,7 +259,7 @@
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             path = project_folder,\n",
-    "                             compute_target = compute_target,\n",
+    "                             run_configuration=conda_run_config,\n",
    "                             data_script = project_folder + \"/get_data.py\",\n",
    "                             **automl_settings\n",
    "                            )\n"
--- a/how-to-use-azureml/automated-machine-learning/04.auto-ml-remote-execution-text-data-blob-store.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/04.auto-ml-remote-execution-text-data-blob-store.ipynb
@@ -131,7 +131,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "from azureml.core.compute import RemoteCompute\n",
+    "from azureml.core.compute import ComputeTarget, RemoteCompute\n",
    "import time\n",
    "\n",
    "# Add your VM information below\n",
@@ -147,7 +147,8 @@
    "    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",
+    "    attach_config = RemoteCompute.attach_configuration(address=dsvm_ip_addr, username=dsvm_username, password=dsvm_password, ssh_port=dsvm_ssh_port)\n",
    "    ComputeTarget.attach(workspace=ws, name=compute_name, attach_configuration=attach_config)\n",
    "\n",
    "    while ws.compute_targets[compute_name].provisioning_state == 'Creating':\n",
    "        time.sleep(1)\n",
@@ -157,7 +158,26 @@
    "    if dsvm_compute.provisioning_state == 'Failed':\n",
    "        print('Attached failed.')\n",
    "        print(dsvm_compute.provisioning_errors)\n",
-    "        dsvm_compute.delete()"
+    "        dsvm_compute.detach()"
   ]
  },
  {
   "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",
    "\n",
    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
    "conda_run_config.environment.python.conda_dependencies = cd"
   ]
  },
  {
@@ -242,12 +262,13 @@
    "\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",
+    "|**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>average_precision_score_weighted</i><br><i>norm_macro_recall</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",
    "|**enable_cache**|Setting this to *True* enables preprocess done once and reuse the same preprocessed data for all the iterations. Default value is True.\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.|"
   ]
  },
@@ -268,7 +289,7 @@
    "\n",
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             path = project_folder,\n",
-    "                             compute_target = dsvm_compute,\n",
+    "                             run_configuration=conda_run_config,\n",
    "                             data_script = project_folder + \"/get_data.py\",\n",
    "                             **automl_settings\n",
    "                            )\n"
@@ -326,6 +347,23 @@
    "remote_run.wait_for_completion(show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-process cache cleanup\n",
    "The preprocess data gets cache at user default file store. When the run is completed the cache can be cleaned by running below cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run.clean_preprocessor_cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
@@ -367,7 +405,7 @@
   "outputs": [],
   "source": [
    "# Cancel the ongoing experiment and stop scheduling new iterations.\n",
-    "remote_run.cancel()\n",
+    "# remote_run.cancel()\n",
    "\n",
    "# Cancel iteration 1 and move onto iteration 2.\n",
    "# remote_run.cancel_iteration(1)"
--- a/how-to-use-azureml/automated-machine-learning/05.auto-ml-missing-data-Blacklist-Early-Termination.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/05.auto-ml-missing-data-Blacklist-Early-Termination.ipynb
@@ -160,7 +160,7 @@
    "|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",
+    "|**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>average_precision_score_weighted</i><br><i>norm_macro_recall</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",
--- a/how-to-use-azureml/automated-machine-learning/06.auto-ml-sparse-data-train-test-split.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/06.auto-ml-sparse-data-train-test-split.ipynb
@@ -162,7 +162,7 @@
    "|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",
+    "|**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>average_precision_score_weighted</i><br><i>norm_macro_recall</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",
--- a/how-to-use-azureml/automated-machine-learning/07.auto-ml-exploring-previous-runs.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/07.auto-ml-exploring-previous-runs.ipynb
--- a/how-to-use-azureml/automated-machine-learning/08.auto-ml-remote-execution-with-DataStore.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/08.auto-ml-remote-execution-with-DataStore.ipynb
@@ -135,7 +135,9 @@
    "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)"
+    "    dsvm_compute.wait_for_completion(show_output=True)\n",
    "    print(\"Waiting one minute for ssh to be accessible\")\n",
    "    time.sleep(60) # Wait for ssh to be accessible"
   ]
  },
  {
@@ -312,12 +314,13 @@
    "\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",
+    "|**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>average_precision_score_weighted</i><br><i>norm_macro_recall</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",
    "|**enable_cache**|Setting this to *True* enables preprocess done once and reuse the same preprocessed data for all the iterations. Default value is True.|\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|"
   ]
  },
@@ -445,6 +448,23 @@
    "# remote_run.cancel_iteration(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-process cache cleanup\n",
    "The preprocess data gets cache at user default file store. When the run is completed the cache can be cleaned by running below cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run.clean_preprocessor_cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
--- a/how-to-use-azureml/automated-machine-learning/08b.auto-ml-remote-execution-with-dataprep.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/08b.auto-ml-remote-execution-with-dataprep.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 08b: Remote Execution with DataPrep\n",
    "\n",
    "This sample accesses a data file on a remote DSVM through Datastore using DataPrep. Advantages of using DataPrep are:\n",
    "1. DataPrep supports reading from and writing to datastores.\n",
    "2. DataPrep supports automatic file type and column type detection.\n",
    "3. DataPrep makes passing data into AutoML really simple.\n",
    "\n",
    "More DataPrep documentation and examples can be found [here](https://github.com/Microsoft/AMLDataPrepDocs).\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. Doing some basic data preparation using DataPrep and passing the prepared data (DataFlow) to AutoML for training (classficiation).\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 = 'automl-dataprep'\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",
    "We will download a 1MB simple random sample of the Chicago Crime data into a local temporary directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tempfile\n",
    "import requests\n",
    "\n",
    "temp_folder = tempfile.mkdtemp()\n",
    "temp_tsv = os.path.join(temp_folder, 'crime0.csv')\n",
    "\n",
    "request = requests.get('https://dprepdata.blob.core.windows.net/demo/crime0-random.csv')\n",
    "with open(temp_tsv, 'w', encoding='utf-8') as f:\n",
    "    f.write(request.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upload data to the cloud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's make the data available in your datastore. Datastore is a convenient construct associated with your workspace for you to  reference different types of cloud storage locations (e.g. Azure Blob Containers, Azure File Shares, Azure Data Lake Stores, etc.). The benefit Datastore brings is you only need to register datastores once and you will be able to access them by name and will not need to expose secrets in your code. When you first create a workspace, a default datastore is registered for you which references the Azure Blob Container that was provisioned with the workspace. Let's upload the data we just got from the public location to the default datastore.\n",
    "\n",
    "The `csv` file is uploaded into a directory named `datasets` at the root of the datastore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Workspace, Datastore\n",
    "\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(src_dir=temp_folder, target_path='datasets', overwrite=True, show_progress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Dataflow using DataPrep\n",
    "Let's use DataPrep to read the `csv` file from the datastore we just uploaded to and get the data profile to make sure our data looks good. We will predict the type of the offense (`Primary Type`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.dataprep as dprep\n",
    "\n",
    "dflow = dprep.read_csv(path=ds.path('datasets/crime0.csv'))\n",
    "dflow.get_profile()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also take a look at the first 5 rows of the data to give ourselves an idea of what the data looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dflow.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the first 5 rows, we see that there are some rows that have no value in the label column (`Primary Type`). Let's remove those rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dflow = dflow.drop_nulls('Primary Type')\n",
    "dflow.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've removed those rows, let's split the dataflow into a features dataflow and a label dataflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dflow.drop_columns(columns=['Primary Type', 'FBI Code'])\n",
    "y = dflow.keep_columns(columns=['Primary Type'])"
   ]
  },
  {
   "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. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</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",
    "|**enable_cache**|Setting this to *True* enables preprocess done once and reuse the same preprocessed data for all the iterations. Default value is True.|\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": [
    "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]==0.1.0.1918169'], conda_packages=['numpy'], pin_sdk_version=False, pip_indexurl='https://azuremlsdktestpypi.azureedge.net/sdk-release/master/588E708E0DF342C4A80BD954289657CF')\n",
    "conda_run_config.environment.python.conda_dependencies = cd"
   ]
  },
  {
   "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\": 'accuracy',\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",
    "                             X=X,\n",
    "                             y=y,\n",
    "                             **automl_settings)"
   ]
  },
  {
   "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run"
   ]
  },
  {
   "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": [
    "## Pre-process cache cleanup\n",
    "The preprocess data gets cache at user default file store. When the run is completed the cache can be cleaned by running below cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run.clean_preprocessor_cache()"
   ]
  },
  {
   "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": [
    "dflow = dprep.read_csv(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n",
    "dflow.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas_ml import ConfusionMatrix\n",
    "\n",
    "y_test = dflow.keep_columns(columns=['Primary Type']).to_pandas_dataframe()\n",
    "X_test = dflow.drop_columns(columns=['Primary Type', 'FBI Code']).to_pandas_dataframe()\n",
    "\n",
    "ypred = fitted_model.predict(X_test.values)\n",
    "\n",
    "cm = ConfusionMatrix(y_test['Primary Type'], ypred)\n",
    "\n",
    "print(cm)\n",
    "\n",
    "cm.plot()"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "savitam"
   }
  ],
  "kernelspec": {
   "display_name": "Python [conda env:cli_dev]",
   "language": "python",
   "name": "conda-env-cli_dev-py"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/09.auto-ml-classification-with-deployment.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/09.auto-ml-classification-with-deployment.ipynb
@@ -120,7 +120,7 @@
    "|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",
+    "|**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>average_precision_score_weighted</i><br><i>norm_macro_recall</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",
--- a/how-to-use-azureml/automated-machine-learning/10.auto-ml-multi-output-example.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/10.auto-ml-multi-output-example.ipynb
--- a/how-to-use-azureml/automated-machine-learning/11.auto-ml-sample-weight.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/11.auto-ml-sample-weight.ipynb
--- a/how-to-use-azureml/automated-machine-learning/12.auto-ml-retrieve-the-training-sdk-versions.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/12.auto-ml-retrieve-the-training-sdk-versions.ipynb
--- a/how-to-use-azureml/automated-machine-learning/13a.auto-ml-dataprep.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/13a.auto-ml-dataprep.ipynb
--- a/how-to-use-azureml/automated-machine-learning/13b.auto-ml-dataprep-remote-execution.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/13b.auto-ml-dataprep-remote-execution.ipynb
@@ -186,7 +186,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "dsvm_name = 'mydsvmd'\n",
+    "dsvm_name = 'mydsvmc'\n",
    "\n",
    "try:\n",
    "    while ws.compute_targets[dsvm_name].provisioning_state == 'Creating':\n",
@@ -198,7 +198,9 @@
    "    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)"
+    "    dsvm_compute.wait_for_completion(show_output = True)\n",
    "    print(\"Waiting one minute for ssh to be accessible\")\n",
    "    time.sleep(60) # Wait for ssh to be accessible"
   ]
  },
  {
--- a/how-to-use-azureml/automated-machine-learning/14.auto-ml-model-explanation.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/14.auto-ml-model-explanation.ipynb
@@ -27,22 +27,6 @@
    "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": {},
@@ -159,7 +143,7 @@
    "|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",
+    "|**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>average_precision_score_weighted</i><br><i>norm_macro_recall</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",
@@ -179,7 +163,7 @@
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             primary_metric = 'AUC_weighted',\n",
-    "                             max_time_sec = 12000,\n",
+    "                             iteration_timeout_minutes = 200,\n",
    "                             iterations = 10,\n",
    "                             verbosity = logging.INFO,\n",
    "                             X = X_train, \n",
@@ -237,16 +221,6 @@
    "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": {},
--- a/how-to-use-azureml/automated-machine-learning/17.auto-ml-classification_with_tensorflow.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/17.auto-ml-classification_with_tensorflow.ipynb
@@ -0,0 +1,390 @@
 {
 "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 17: Classification with Local Compute with Tesnorflow DNNClassifier and LinearClassifier using whitelist models feature.\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",
    "This notebooks shows how can automl can be trained on a a selected list of models,see the readme.md for the models.\n",
    "This trains the model exclusively on tensorflow based models.\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 on a whilelisted models 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 = 10,\n",
    "                             n_cross_validations = 3,\n",
    "                             verbosity = logging.INFO,\n",
    "                             X = X_train, \n",
    "                             y = y_train,\n",
    "                             enable_tf=True,\n",
    "                             whitelist_models=[\"TensorFlowLinearClassifier\", \"TensorFlowDNN\"],\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\n"
   ]
  },
  {
   "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
 }
--- a/how-to-use-azureml/automated-machine-learning/18a.auto-ml-forecasting.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/18a.auto-ml-forecasting.ipynb
@@ -0,0 +1,397 @@
 {
 "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 18: Energy Demand Forecasting\n",
    "\n",
    "In this example, we show how AutoML can be used for energy demand forecasting.\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 with new task type \"forecasting\" for timeseries data training, and other timeseries related settings: for this dataset we use the basic one: \"time_column_name\" \n",
    "3. Training the Model using local compute\n",
    "4. Exploring the results\n",
    "5. Testing the fitted model"
   ]
  },
  {
   "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 azureml.core\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import logging\n",
    "import warnings\n",
    "warnings.showwarning = lambda *args, **kwargs: None\n",
    "\n",
    "\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
   ]
  },
  {
   "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-energydemandforecasting'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-local-energydemandforecasting'\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['Run History Name'] = experiment_name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Data\n",
    "Read energy demanding data from file, and preview data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"nyc_energy.csv\", parse_dates=['timeStamp'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data to train and test\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = data[data['timeStamp'] < '2017-02-01']\n",
    "test = data[data['timeStamp'] >= '2017-02-01']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare the test data, we will feed X_test to the fitted model and get prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = test.pop('demand').values\n",
    "X_test = test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the train data to train and valid\n",
    "\n",
    "Use one month's data as valid data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train[train['timeStamp'] < '2017-01-01']\n",
    "X_valid = train[train['timeStamp'] >= '2017-01-01']\n",
    "y_train = X_train.pop('demand').values\n",
    "y_valid = X_valid.pop('demand').values\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_valid.shape)\n",
    "print(y_valid.shape)"
   ]
  },
  {
   "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**|forecasting|\n",
    "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting 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",
    "|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\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**|Data used to evaluate a model in a iteration. (sparse) array-like, shape = [n_samples, n_features]|\n",
    "|**y_valid**|Data used to evaluate a model in a iteration. (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": [
    "time_column_name = 'timeStamp'\n",
    "automl_settings = {\n",
    "    \"time_column_name\": time_column_name,\n",
    "}\n",
    "\n",
    "\n",
    "automl_config = AutoMLConfig(task = 'forecasting',\n",
    "                             debug_log = 'automl_nyc_energy_errors.log',\n",
    "                             primary_metric='normalized_root_mean_squared_error',\n",
    "                             iterations = 10,\n",
    "                             iteration_timeout_minutes = 5,\n",
    "                             X = X_train,\n",
    "                             y = y_train,\n",
    "                             X_valid = X_valid,\n",
    "                             y_valid = y_valid,\n",
    "                             path=project_folder,\n",
    "                             verbosity = logging.INFO,\n",
    "                            **automl_settings)"
   ]
  },
  {
   "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": [
    "### Retrieve the Best Model\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",
    "fitted_model.steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Best Fitted Model\n",
    "\n",
    "Predict on training and test set, and calculate residual values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = fitted_model.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define a Check Data Function\n",
    "\n",
    "Remove the nan values from y_test to avoid error when calculate metrics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _check_calc_input(y_true, y_pred, rm_na=True):\n",
    "    \"\"\"\n",
    "    Check that 'y_true' and 'y_pred' are non-empty and\n",
    "    have equal length.\n",
    "\n",
    "    :param y_true: Vector of actual values\n",
    "    :type y_true: array-like\n",
    "\n",
    "    :param y_pred: Vector of predicted values\n",
    "    :type y_pred: array-like\n",
    "\n",
    "    :param rm_na:\n",
    "        If rm_na=True, remove entries where y_true=NA and y_pred=NA.\n",
    "    :type rm_na: boolean\n",
    "\n",
    "    :return:\n",
    "        Tuple (y_true, y_pred). if rm_na=True,\n",
    "        the returned vectors may differ from their input values.\n",
    "    :rtype: Tuple with 2 entries\n",
    "    \"\"\"\n",
    "    if len(y_true) != len(y_pred):\n",
    "        raise ValueError(\n",
    "            'the true values and prediction values do not have equal length.')\n",
    "    elif len(y_true) == 0:\n",
    "        raise ValueError(\n",
    "            'y_true and y_pred are empty.')\n",
    "    # if there is any non-numeric element in the y_true or y_pred,\n",
    "    # the ValueError exception will be thrown.\n",
    "    y_true = np.array(y_true).astype(float)\n",
    "    y_pred = np.array(y_pred).astype(float)\n",
    "    if rm_na:\n",
    "        # remove entries both in y_true and y_pred where at least\n",
    "        # one element in y_true or y_pred is missing\n",
    "        y_true_rm_na = y_true[~(np.isnan(y_true) | np.isnan(y_pred))]\n",
    "        y_pred_rm_na = y_pred[~(np.isnan(y_true) | np.isnan(y_pred))]\n",
    "        return (y_true_rm_na, y_pred_rm_na)\n",
    "    else:\n",
    "        return y_true, y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use the Check Data Function to remove the nan values from y_test to avoid error when calculate metrics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test,y_pred =  _check_calc_input(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate metrics for the prediction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
    "print('R2 score: %.2f' % r2_score(y_test, y_pred))\n",
    "\n",
    "\n",
    "\n",
    "# Plot outputs\n",
    "%matplotlib notebook\n",
    "test_pred = plt.scatter(y_test, y_pred, color='b')\n",
    "test_test = plt.scatter(y_test, y_test, color='g')\n",
    "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "xiaga"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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 }
--- a/how-to-use-azureml/automated-machine-learning/18b.auto-ml-forecasting.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/18b.auto-ml-forecasting.ipynb
@@ -0,0 +1,390 @@
 {
 "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 18B: Orange Juice Sales Forecasting\n",
    "\n",
    "In this example, we use AutoML to find and tune a time-series forecasting model.\n",
    "\n",
    "Make sure you have executed the [configuration notebook](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook, you will:\n",
    "1. Create an Experiment in an existing Workspace\n",
    "2. Instantiate an AutoMLConfig \n",
    "3. Find and train a forecasting model using local compute\n",
    "4. Evaluate the performance of the model\n",
    "\n",
    "## Sample Data\n",
    "The examples in the follow code samples use the [University of Chicago's Dominick's Finer Foods dataset](https://research.chicagobooth.edu/kilts/marketing-databases/dominicks) to forecast orange juice sales. Dominick's was a grocery chain in the Chicago metropolitan area."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Experiment\n",
    "\n",
    "As part of the setup you have already created a <b>Workspace</b>. To run AutoML, you also need to create an <b>Experiment</b>. An Experiment is a named object in a Workspace which represents a predictive task, the output of which is a trained model and a set of evaluation metrics for the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.core\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import logging\n",
    "\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error"
   ]
  },
  {
   "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-ojsalesforecasting'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-local-ojsalesforecasting'\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['Run History Name'] = experiment_name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Data\n",
    "You are now ready to load the historical orange juice sales data. We will load the CSV file into a plain pandas DataFrame; the time column in the CSV is called _WeekStarting_, so it will be specially parsed into the datetime type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_column_name = 'WeekStarting'\n",
    "data = pd.read_csv(\"dominicks_OJ.csv\", parse_dates=[time_column_name])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each row in the DataFrame holds a quantity of weekly sales for an OJ brand at a single store. The data also includes the sales price, a flag indicating if the OJ brand was advertised in the store that week, and some customer demographic information based on the store location. For historical reasons, the data also include the logarithm of the sales quantity. The Dominick's grocery data is commonly used to illustrate econometric modeling techniques where logarithms of quantities are generally preferred.    \n",
    "\n",
    "The task is now to build a time-series model for the _Quantity_ column. It is important to note that this dataset is comprised of many individual time-series - one for each unique combination of _Store_ and _Brand_. To distinguish the individual time-series, we thus define the **grain** - the columns whose values determine the boundaries between time-series: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grain_column_names = ['Store', 'Brand']\n",
    "nseries = data.groupby(grain_column_names).ngroups\n",
    "print('Data contains {0} individual time-series.'.format(nseries))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Splitting\n",
    "For the purposes of demonstration and later forecast evaluation, we now split the data into a training and a testing set. The test set will contain the final 20 weeks of observed sales for each time-series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ntest_periods = 20\n",
    "\n",
    "def split_last_n_by_grain(df, n):\n",
    "    \"\"\"\n",
    "    Group df by grain and split on last n rows for each group\n",
    "    \"\"\"\n",
    "    df_grouped = (df.sort_values(time_column_name) # Sort by ascending time\n",
    "                  .groupby(grain_column_names, group_keys=False))\n",
    "    df_head = df_grouped.apply(lambda dfg: dfg.iloc[:-n])\n",
    "    df_tail = df_grouped.apply(lambda dfg: dfg.iloc[-n:])\n",
    "    return df_head, df_tail\n",
    "\n",
    "X_train, X_test = split_last_n_by_grain(data, ntest_periods)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "\n",
    "For forecasting tasks, AutoML uses pre-processing and estimation steps that are specific to time-series. AutoML will undertake the following pre-processing steps:\n",
    "* Detect time-series sample frequency (e.g. hourly, daily, weekly) and create new records for absent time points to make the series regular. A regular time series has a well-defined frequency and has a value at every sample point in a contiguous time span \n",
    "* Impute missing values in the target (via forward-fill) and feature columns (using median column values) \n",
    "* Create grain-based features to enable fixed effects across different series\n",
    "* Create time-based features to assist in learning seasonal patterns\n",
    "* Encode categorical variables to numeric quantities\n",
    "\n",
    "AutoML will currently train a single, regression-type model across **all** time-series in a given training set. This allows the model to generalize across related series.\n",
    "\n",
    "You are almost ready to start an AutoML training job. We will first need to create a validation set from the existing training set (i.e. for hyper-parameter tuning): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nvalidation_periods = 20\n",
    "X_train, X_validate = split_last_n_by_grain(X_train, nvalidation_periods)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to separate the target column from the rest of the DataFrame: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_column_name = 'Quantity'\n",
    "y_train = X_train.pop(target_column_name).values\n",
    "y_validate = X_validate.pop(target_column_name).values "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an AutoMLConfig\n",
    "\n",
    "The AutoMLConfig object defines the settings and data for an AutoML training job. Here, we set necessary inputs like the task type, the number of AutoML iterations to try, and the training and validation data. \n",
    "\n",
    "For forecasting tasks, there are some additional parameters that can be set: the name of the input data column, holding the date/time and the grain column names. A time column is required for forecasting, while the grain is optional. If a grain is not given, the forecaster assumes that the whole dataset is a single time-series. We also pass a list of columns to drop prior to modeling. The _logQuantity_ column is completely correlated with the target quantity, so it must be removed to prevent a target leak. \n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**task**|forecasting|\n",
    "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting 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",
    "|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
    "|**X**|Training matrix of features, shape = [n_training_samples, n_features]|\n",
    "|**y**|Target values, shape = [n_training_samples, ]|\n",
    "|**X_valid**|Validation matrix of features, shape = [n_validation_samples, n_features]|\n",
    "|**y_valid**|Target values for validation, shape = [n_validation_samples, ]\n",
    "|**enable_ensembling**|Allow AutoML to create ensembles of the best performing models\n",
    "|**debug_log**|Log file path for writing debugging information\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_settings = {\n",
    "    'time_column_name': time_column_name,\n",
    "    'grain_column_names': grain_column_names,\n",
    "    'drop_column_names': ['logQuantity']\n",
    "}\n",
    "\n",
    "automl_config = AutoMLConfig(task='forecasting',\n",
    "                             debug_log='automl_oj_sales_errors.log',\n",
    "                             primary_metric='normalized_root_mean_squared_error',\n",
    "                             iterations=10,\n",
    "                             X=X_train,\n",
    "                             y=y_train,\n",
    "                             X_valid=X_validate,\n",
    "                             y_valid=y_validate,\n",
    "                             enable_ensembling=False,\n",
    "                             path=project_folder,\n",
    "                             verbosity=logging.INFO,\n",
    "                            **automl_settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model\n",
    "\n",
    "You can now submit a new training run. For local runs, the execution is synchronous. Depending on the data and number of iterations this operation may take several minutes.\n",
    "Information from each iteration will be printed 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",
    "Each run within an Experiment stores serialized (i.e. pickled) pipelines from the AutoML iterations. We can now retrieve the pipeline with the best performance on the validation dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_pipeline = local_run.get_output()\n",
    "fitted_pipeline.steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make Predictions from the Best Fitted Model\n",
    "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. First, we remove the target values from the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = X_test.pop(target_column_name).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To produce predictions on the test set, we need to know the feature values at all dates in the test set. This requirement is somewhat reasonable for the OJ sales data since the features mainly consist of price, which is usually set in advance, and customer demographics which are approximately constant for each store over the 20 week forecast horizon in the testing data. \n",
    "\n",
    "The target predictions can be retrieved by calling the `predict` method on the best model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = fitted_pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate evaluation metrics for the prediction\n",
    "To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MAPE(actual, pred):\n",
    "    \"\"\"\n",
    "    Calculate mean absolute percentage error.\n",
    "    Remove NA and values where actual is close to zero\n",
    "    \"\"\"\n",
    "    not_na = ~(np.isnan(actual) | np.isnan(pred))\n",
    "    not_zero = ~np.isclose(actual, 0.0)\n",
    "    actual_safe = actual[not_na & not_zero]\n",
    "    pred_safe = pred[not_na & not_zero]\n",
    "    APE = 100*np.abs((actual_safe - pred_safe)/actual_safe)\n",
    "    return np.mean(APE)\n",
    "\n",
    "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
    "print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
    "print('MAPE: %.2f' % MAPE(y_test, y_pred))"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "erwright"
   }
  ],
  "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
 }
--- a/how-to-use-azureml/automated-machine-learning/README.md
+++ b/how-to-use-azureml/automated-machine-learning/README.md
@@ -1,6 +1,7 @@
 # Table of Contents
 1. [Automated ML Introduction](#introduction)
 1. [Running samples in Azure Notebooks](#jupyter)
 1. [Running samples in Azure Databricks](#databricks)
 1. [Running samples in a Local Conda environment](#localconda)
 1. [Automated ML SDK Sample Notebooks](#samples)
 1. [Documentation](#documentation)
@@ -16,17 +17,25 @@ If you are new to Data Science, AutoML will help you get jumpstarted by simplify
 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.
 Below are the three execution environments supported by AutoML.
 <a name="jupyter"></a>
 ## Running samples in Azure Notebooks - Jupyter based notebooks in the Azure cloud
 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. Follow the instructions in the [configuration](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. 
+ <a name="databricks"></a>
 ## Running samples in Azure Databricks
-    ![set kernel to Python 3.6](../images/python36.png)
+**NOTE**: Please create your Azure Databricks cluster as v4.x (high concurrency preferred) with **Python 3** (dropdown).
 **NOTE**: You should at least have contributor access to your Azure subcription to run the notebook.
 - Please remove the previous SDK version if there is any and install the latest SDK by installing **azureml-sdk[automl_databricks]** as a PyPi library in Azure Databricks workspace.
 - Download the sample notebook 16a.auto-ml-classification-local-azuredatabricks from [GitHub](https://github.com/Azure/MachineLearningNotebooks) and import into the Azure databricks workspace.
 - Attach the notebook to the cluster.
 <a name="localconda"></a>
 ## Running samples in a Local Conda environment
@@ -57,9 +66,9 @@ 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 10 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.  See the specific sections below for Windows, Mac and Linux.  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: 
+Start an **Anaconda Prompt** window, cd to the **automl** folder where the sample notebooks were extracted and then run:
 ```
 automl_setup
 ```
@@ -80,7 +89,7 @@ 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
+- Before running any samples you next need to run the configuration notebook. Click on configuration.ipynb notebook
 - Execute the cells in the notebook to Register Machine Learning Services Resource Provider and create a workspace. (*instructions in notebook*)
 ### 5. Running Samples
@@ -89,22 +98,21 @@ bash automl_setup_linux.sh
 <a name="samples"></a>
 # Automated ML SDK Sample Notebooks
- [00.configuration.ipynb](00.configuration.ipynb)
+- [configuration.ipynb](configuration.ipynb)
    - Register Machine Learning Services Resource Provider
    - Create new Azure ML Workspace
    - Save Workspace configuration file
- [01.auto-ml-classification.ipynb](01.auto-ml-classification.ipynb)
+- [auto-ml-classification.ipynb](classification/auto-ml-classification.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
    - Simple example of using Auto ML for classification
    - Uses local compute for training
- [02.auto-ml-regression.ipynb](02.auto-ml-regression.ipynb)
+- [auto-ml-regression.ipynb](regression/auto-ml-regression.ipynb)
    - Dataset: scikit learn's [diabetes dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html)
    - Simple example of using Auto ML for regression
    - Uses local compute for training
- [03.auto-ml-remote-execution.ipynb](03.auto-ml-remote-execution.ipynb)
+- [auto-ml-remote-execution.ipynb](remote-execution/auto-ml-remote-execution.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 linux DSVM for training
    - Parallel execution of iterations
@@ -113,7 +121,7 @@ bash automl_setup_linux.sh
    - Retrieving models for any iteration or logged metric
    - Specify automl settings as kwargs
- [03b.auto-ml-remote-batchai.ipynb](03b.auto-ml-remote-batchai.ipynb)
+- [auto-ml-remote-batchai.ipynb](remote-batchai/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 automated ML for classification using a remote Batch AI compute for training
    - Parallel execution of iterations
@@ -122,69 +130,62 @@ bash automl_setup_linux.sh
    - Retrieving models for any iteration or logged metric
    - Specify automl settings as kwargs
- [04.auto-ml-remote-execution-text-data-blob-store.ipynb](04.auto-ml-remote-execution-text-data-blob-store.ipynb)
+- [auto-ml-remote-attach.ipynb](remote-attach/auto-ml-remote-attach.ipynb)
    - Dataset: [Burning Man 2016 dataset](https://innovate.burningman.org/datasets-page/)
    - handling text data with preprocess flag
    - Reading data from a blob store for remote executions
    - using pandas dataframes for reading data
- [05.auto-ml-missing-data-blacklist-early-termination.ipynb](05.auto-ml-missing-data-blacklist-early-termination.ipynb)
+- [auto-ml-missing-data-blacklist-early-termination.ipynb](missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
    - Blacklist certain pipelines
    - Specify a target metrics to indicate stopping criteria
    - Handling Missing Data in the input
- [06.auto-ml-sparse-data-train-test-split.ipynb](06.auto-ml-sparse-data-train-test-split.ipynb)
+- [auto-ml-sparse-data-train-test-split.ipynb](sparse-data-train-test-split/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
- [07.auto-ml-exploring-previous-runs.ipynb](07.auto-ml-exploring-previous-runs)
+- [auto-ml-exploring-previous-runs.ipynb](exploring-previous-runs/auto-ml-exploring-previous-runs.ipynb)
    - 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)
    - Download fitted pipeline for any iteration
- [08.auto-ml-remote-execution-with-DataStore.ipynb](08.auto-ml-remote-execution-with-DataStore.ipynb)
+- [auto-ml-remote-execution-with-datastore.ipynb](remote-execution-with-datastore/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 DataStore.
- [09.auto-ml-classification-with-deployment.ipynb](09.auto-ml-classification-with-deployment.ipynb)
+- [auto-ml-classification-with-deployment.ipynb](classification-with-deployment/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)
    - Simple example of using Auto ML for classification
    - Registering the model
    - Creating Image and creating aci service
    - Testing the aci service
- [10.auto-ml-multi-output-example.ipynb](10.auto-ml-multi-output-example.ipynb)
+- [auto-ml-sample-weight.ipynb](sample-weight/auto-ml-sample-weight.ipynb)
    - Dataset: scikit learn's random example using multi-output pipeline(http://scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py)
    - Simple example of using Auto ML for multi output regression
    - Handle both the dense and sparse metrix
 - [11.auto-ml-sample-weight.ipynb](11.auto-ml-sample-weight.ipynb)
    - How to specifying sample_weight
    - The difference that it makes to test results
- [12.auto-ml-retrieve-the-training-sdk-versions.ipynb](12.auto-ml-retrieve-the-training-sdk-versions.ipynb)
+- [auto-ml-dataprep.ipynb](dataprep/auto-ml-dataprep.ipynb)
    - How to get current and training env SDK versions
 - [13.auto-ml-dataprep.ipynb](13.auto-ml-dataprep.ipynb)
    - Using DataPrep for reading data
- [14.auto-ml-model-explanation.ipynb](14.auto-ml-model-explanation.ipynb)
+- [auto-ml-dataprep-remote-execution.ipynb](dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb)
-    - Dataset: sklearn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html)
+    - Using DataPrep for reading data with remote execution
    - Explaining the AutoML classification pipeline
    - Visualizing feature importance in widget
- [15a.auto-ml-classification-ensemble.ipynb](15a.auto-ml-classification-ensemble.ipynb)
+- [auto-ml-classification-local-azuredatabricks.ipynb](classification-local-azuredatabricks/auto-ml-classification-local-azuredatabricks.ipynb)
    - Dataset: scikit learn's [digit dataset](https://innovate.burningman.org/datasets-page/)
    - Example of using AutoML for classification using Azure Databricks as the platform for training
 - [auto-ml-classification_with_tensorflow.ipynb](classification_with_tensorflow/auto-ml-classification_with_tensorflow.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
+    - Simple example of using Auto ML for classification with whitelisting tensorflow models.checkout
    - Uses local compute for training
- [15b.auto-ml-regression-ensemble.ipynb](15b.auto-ml-regression-ensemble.ipynb)
+- [auto-ml-timeseries.ipynb](timeseries/auto-ml-timeseries.ipynb)
-    - Dataset: scikit learn's [diabetes dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html)
+    - Dataset: NYC energy demanding data
-    - Enables an extra iteration for generating an Ensemble of models
+    - Example of using AutoML for timeseries data training
    - Uses remote Linux DSVM for training
 <a name="documentation"></a>
 # Documentation
@@ -199,7 +200,7 @@ bash automl_setup_linux.sh
 |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
+|**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>average_precision_score_weighted</i><br><i>norm_macro_recall</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
 |**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|
@@ -209,7 +210,7 @@ bash automl_setup_linux.sh
 |**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|
 |**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|
+|**whitelist_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**
@@ -303,4 +304,3 @@ To resolve this issue, allocate a DSVM with more memory or reduce the value spec
 ## 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 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.
--- a/how-to-use-azureml/automated-machine-learning/automl_env.yml
+++ b/how-to-use-azureml/automated-machine-learning/automl_env.yml
@@ -8,9 +8,10 @@ dependencies:
 - numpy>=1.11.0,<1.15.0
 - cython
 - urllib3<1.24
- scipy>=0.19.0,<0.20.0
+- scipy>=1.0.0,<=1.1.0
 - scikit-learn>=0.18.0,<=0.19.1
 - pandas>=0.22.0,<0.23.0
 - tensorflow>=1.12.0
 # Required for azuremlftk
 - dill
@@ -23,9 +24,9 @@ dependencies:
 - pip:
  # Required for azuremlftk
-  - https://azuremlpackages.blob.core.windows.net/forecasting/azuremlftk-0.1.18313.5a1-py3-none-any.whl
+  - https://azuremlpackages.blob.core.windows.net/forecasting/azuremlftk-0.1.18323.5a1-py3-none-any.whl
  # Required packages for AzureML execution, history, and data preparation.
-  - azureml-sdk[automl,notebooks]
+  - azureml-sdk[automl,notebooks,explain]
  - pandas_ml
--- a/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
+++ b/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
@@ -8,9 +8,10 @@ dependencies:
 - numpy>=1.15.3
 - cython
 - urllib3<1.24
- scipy>=0.19.0,<0.20.0
+- scipy>=1.0.0,<=1.1.0
 - scikit-learn>=0.18.0,<=0.19.1
 - pandas>=0.22.0,<0.23.0
 - tensorflow>=1.12.0
 # Required for azuremlftk
 - dill
@@ -23,9 +24,10 @@ dependencies:
 - pip:
  # Required for azuremlftk
-  - https://azuremlpackages.blob.core.windows.net/forecasting/azuremlftk-0.1.18313.5a1-py3-none-any.whl
+  - https://azuremlpackages.blob.core.windows.net/forecasting/azuremlftk-0.1.18323.5a1-py3-none-any.whl
  # Required packages for AzureML execution, history, and data preparation.
-  - azureml-sdk[automl,notebooks]
+  - azureml-sdk[automl,notebooks,explain]
  - pandas_ml
--- a/how-to-use-azureml/automated-machine-learning/automl_setup.cmd
+++ b/how-to-use-azureml/automated-machine-learning/automl_setup.cmd
--- a/how-to-use-azureml/automated-machine-learning/automl_setup_linux.sh
+++ b/how-to-use-azureml/automated-machine-learning/automl_setup_linux.sh
--- a/how-to-use-azureml/automated-machine-learning/automl_setup_mac.sh
+++ b/how-to-use-azureml/automated-machine-learning/automl_setup_mac.sh
--- a/how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb
@@ -0,0 +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": [
    "# Automated Machine Learning: 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 [configuration](../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>average_precision_score_weighted</i><br><i>norm_macro_recall</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
 }
--- a/how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.ipynb
@@ -13,15 +13,15 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# AutoML 15a: Classification with ensembling on local compute\n",
+    "# Automated Machine Learning: Classification with 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",
+    "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",
+    "Make sure you have executed the [configuration](../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",
+    "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"
@@ -108,7 +108,9 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "## Load Training Data"
+    "## 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."
   ]
  },
  {
@@ -121,11 +123,9 @@
    "\n",
    "digits = datasets.load_digits()\n",
    "\n",
-    "# Exclude the first 50 rows from training so that they can be used for test.\n",
+    "# Exclude the first 100 rows from training so that they can be used for test.\n",
-    "X_train = digits.data[150:,:]\n",
+    "X_train = digits.data[100:,:]\n",
-    "y_train = digits.target[150:]\n",
+    "y_train = digits.target[100:]"
    "X_valid = digits.data[50:150]\n",
    "y_valid = digits.target[50:150]"
   ]
  },
  {
@@ -139,16 +139,12 @@
    "|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",
+    "|**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>average_precision_score_weighted</i><br><i>norm_macro_recall</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.|"
   ]
  },
@@ -159,17 +155,14 @@
   "outputs": [],
   "source": [
    "automl_config = AutoMLConfig(task = 'classification',\n",
-    "                             debug_log = 'classification.log',\n",
+    "                             debug_log = 'automl_errors.log',\n",
    "                             primary_metric = 'AUC_weighted',\n",
    "                             iteration_timeout_minutes = 60,\n",
-    "                             iterations = 10,\n",
+    "                             iterations = 25,\n",
    "                             n_cross_validations = 3,\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)"
   ]
  },
@@ -177,7 +170,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "## Train the Model\n",
+    "## 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."
@@ -215,9 +208,7 @@
   "outputs": [],
   "source": [
    "local_run = local_run.continue_experiment(X = X_train, \n",
-    "                                          y = y_train,\n",
+    "                                          y = y_train, \n",
    "                                          X_valid = X_valid,\n",
    "                                          y_valid = y_valid,\n",
    "                                          show_output = True,\n",
    "                                          iterations = 5)"
   ]
@@ -291,7 +282,7 @@
   "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*."
+    "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*."
   ]
  },
  {
@@ -370,7 +361,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "#### Testing Our Best Pipeline\n",
+    "#### Testing Our Best Fitted Model\n",
    "We will try to predict 2 digits and see how our model works."
   ]
  },
@@ -397,11 +388,11 @@
 "metadata": {
  "authors": [
   {
-    "name": "ratanase"
+    "name": "savitam"
   }
  ],
  "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
--- a/how-to-use-azureml/automated-machine-learning/classification_with_tensorflow/auto-ml-classification_with_tensorflow.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification_with_tensorflow/auto-ml-classification_with_tensorflow.ipynb
@@ -0,0 +1,390 @@
 {
 "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": [
    "# Automated Machine Learning: Classification with Local Compute with Tesnorflow DNNClassifier and LinearClassifier using whitelist models\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 [configuration](../configuration.ipynb) before running this notebook.\n",
    "This notebooks shows how can automl can be trained on a a selected list of models,see the readme.md for the models.\n",
    "This trains the model exclusively on tensorflow based models.\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 on a whilelisted models 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 = 10,\n",
    "                             n_cross_validations = 3,\n",
    "                             verbosity = logging.INFO,\n",
    "                             X = X_train, \n",
    "                             y = y_train,\n",
    "                             enable_tf=True,\n",
    "                             whitelist_models=[\"TensorFlowLinearClassifier\", \"TensorFlowDNN\"],\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\n"
   ]
  },
  {
   "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"
  }
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 "nbformat": 4,
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 }
--- a/how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb
@@ -0,0 +1,497 @@
 {
 "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": [
    "# Automated Machine Learning: 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 [configuration](../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 = 'mydsvmc'\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)\n",
    "    print(\"Waiting one minute for ssh to be accessible\")\n",
    "    time.sleep(60) # Wait for ssh to be accessible"
   ]
  },
  {
   "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",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
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--- a/how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/dataprep/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": [
    "# Automated Machine Learning: 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 [configuration](../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
 }
--- a/how-to-use-azureml/automated-machine-learning/dominicks_OJ.csv
+++ b/how-to-use-azureml/automated-machine-learning/dominicks_OJ.csv
--- a/how-to-use-azureml/automated-machine-learning/exploring-previous-runs/auto-ml-exploring-previous-runs.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/exploring-previous-runs/auto-ml-exploring-previous-runs.ipynb
@@ -0,0 +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": [
    "# Automated Machine Learning: 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 [configuration](../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",
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  }
 },
 "nbformat": 4,
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--- a/how-to-use-azureml/automated-machine-learning/forecasting-a/auto-ml-forecasting-a.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-a/auto-ml-forecasting-a.ipynb
@@ -0,0 +1,398 @@
 {
 "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": [
    "# Automated Machine Learning: Energy Demand Forecasting\n",
    "\n",
    "In this example, we show how AutoML can be used for energy demand forecasting.\n",
    "\n",
    "Make sure you have executed the [configuration](../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 with new task type \"forecasting\" for timeseries data training, and other timeseries related settings: for this dataset we use the basic one: \"time_column_name\" \n",
    "3. Training the Model using local compute\n",
    "4. Exploring the results\n",
    "5. Testing the fitted model"
   ]
  },
  {
   "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 azureml.core\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import logging\n",
    "import warnings\n",
    "# Squash warning messages for cleaner output in the notebook\n",
    "warnings.showwarning = lambda *args, **kwargs: None\n",
    "\n",
    "\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
   ]
  },
  {
   "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-energydemandforecasting'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-local-energydemandforecasting'\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['Run History Name'] = experiment_name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Data\n",
    "Read energy demanding data from file, and preview data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"nyc_energy.csv\", parse_dates=['timeStamp'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data to train and test\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = data[data['timeStamp'] < '2017-02-01']\n",
    "test = data[data['timeStamp'] >= '2017-02-01']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare the test data, we will feed X_test to the fitted model and get prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = test.pop('demand').values\n",
    "X_test = test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the train data to train and valid\n",
    "\n",
    "Use one month's data as valid data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train[train['timeStamp'] < '2017-01-01']\n",
    "X_valid = train[train['timeStamp'] >= '2017-01-01']\n",
    "y_train = X_train.pop('demand').values\n",
    "y_valid = X_valid.pop('demand').values\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_valid.shape)\n",
    "print(y_valid.shape)"
   ]
  },
  {
   "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**|forecasting|\n",
    "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting 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",
    "|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\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**|Data used to evaluate a model in a iteration. (sparse) array-like, shape = [n_samples, n_features]|\n",
    "|**y_valid**|Data used to evaluate a model in a iteration. (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": [
    "time_column_name = 'timeStamp'\n",
    "automl_settings = {\n",
    "    \"time_column_name\": time_column_name,\n",
    "}\n",
    "\n",
    "\n",
    "automl_config = AutoMLConfig(task = 'forecasting',\n",
    "                             debug_log = 'automl_nyc_energy_errors.log',\n",
    "                             primary_metric='normalized_root_mean_squared_error',\n",
    "                             iterations = 10,\n",
    "                             iteration_timeout_minutes = 5,\n",
    "                             X = X_train,\n",
    "                             y = y_train,\n",
    "                             X_valid = X_valid,\n",
    "                             y_valid = y_valid,\n",
    "                             path=project_folder,\n",
    "                             verbosity = logging.INFO,\n",
    "                            **automl_settings)"
   ]
  },
  {
   "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": [
    "### Retrieve the Best Model\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",
    "fitted_model.steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Best Fitted Model\n",
    "\n",
    "Predict on training and test set, and calculate residual values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = fitted_model.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define a Check Data Function\n",
    "\n",
    "Remove the nan values from y_test to avoid error when calculate metrics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _check_calc_input(y_true, y_pred, rm_na=True):\n",
    "    \"\"\"\n",
    "    Check that 'y_true' and 'y_pred' are non-empty and\n",
    "    have equal length.\n",
    "\n",
    "    :param y_true: Vector of actual values\n",
    "    :type y_true: array-like\n",
    "\n",
    "    :param y_pred: Vector of predicted values\n",
    "    :type y_pred: array-like\n",
    "\n",
    "    :param rm_na:\n",
    "        If rm_na=True, remove entries where y_true=NA and y_pred=NA.\n",
    "    :type rm_na: boolean\n",
    "\n",
    "    :return:\n",
    "        Tuple (y_true, y_pred). if rm_na=True,\n",
    "        the returned vectors may differ from their input values.\n",
    "    :rtype: Tuple with 2 entries\n",
    "    \"\"\"\n",
    "    if len(y_true) != len(y_pred):\n",
    "        raise ValueError(\n",
    "            'the true values and prediction values do not have equal length.')\n",
    "    elif len(y_true) == 0:\n",
    "        raise ValueError(\n",
    "            'y_true and y_pred are empty.')\n",
    "    # if there is any non-numeric element in the y_true or y_pred,\n",
    "    # the ValueError exception will be thrown.\n",
    "    y_true = np.array(y_true).astype(float)\n",
    "    y_pred = np.array(y_pred).astype(float)\n",
    "    if rm_na:\n",
    "        # remove entries both in y_true and y_pred where at least\n",
    "        # one element in y_true or y_pred is missing\n",
    "        y_true_rm_na = y_true[~(np.isnan(y_true) | np.isnan(y_pred))]\n",
    "        y_pred_rm_na = y_pred[~(np.isnan(y_true) | np.isnan(y_pred))]\n",
    "        return (y_true_rm_na, y_pred_rm_na)\n",
    "    else:\n",
    "        return y_true, y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use the Check Data Function to remove the nan values from y_test to avoid error when calculate metrics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test,y_pred =  _check_calc_input(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate metrics for the prediction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
    "print('R2 score: %.2f' % r2_score(y_test, y_pred))\n",
    "\n",
    "\n",
    "\n",
    "# Plot outputs\n",
    "%matplotlib notebook\n",
    "test_pred = plt.scatter(y_test, y_pred, color='b')\n",
    "test_test = plt.scatter(y_test, y_test, color='g')\n",
    "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "xiaga"
   }
  ],
  "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
 }
--- a/how-to-use-azureml/automated-machine-learning/forecasting-a/nyc_energy.csv
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-a/nyc_energy.csv
--- a/how-to-use-azureml/automated-machine-learning/forecasting-b/auto-ml-forecasting-b.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-b/auto-ml-forecasting-b.ipynb
@@ -0,0 +1,394 @@
 {
 "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": [
    "# Automated Machine Learning: Orange Juice Sales Forecasting\n",
    "\n",
    "In this example, we use AutoML to find and tune a time-series forecasting model.\n",
    "\n",
    "Make sure you have executed the [configuration notebook](../configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook, you will:\n",
    "1. Create an Experiment in an existing Workspace\n",
    "2. Instantiate an AutoMLConfig \n",
    "3. Find and train a forecasting model using local compute\n",
    "4. Evaluate the performance of the model\n",
    "\n",
    "## Sample Data\n",
    "The examples in the follow code samples use the [University of Chicago's Dominick's Finer Foods dataset](https://research.chicagobooth.edu/kilts/marketing-databases/dominicks) to forecast orange juice sales. Dominick's was a grocery chain in the Chicago metropolitan area."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Experiment\n",
    "\n",
    "As part of the setup you have already created a <b>Workspace</b>. To run AutoML, you also need to create an <b>Experiment</b>. An Experiment is a named object in a Workspace which represents a predictive task, the output of which is a trained model and a set of evaluation metrics for the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.core\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import logging\n",
    "import warnings\n",
    "# Squash warning messages for cleaner output in the notebook\n",
    "warnings.showwarning = lambda *args, **kwargs: None\n",
    "\n",
    "\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error"
   ]
  },
  {
   "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-ojsalesforecasting'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-local-ojsalesforecasting'\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['Run History Name'] = experiment_name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Data\n",
    "You are now ready to load the historical orange juice sales data. We will load the CSV file into a plain pandas DataFrame; the time column in the CSV is called _WeekStarting_, so it will be specially parsed into the datetime type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_column_name = 'WeekStarting'\n",
    "data = pd.read_csv(\"dominicks_OJ.csv\", parse_dates=[time_column_name])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each row in the DataFrame holds a quantity of weekly sales for an OJ brand at a single store. The data also includes the sales price, a flag indicating if the OJ brand was advertised in the store that week, and some customer demographic information based on the store location. For historical reasons, the data also include the logarithm of the sales quantity. The Dominick's grocery data is commonly used to illustrate econometric modeling techniques where logarithms of quantities are generally preferred.    \n",
    "\n",
    "The task is now to build a time-series model for the _Quantity_ column. It is important to note that this dataset is comprised of many individual time-series - one for each unique combination of _Store_ and _Brand_. To distinguish the individual time-series, we thus define the **grain** - the columns whose values determine the boundaries between time-series: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grain_column_names = ['Store', 'Brand']\n",
    "nseries = data.groupby(grain_column_names).ngroups\n",
    "print('Data contains {0} individual time-series.'.format(nseries))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Splitting\n",
    "For the purposes of demonstration and later forecast evaluation, we now split the data into a training and a testing set. The test set will contain the final 20 weeks of observed sales for each time-series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ntest_periods = 20\n",
    "\n",
    "def split_last_n_by_grain(df, n):\n",
    "    \"\"\"\n",
    "    Group df by grain and split on last n rows for each group\n",
    "    \"\"\"\n",
    "    df_grouped = (df.sort_values(time_column_name) # Sort by ascending time\n",
    "                  .groupby(grain_column_names, group_keys=False))\n",
    "    df_head = df_grouped.apply(lambda dfg: dfg.iloc[:-n])\n",
    "    df_tail = df_grouped.apply(lambda dfg: dfg.iloc[-n:])\n",
    "    return df_head, df_tail\n",
    "\n",
    "X_train, X_test = split_last_n_by_grain(data, ntest_periods)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "\n",
    "For forecasting tasks, AutoML uses pre-processing and estimation steps that are specific to time-series. AutoML will undertake the following pre-processing steps:\n",
    "* Detect time-series sample frequency (e.g. hourly, daily, weekly) and create new records for absent time points to make the series regular. A regular time series has a well-defined frequency and has a value at every sample point in a contiguous time span \n",
    "* Impute missing values in the target (via forward-fill) and feature columns (using median column values) \n",
    "* Create grain-based features to enable fixed effects across different series\n",
    "* Create time-based features to assist in learning seasonal patterns\n",
    "* Encode categorical variables to numeric quantities\n",
    "\n",
    "AutoML will currently train a single, regression-type model across **all** time-series in a given training set. This allows the model to generalize across related series.\n",
    "\n",
    "You are almost ready to start an AutoML training job. We will first need to create a validation set from the existing training set (i.e. for hyper-parameter tuning): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nvalidation_periods = 20\n",
    "X_train, X_validate = split_last_n_by_grain(X_train, nvalidation_periods)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to separate the target column from the rest of the DataFrame: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_column_name = 'Quantity'\n",
    "y_train = X_train.pop(target_column_name).values\n",
    "y_validate = X_validate.pop(target_column_name).values "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an AutoMLConfig\n",
    "\n",
    "The AutoMLConfig object defines the settings and data for an AutoML training job. Here, we set necessary inputs like the task type, the number of AutoML iterations to try, and the training and validation data. \n",
    "\n",
    "For forecasting tasks, there are some additional parameters that can be set: the name of the input data column, holding the date/time and the grain column names. A time column is required for forecasting, while the grain is optional. If a grain is not given, the forecaster assumes that the whole dataset is a single time-series. We also pass a list of columns to drop prior to modeling. The _logQuantity_ column is completely correlated with the target quantity, so it must be removed to prevent a target leak. \n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**task**|forecasting|\n",
    "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting 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",
    "|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
    "|**X**|Training matrix of features, shape = [n_training_samples, n_features]|\n",
    "|**y**|Target values, shape = [n_training_samples, ]|\n",
    "|**X_valid**|Validation matrix of features, shape = [n_validation_samples, n_features]|\n",
    "|**y_valid**|Target values for validation, shape = [n_validation_samples, ]\n",
    "|**enable_ensembling**|Allow AutoML to create ensembles of the best performing models\n",
    "|**debug_log**|Log file path for writing debugging information\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_settings = {\n",
    "    'time_column_name': time_column_name,\n",
    "    'grain_column_names': grain_column_names,\n",
    "    'drop_column_names': ['logQuantity']\n",
    "}\n",
    "\n",
    "automl_config = AutoMLConfig(task='forecasting',\n",
    "                             debug_log='automl_oj_sales_errors.log',\n",
    "                             primary_metric='normalized_root_mean_squared_error',\n",
    "                             iterations=10,\n",
    "                             X=X_train,\n",
    "                             y=y_train,\n",
    "                             X_valid=X_validate,\n",
    "                             y_valid=y_validate,\n",
    "                             enable_ensembling=False,\n",
    "                             path=project_folder,\n",
    "                             verbosity=logging.INFO,\n",
    "                            **automl_settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model\n",
    "\n",
    "You can now submit a new training run. For local runs, the execution is synchronous. Depending on the data and number of iterations this operation may take several minutes.\n",
    "Information from each iteration will be printed 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",
    "Each run within an Experiment stores serialized (i.e. pickled) pipelines from the AutoML iterations. We can now retrieve the pipeline with the best performance on the validation dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_pipeline = local_run.get_output()\n",
    "fitted_pipeline.steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make Predictions from the Best Fitted Model\n",
    "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. First, we remove the target values from the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = X_test.pop(target_column_name).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To produce predictions on the test set, we need to know the feature values at all dates in the test set. This requirement is somewhat reasonable for the OJ sales data since the features mainly consist of price, which is usually set in advance, and customer demographics which are approximately constant for each store over the 20 week forecast horizon in the testing data. \n",
    "\n",
    "The target predictions can be retrieved by calling the `predict` method on the best model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = fitted_pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate evaluation metrics for the prediction\n",
    "To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MAPE(actual, pred):\n",
    "    \"\"\"\n",
    "    Calculate mean absolute percentage error.\n",
    "    Remove NA and values where actual is close to zero\n",
    "    \"\"\"\n",
    "    not_na = ~(np.isnan(actual) | np.isnan(pred))\n",
    "    not_zero = ~np.isclose(actual, 0.0)\n",
    "    actual_safe = actual[not_na & not_zero]\n",
    "    pred_safe = pred[not_na & not_zero]\n",
    "    APE = 100*np.abs((actual_safe - pred_safe)/actual_safe)\n",
    "    return np.mean(APE)\n",
    "\n",
    "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
    "print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
    "print('MAPE: %.2f' % MAPE(y_test, y_pred))"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "erwright"
   }
  ],
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  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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--- a/how-to-use-azureml/automated-machine-learning/forecasting-b/dominicks_OJ.csv
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-b/dominicks_OJ.csv
--- a/how-to-use-azureml/automated-machine-learning/missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.ipynb
@@ -0,0 +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": [
    "# Automated Machine Learning: 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 [configuration](../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>average_precision_score_weighted</i><br><i>norm_macro_recall</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",
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    "version": 3
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--- a/how-to-use-azureml/automated-machine-learning/model-explanation/auto-ml-model-explanation.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/model-explanation/auto-ml-model-explanation.ipynb
@@ -0,0 +1,348 @@
 {
 "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": [
    "# Automated Machine Learning: 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 [configuration](../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": [
    "## 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. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</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",
    "                             iteration_timeout_minutes = 200,\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": "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
 }
--- a/how-to-use-azureml/automated-machine-learning/nyc_energy.csv
+++ b/how-to-use-azureml/automated-machine-learning/nyc_energy.csv
--- a/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb
@@ -13,16 +13,16 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# AutoML 15b: Regression with ensembling on remote compute\n",
+    "# AutoML: Regression with Local 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",
+    "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`which enables an extra ensembling iteration.\n",
+    "2. Configure AutoML using `AutoMLConfig`.\n",
-    "3. Train the model using remote compute.\n",
+    "3. Train the model using local compute.\n",
    "4. Explore the results.\n",
    "5. Test the best fitted model.\n"
   ]
@@ -108,8 +108,8 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "## Create a Remote Linux DSVM\n",
+    "### Load Training Data\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."
+    "This uses scikit-learn's [load_diabetes](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html) method."
   ]
  },
  {
@@ -118,60 +118,17 @@
   "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",
    "    X, y = load_diabetes(return_X_y = True)\n",
    "\n",
-    "    columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\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_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
    "    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 }"
   ]
  },
  {
@@ -188,8 +145,9 @@
    "|**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",
+    "|**n_cross_validations**|Number of cross validation splits.|\n",
-    "|**ensemble_iterations**|Number of iterations during which we choose a fitted pipeline to be part of the final ensemble.|\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.|"
   ]
  },
@@ -201,14 +159,13 @@
   "source": [
    "automl_config = AutoMLConfig(task = 'regression',\n",
    "                             iteration_timeout_minutes = 10,\n",
-    "                             iterations = 20,\n",
+    "                             iterations = 10,\n",
    "                             primary_metric = 'spearman_correlation',\n",
-    "                             debug_log = 'regression.log',\n",
+    "                             n_cross_validations = 5,\n",
    "                             debug_log = 'automl.log',\n",
    "                             verbosity = logging.INFO,\n",
-    "                             compute_target = dsvm_compute,\n",
+    "                             X = X_train, \n",
-    "                             data_script = project_folder + \"/get_data.py\",\n",
+    "                             y = y_train,\n",
    "                             enable_ensembling = True,\n",
    "                             ensemble_iterations = 5,\n",
    "                             path = project_folder)"
   ]
  },
@@ -216,7 +173,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "## Train the Model\n",
+    "## 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."
@@ -300,7 +257,7 @@
   "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*."
+    "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*."
   ]
  },
  {
@@ -319,7 +276,7 @@
   "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."
+    "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):"
   ]
  },
  {
@@ -338,7 +295,27 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "### Test the Best Model (Ensemble)"
+    "#### 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"
   ]
  },
  {
@@ -354,17 +331,6 @@
   "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",
@@ -423,11 +389,11 @@
 "metadata": {
  "authors": [
   {
-    "name": "ratanase"
+    "name": "savitam"
   }
  ],
  "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
--- a/how-to-use-azureml/automated-machine-learning/remote-attach/auto-ml-remote-attach.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/remote-attach/auto-ml-remote-attach.ipynb
@@ -0,0 +1,517 @@
 {
 "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": [
    "# Automated Machine Learning: Remote Execution using attach\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 to handle text data with remote attach.\n",
    "\n",
    "Make sure you have executed the [configuration](../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 ComputeTarget, 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",
    "    attach_config = RemoteCompute.attach_configuration(address=dsvm_ip_addr, username=dsvm_username, password=dsvm_password, ssh_port=dsvm_ssh_port)\n",
    "    ComputeTarget.attach(workspace=ws, name=compute_name, attach_configuration=attach_config)\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.detach()"
   ]
  },
  {
   "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",
    "\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",
    "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 numpy as np\n",
    "from sklearn.datasets import fetch_20newsgroups\n",
    "\n",
    "def get_data():\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 = np.array(data_train.data).reshape((len(data_train.data),1))\n",
    "    y_train = np.array(data_train.target)\n",
    "    \n",
    "    return { \"X\" : X_train, \"y\" : y_train }"
   ]
  },
  {
   "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>average_precision_score_weighted</i><br><i>norm_macro_recall</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",
    "|**enable_cache**|Setting this to *True* enables preprocess done once and reuse the same preprocessed data for all the iterations. Default value is True.\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",
    "                             run_configuration=conda_run_config,\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": [
    "## Pre-process cache cleanup\n",
    "The preprocess data gets cache at user default file store. When the run is completed the cache can be cleaned by running below cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run.clean_preprocessor_cache()"
   ]
  },
  {
   "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": [
    "# Load test data.\n",
    "from pandas_ml import ConfusionMatrix\n",
    "from sklearn.datasets import fetch_20newsgroups\n",
    "\n",
    "remove = ('headers', 'footers', 'quotes')\n",
    "categories = [\n",
    "        'alt.atheism',\n",
    "        'talk.religion.misc',\n",
    "        'comp.graphics',\n",
    "        'sci.space',\n",
    "    ]\n",
    "\n",
    "data_test = fetch_20newsgroups(subset = 'test', categories = categories,\n",
    "                               shuffle = True, random_state = 42,\n",
    "                               remove = remove)\n",
    "\n",
    "X_test = np.array(data_test.data).reshape((len(data_test.data),1))\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",
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 "nbformat": 4,
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 }
--- a/how-to-use-azureml/automated-machine-learning/remote-batchai/auto-ml-remote-batchai.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/remote-batchai/auto-ml-remote-batchai.ipynb
@@ -0,0 +1,528 @@
 {
 "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": [
    "# Automated Machine Learning: Remote Execution using Batch AI\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 [configuration](../configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you would see\n",
    "1. Create an `Experiment` in an existing `Workspace`.\n",
    "2. Attach an existing Batch AI compute to a workspace.\n",
    "3. Configure AutoML using `AutoMLConfig`.\n",
    "4. Train the model using Batch AI.\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-batchai'\n",
    "project_folder = './sample_projects/automl-remote-batchai'\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 Batch AI Cluster\n",
    "The cluster is created as Machine Learning Compute and will appear under your workspace.\n",
    "\n",
    "**Note:** The creation of the Batch AI cluster can take over 10 minutes, please be patient.\n",
    "\n",
    "As with other Azure services, there are limits on certain resources (e.g. Batch AI 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.compute import AmlCompute\n",
    "from azureml.core.compute import ComputeTarget\n",
    "\n",
    "# Choose a name for your cluster.\n",
    "batchai_cluster_name = \"automlcl\"\n",
    "\n",
    "found = False\n",
    "# Check if this compute target already exists in the workspace.\n",
    "cts = ws.compute_targets\n",
    "if batchai_cluster_name in cts and cts[batchai_cluster_name].type == 'BatchAI':\n",
    "    found = True\n",
    "    print('Found existing compute target.')\n",
    "    compute_target = cts[batchai_cluster_name]\n",
    "    \n",
    "if not found:\n",
    "    print('Creating a new compute target...')\n",
    "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
    "                                                                #vm_priority = 'lowpriority', # optional\n",
    "                                                                max_nodes = 6)\n",
    "\n",
    "    # Create the cluster.\n",
    "    compute_target = ComputeTarget.create(ws, batchai_cluster_name, provisioning_config)\n",
    "    \n",
    "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
    "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
    "    compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
    "    \n",
    "     # For a more detailed view of current Batch AI cluster status, use the 'status' property."
   ]
  },
  {
   "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 Batch AI cluster\n",
    "conda_run_config.target = compute_target\n",
    "conda_run_config.environment.docker.enabled = True\n",
    "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\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",
    "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\n",
    "    y_train = digits.target\n",
    "\n",
    "    return { \"X\" : X_train, \"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 a `get_data()` function for local excutions too.\n",
    "\n",
    "**Note:** When using Batch AI, 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>average_precision_score_weighted</i><br><i>norm_macro_recall</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.|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_settings = {\n",
    "    \"iteration_timeout_minutes\": 2,\n",
    "    \"iterations\": 20,\n",
    "    \"n_cross_validations\": 5,\n",
    "    \"primary_metric\": 'AUC_weighted',\n",
    "    \"preprocess\": False,\n",
    "    \"max_concurrent_iterations\": 5,\n",
    "    \"verbosity\": logging.INFO\n",
    "}\n",
    "\n",
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             path = project_folder,\n",
    "                             run_configuration=conda_run_config,\n",
    "                             data_script = project_folder + \"/get_data.py\",\n",
    "                             **automl_settings\n",
    "                            )\n"
   ]
  },
  {
   "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",
    "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_5db13491-c92a-4f1d-b622-8ab8d973a058')"
   ]
  },
  {
   "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": [
    "remote_run"
   ]
  },
  {
   "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": [
    "### Testing the 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": [
    "#### Testing 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",
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 "nbformat": 4,
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 }
--- a/how-to-use-azureml/automated-machine-learning/remote-execution-with-datastore/auto-ml-remote-execution-with-datastore.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/remote-execution-with-datastore/auto-ml-remote-execution-with-datastore.ipynb
@@ -0,0 +1,583 @@
 {
 "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": [
    "# Automated Machine Learning: 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 [configuration](../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)\n",
    "    print(\"Waiting one minute for ssh to be accessible\")\n",
    "    time.sleep(60) # Wait for ssh to be accessible"
   ]
  },
  {
   "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": [
    "from sklearn.datasets import fetch_20newsgroups\n",
    "import csv\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",
    "pd.DataFrame(data_train.data).to_csv(\"data/X_train.tsv\", index=False, header=False, quoting=csv.QUOTE_ALL, sep=\"\\t\")\n",
    "pd.DataFrame(data_train.target).to_csv(\"data/y_train.tsv\", index=False, header=False, sep=\"\\t\")"
   ]
  },
  {
   "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",
    "\n",
    "def get_data():\n",
    "    X_train = pd.read_csv(\"/tmp/azureml_runs/data/X_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
    "    y_train = pd.read_csv(\"/tmp/azureml_runs/data/y_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
    "\n",
    "    return { \"X\" : X_train.values, \"y\" : y_train[0].values }"
   ]
  },
  {
   "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. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</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",
    "|**enable_cache**|Setting this to *True* enables preprocess done once and reuse the same preprocessed data for all the iterations. Default value is True.|\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": [
    "## Pre-process cache cleanup\n",
    "The preprocess data gets cache at user default file store. When the run is completed the cache can be cleaned by running below cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run.clean_preprocessor_cache()"
   ]
  },
  {
   "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": [
    "# 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 = np.array(data_test.data).reshape((len(data_test.data),1))\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,
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 }
--- a/how-to-use-azureml/automated-machine-learning/remote-execution/auto-ml-remote-execution.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/remote-execution/auto-ml-remote-execution.ipynb
@@ -0,0 +1,507 @@
 {
 "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": [
    "# Automated Machine Learning: 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 [configuration](../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",
    "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.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)\n",
    "    print(\"Waiting one minute for ssh to be accessible\")\n",
    "    time.sleep(60) # Wait for ssh to be accessible"
   ]
  },
  {
   "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",
    "\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",
    "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>average_precision_score_weighted</i><br><i>norm_macro_recall</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",
    "                             run_configuration=conda_run_config,\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,
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 }
--- a/how-to-use-azureml/automated-machine-learning/sample-weight/auto-ml-sample-weight.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/sample-weight/auto-ml-sample-weight.ipynb
@@ -0,0 +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": [
    "# Automated Machine Learning: 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 [configuration](../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
 }
--- a/how-to-use-azureml/automated-machine-learning/sparse-data-train-test-split/auto-ml-sparse-data-train-test-split.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/sparse-data-train-test-split/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": [
    "# Automated Machine Learning: 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 [configuration](../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>average_precision_score_weighted</i><br><i>norm_macro_recall</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
 }
--- a/how-to-use-azureml/azure-databricks/01.Installation_and_Configuration.ipynb
+++ b/how-to-use-azureml/azure-databricks/01.Installation_and_Configuration.ipynb
@@ -0,0 +1,264 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
    "\n",
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package. You can select the option to attach the library to all clusters or just one cluster.\n",
    "\n",
    "**azureml-sdk**\n",
    "* Source: Upload Python Egg or PyPi\n",
    "* PyPi Name: `azureml-sdk[databricks]`\n",
    "* Select Install Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.core\n",
    "\n",
    "# Check core SDK version number - based on build number of preview/master.\n",
    "print(\"SDK version:\", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![04ACI](files/tables/image2b.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please specify the Azure subscription Id, resource group name, workspace name, and the region in which you want to create the Azure Machine Learning Workspace.\n",
    "\n",
    "You can get the value of your Azure subscription ID from the Azure Portal, and then selecting Subscriptions from the menu on the left.\n",
    "\n",
    "For the resource_group, use the name of the resource group that contains your Azure Databricks Workspace.\n",
    "\n",
    "NOTE: If you provide a resource group name that does not exist, the resource group will be automatically created. This may or may not succeed in your environment, depending on the permissions you have on your Azure Subscription."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subscription_id = \"<your-subscription-id>\"\n",
    "# resource_group = \"<your-existing-resource-group>\"\n",
    "# workspace_name = \"<a-new-or-existing-workspace; it is unrelated to Databricks workspace>\"\n",
    "# workspace_region = \"<your-resource group-region>\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##TESTONLY\n",
    "# import auth creds from notebook parameters\n",
    "tenant = dbutils.widgets.get('tenant_id')\n",
    "username = dbutils.widgets.get('service_principal_id')\n",
    "password = dbutils.widgets.get('service_principal_password')\n",
    "\n",
    "auth = azureml.core.authentication.ServicePrincipalAuthentication(tenant, username, password)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##TESTONLY\n",
    "subscription_id  = dbutils.widgets.get('subscription_id')\n",
    "resource_group = dbutils.widgets.get('resource_group')\n",
    "workspace_name = dbutils.widgets.get('workspace_name')\n",
    "workspace_region = dbutils.widgets.get('workspace_region')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##TESTONLY\n",
    "# import the Workspace class and check the azureml SDK version\n",
    "# exist_ok checks if workspace exists or not.\n",
    "\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",
    "                      auth = auth,\n",
    "                      exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##PUBLISHONLY\n",
    "## import the Workspace class and check the azureml SDK version\n",
    "## exist_ok checks if workspace exists or not.\n",
    "#\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",
    "#                      exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get workspace details\n",
    "ws.get_details()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##TESTONLY\n",
    "ws = Workspace(workspace_name = workspace_name,\n",
    "               subscription_id = subscription_id,\n",
    "               resource_group = resource_group,\n",
    "               auth = auth)\n",
    "\n",
    "# persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
    "ws.write_config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##PUBLISHONLY\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()\n",
    "###if you need to give a different path/filename please use this\n",
    "###write_config(path=\"/databricks/driver/aml_config/\",file_name=<alias_conf.cfg>)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "help(Workspace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##TESTONLY\n",
    "# import the Workspace class and check the azureml SDK version\n",
    "from azureml.core import Workspace\n",
    "\n",
    "ws = Workspace.from_config(auth = auth)\n",
    "#ws = Workspace.from_config(<full path>)\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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##PUBLISHONLY\n",
    "## import the Workspace class and check the azureml SDK version\n",
    "#from azureml.core import Workspace\n",
    "#\n",
    "#ws = Workspace.from_config()\n",
    "##ws = Workspace.from_config(<full path>)\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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "pasha"
   },
   {
    "name": "wamartin"
   }
  ],
  "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": "3.7.0"
  },
  "name": "01.Installation_and_Configuration",
  "notebookId": 3836944406456490
 },
 "nbformat": 4,
 "nbformat_minor": 1
 }
--- a/how-to-use-azureml/azure-databricks/02.Ingest_data.ipynb
+++ b/how-to-use-azureml/azure-databricks/02.Ingest_data.ipynb
@@ -0,0 +1,182 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
    "\n",
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![04ACI](files/tables/image1.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Data Ingestion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import urllib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download AdultCensusIncome.csv from Azure CDN. This file has 32,561 rows.\n",
    "basedataurl = \"https://amldockerdatasets.azureedge.net\"\n",
    "datafile = \"AdultCensusIncome.csv\"\n",
    "datafile_dbfs = os.path.join(\"/dbfs\", datafile)\n",
    "\n",
    "if os.path.isfile(datafile_dbfs):\n",
    "    print(\"found {} at {}\".format(datafile, datafile_dbfs))\n",
    "else:\n",
    "    print(\"downloading {} to {}\".format(datafile, datafile_dbfs))\n",
    "    urllib.request.urlretrieve(os.path.join(basedataurl, datafile), datafile_dbfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Spark dataframe out of the csv file.\n",
    "data_all = sqlContext.read.format('csv').options(header='true', inferSchema='true', ignoreLeadingWhiteSpace='true', ignoreTrailingWhiteSpace='true').load(datafile)\n",
    "print(\"({}, {})\".format(data_all.count(), len(data_all.columns)))\n",
    "data_all.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#renaming columns\n",
    "columns_new = [col.replace(\"-\", \"_\") for col in data_all.columns]\n",
    "data_all = data_all.toDF(*columns_new)\n",
    "data_all.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "display(data_all.limit(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose feature columns and the label column.\n",
    "label = \"income\"\n",
    "xvars = set(data_all.columns) - {label}\n",
    "\n",
    "print(\"label = {}\".format(label))\n",
    "print(\"features = {}\".format(xvars))\n",
    "\n",
    "data = data_all.select([*xvars, label])\n",
    "\n",
    "# Split data into train and test.\n",
    "train, test = data.randomSplit([0.75, 0.25], seed=123)\n",
    "\n",
    "print(\"train ({}, {})\".format(train.count(), len(train.columns)))\n",
    "print(\"test ({}, {})\".format(test.count(), len(test.columns)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Data Persistence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write the train and test data sets to intermediate storage\n",
    "train_data_path = \"AdultCensusIncomeTrain\"\n",
    "test_data_path = \"AdultCensusIncomeTest\"\n",
    "\n",
    "train_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTrain\")\n",
    "test_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTest\")\n",
    "\n",
    "train.write.mode('overwrite').parquet(train_data_path)\n",
    "test.write.mode('overwrite').parquet(test_data_path)\n",
    "print(\"train and test datasets saved to {} and {}\".format(train_data_path_dbfs, test_data_path_dbfs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "pasha"
   },
   {
    "name": "wamartin"
   }
  ],
  "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": "3.7.0"
  },
  "name": "02.Ingest_data",
  "notebookId": 3836944406456362
 },
 "nbformat": 4,
 "nbformat_minor": 1
 }
--- a/how-to-use-azureml/azure-databricks/03.Build_model_runHistory.ipynb
+++ b/how-to-use-azureml/azure-databricks/03.Build_model_runHistory.ipynb
@@ -0,0 +1,396 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
    "\n",
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![04ACI](files/tables/image2.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pprint\n",
    "import numpy as np\n",
    "\n",
    "from pyspark.ml import Pipeline, PipelineModel\n",
    "from pyspark.ml.feature import OneHotEncoder, StringIndexer, VectorAssembler\n",
    "from pyspark.ml.classification import LogisticRegression\n",
    "from pyspark.ml.evaluation import BinaryClassificationEvaluator\n",
    "from pyspark.ml.tuning import CrossValidator, ParamGridBuilder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.core\n",
    "\n",
    "# Check core SDK version number\n",
    "print(\"SDK version:\", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##TESTONLY\n",
    "# import auth creds from notebook parameters\n",
    "tenant = dbutils.widgets.get('tenant_id')\n",
    "username = dbutils.widgets.get('service_principal_id')\n",
    "password = dbutils.widgets.get('service_principal_password')\n",
    "\n",
    "auth = azureml.core.authentication.ServicePrincipalAuthentication(tenant, username, password)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the Workspace class and check the azureml SDK version\n",
    "from azureml.core import Workspace\n",
    "\n",
    "ws = Workspace.from_config(auth = auth)\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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##PUBLISHONLY\n",
    "## import the Workspace class and check the azureml SDK version\n",
    "#from azureml.core 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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get the train and test datasets\n",
    "train_data_path = \"AdultCensusIncomeTrain\"\n",
    "test_data_path = \"AdultCensusIncomeTest\"\n",
    "\n",
    "train = spark.read.parquet(train_data_path)\n",
    "test = spark.read.parquet(test_data_path)\n",
    "\n",
    "print(\"train: ({}, {})\".format(train.count(), len(train.columns)))\n",
    "print(\"test: ({}, {})\".format(test.count(), len(test.columns)))\n",
    "\n",
    "train.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Define Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "label = \"income\"\n",
    "dtypes = dict(train.dtypes)\n",
    "dtypes.pop(label)\n",
    "\n",
    "si_xvars = []\n",
    "ohe_xvars = []\n",
    "featureCols = []\n",
    "for idx,key in enumerate(dtypes):\n",
    "    if dtypes[key] == \"string\":\n",
    "        featureCol = \"-\".join([key, \"encoded\"])\n",
    "        featureCols.append(featureCol)\n",
    "        \n",
    "        tmpCol = \"-\".join([key, \"tmp\"])\n",
    "        # string-index and one-hot encode the string column\n",
    "        #https://spark.apache.org/docs/2.3.0/api/java/org/apache/spark/ml/feature/StringIndexer.html\n",
    "        #handleInvalid: Param for how to handle invalid data (unseen labels or NULL values). \n",
    "        #Options are 'skip' (filter out rows with invalid data), 'error' (throw an error), \n",
    "        #or 'keep' (put invalid data in a special additional bucket, at index numLabels). Default: \"error\"\n",
    "        si_xvars.append(StringIndexer(inputCol=key, outputCol=tmpCol, handleInvalid=\"skip\"))\n",
    "        ohe_xvars.append(OneHotEncoder(inputCol=tmpCol, outputCol=featureCol))\n",
    "    else:\n",
    "        featureCols.append(key)\n",
    "\n",
    "# string-index the label column into a column named \"label\"\n",
    "si_label = StringIndexer(inputCol=label, outputCol='label')\n",
    "\n",
    "# assemble the encoded feature columns in to a column named \"features\"\n",
    "assembler = VectorAssembler(inputCols=featureCols, outputCol=\"features\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.run import Run\n",
    "from azureml.core.experiment import Experiment\n",
    "import numpy as np\n",
    "import os\n",
    "import shutil\n",
    "\n",
    "model_name = \"AdultCensus_runHistory.mml\"\n",
    "model_dbfs = os.path.join(\"/dbfs\", model_name)\n",
    "run_history_name = 'spark-ml-notebook'\n",
    "\n",
    "# start a training run by defining an experiment\n",
    "myexperiment = Experiment(ws, \"Ignite_AI_Talk\")\n",
    "root_run = myexperiment.start_logging()\n",
    "\n",
    "# Regularization Rates - \n",
    "regs = [0.0001, 0.001, 0.01, 0.1]\n",
    " \n",
    "# try a bunch of regularization rate in a Logistic Regression model\n",
    "for reg in regs:\n",
    "    print(\"Regularization rate: {}\".format(reg))\n",
    "    # create a bunch of child runs\n",
    "    with root_run.child_run(\"reg-\" + str(reg)) as run:\n",
    "        # create a new Logistic Regression model.\n",
    "        lr = LogisticRegression(regParam=reg)\n",
    "        \n",
    "        # put together the pipeline\n",
    "        pipe = Pipeline(stages=[*si_xvars, *ohe_xvars, si_label, assembler, lr])\n",
    "\n",
    "        # train the model\n",
    "        model_p = pipe.fit(train)\n",
    "        \n",
    "        # make prediction\n",
    "        pred = model_p.transform(test)\n",
    "        \n",
    "        # evaluate. note only 2 metrics are supported out of the box by Spark ML.\n",
    "        bce = BinaryClassificationEvaluator(rawPredictionCol='rawPrediction')\n",
    "        au_roc = bce.setMetricName('areaUnderROC').evaluate(pred)\n",
    "        au_prc = bce.setMetricName('areaUnderPR').evaluate(pred)\n",
    "\n",
    "        print(\"Area under ROC: {}\".format(au_roc))\n",
    "        print(\"Area Under PR: {}\".format(au_prc))\n",
    "      \n",
    "        # log reg, au_roc, au_prc and feature names in run history\n",
    "        run.log(\"reg\", reg)\n",
    "        run.log(\"au_roc\", au_roc)\n",
    "        run.log(\"au_prc\", au_prc)\n",
    "        run.log_list(\"columns\", train.columns)\n",
    "\n",
    "        # save model\n",
    "        model_p.write().overwrite().save(model_name)\n",
    "        \n",
    "        # upload the serialized model into run history record\n",
    "        mdl, ext = model_name.split(\".\")\n",
    "        model_zip = mdl + \".zip\"\n",
    "        shutil.make_archive(mdl, 'zip', model_dbfs)\n",
    "        run.upload_file(\"outputs/\" + model_name, model_zip)        \n",
    "        #run.upload_file(\"outputs/\" + model_name, path_or_stream = model_dbfs) #cannot deal with folders\n",
    "\n",
    "        # now delete the serialized model from local folder since it is already uploaded to run history \n",
    "        shutil.rmtree(model_dbfs)\n",
    "        os.remove(model_zip)\n",
    "        \n",
    "# Declare run completed\n",
    "root_run.complete()\n",
    "root_run_id = root_run.id\n",
    "print (\"run id:\", root_run.id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = root_run.get_metrics(recursive=True)\n",
    "best_run_id = max(metrics, key = lambda k: metrics[k]['au_roc'])\n",
    "print(best_run_id, metrics[best_run_id]['au_roc'], metrics[best_run_id]['reg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get the best run\n",
    "child_runs = {}\n",
    "\n",
    "for r in root_run.get_children():\n",
    "    child_runs[r.id] = r\n",
    "   \n",
    "best_run = child_runs[best_run_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Download the model from the best run to a local folder\n",
    "best_model_file_name = \"best_model.zip\"\n",
    "best_run.download_file(name = 'outputs/' + model_name, output_file_path = best_model_file_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##unzip the model to dbfs (as load() seems to require that) and load it.\n",
    "if os.path.isfile(model_dbfs) or os.path.isdir(model_dbfs):\n",
    "    shutil.rmtree(model_dbfs)\n",
    "shutil.unpack_archive(best_model_file_name, model_dbfs)\n",
    "\n",
    "model_p_best = PipelineModel.load(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make prediction\n",
    "pred = model_p_best.transform(test)\n",
    "output = pred[['hours_per_week','age','workclass','marital_status','income','prediction']]\n",
    "display(output.limit(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# evaluate. note only 2 metrics are supported out of the box by Spark ML.\n",
    "bce = BinaryClassificationEvaluator(rawPredictionCol='rawPrediction')\n",
    "au_roc = bce.setMetricName('areaUnderROC').evaluate(pred)\n",
    "au_prc = bce.setMetricName('areaUnderPR').evaluate(pred)\n",
    "\n",
    "print(\"Area under ROC: {}\".format(au_roc))\n",
    "print(\"Area Under PR: {}\".format(au_prc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Model Persistence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##NOTE: by default the model is saved to and loaded from /dbfs/ instead of cwd!\n",
    "model_p_best.write().overwrite().save(model_name)\n",
    "print(\"saved model to {}\".format(model_dbfs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%sh\n",
    "\n",
    "ls -la /dbfs/AdultCensus_runHistory.mml/*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dbutils.notebook.exit(\"success\")"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "pasha"
   },
   {
    "name": "wamartin"
   }
  ],
  "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": "3.7.0"
  },
  "name": "03.Build_model_runHistory",
  "notebookId": 3836944406456339
 },
 "nbformat": 4,
 "nbformat_minor": 1
 }
--- a/how-to-use-azureml/azure-databricks/04.Deploy_to_ACI.ipynb
+++ b/how-to-use-azureml/azure-databricks/04.Deploy_to_ACI.ipynb
@@ -0,0 +1,354 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
    "\n",
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please ensure you have run all previous notebooks in sequence before running this.\n",
    "\n",
    "Please Register Azure Container Instance(ACI) using Azure Portal: https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-manager-supported-services#portal in your subscription before using the SDK to deploy your ML model to ACI."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![04ACI](files/tables/image3.JPG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.core\n",
    "\n",
    "# Check core SDK version number\n",
    "print(\"SDK version:\", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##TESTONLY\n",
    "# import auth creds from notebook parameters\n",
    "tenant = dbutils.widgets.get('tenant_id')\n",
    "username = dbutils.widgets.get('service_principal_id')\n",
    "password = dbutils.widgets.get('service_principal_password')\n",
    "\n",
    "auth = azureml.core.authentication.ServicePrincipalAuthentication(tenant, username, password)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Workspace\n",
    "\n",
    "#'''\n",
    "ws = Workspace.from_config(auth = auth)\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",
    "#'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##PUBLISHONLY\n",
    "#from azureml.core import Workspace\n",
    "#import azureml.core\n",
    "#\n",
    "## Check core SDK version number\n",
    "#print(\"SDK version:\", azureml.core.VERSION)\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')\n",
    "##'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##NOTE: service deployment always gets the model from the current working dir.\n",
    "import os\n",
    "\n",
    "model_name = \"AdultCensus_runHistory.mml\" # \n",
    "model_name_dbfs = os.path.join(\"/dbfs\", model_name)\n",
    "\n",
    "print(\"copy model from dbfs to local\")\n",
    "model_local = \"file:\" + os.getcwd() + \"/\" + model_name\n",
    "dbutils.fs.cp(model_name, model_local, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Register the model\n",
    "from azureml.core.model import Model\n",
    "mymodel = Model.register(model_path = model_name, # this points to a local file\n",
    "                       model_name = model_name, # this is the name the model is registered as, am using same name for both path and name.                 \n",
    "                       description = \"ADB trained model by Parashar\",\n",
    "                       workspace = ws)\n",
    "\n",
    "print(mymodel.name, mymodel.description, mymodel.version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%%writefile score_sparkml.py\n",
    "score_sparkml = \"\"\"\n",
    " \n",
    "import json\n",
    " \n",
    "def init():\n",
    "    # One-time initialization of PySpark and predictive model\n",
    "    import pyspark\n",
    "    from azureml.core.model import Model\n",
    "    from pyspark.ml import PipelineModel\n",
    " \n",
    "    global trainedModel\n",
    "    global spark\n",
    " \n",
    "    spark = pyspark.sql.SparkSession.builder.appName(\"ADB and AML notebook by Parashar\").getOrCreate()\n",
    "    model_name = \"{model_name}\" #interpolated\n",
    "    model_path = Model.get_model_path(model_name)\n",
    "    trainedModel = PipelineModel.load(model_path)\n",
    "    \n",
    "def run(input_json):\n",
    "    if isinstance(trainedModel, Exception):\n",
    "        return json.dumps({{\"trainedModel\":str(trainedModel)}})\n",
    "      \n",
    "    try:\n",
    "        sc = spark.sparkContext\n",
    "        input_list = json.loads(input_json)\n",
    "        input_rdd = sc.parallelize(input_list)\n",
    "        input_df = spark.read.json(input_rdd)\n",
    "    \n",
    "        # Compute prediction\n",
    "        prediction = trainedModel.transform(input_df)\n",
    "        #result = prediction.first().prediction\n",
    "        predictions = prediction.collect()\n",
    " \n",
    "        #Get each scored result\n",
    "        preds = [str(x['prediction']) for x in predictions]\n",
    "        result = \",\".join(preds)\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",
    "        return result\n",
    "    \n",
    "\"\"\".format(model_name=model_name)\n",
    " \n",
    "exec(score_sparkml)\n",
    " \n",
    "with open(\"score_sparkml.py\", \"w\") as file:\n",
    "    file.write(score_sparkml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.conda_dependencies import CondaDependencies \n",
    "\n",
    "myacienv = CondaDependencies.create(conda_packages=['scikit-learn','numpy','pandas']) #showing how to add libs as an eg. - not needed for this model.\n",
    "\n",
    "with open(\"mydeployenv.yml\",\"w\") as f:\n",
    "    f.write(myacienv.serialize_to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#deploy to ACI\n",
    "from azureml.core.webservice import AciWebservice, Webservice\n",
    "\n",
    "myaci_config = AciWebservice.deploy_configuration(\n",
    "    cpu_cores = 2, \n",
    "    memory_gb = 2, \n",
    "    tags = {'name':'Databricks Azure ML ACI'}, \n",
    "    description = 'This is for ADB and AML example. Azure Databricks & Azure ML SDK demo with ACI by Parashar.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this will take 10-15 minutes to finish\n",
    "\n",
    "service_name = \"aciws\"\n",
    "runtime = \"spark-py\" \n",
    "driver_file = \"score_sparkml.py\"\n",
    "my_conda_file = \"mydeployenv.yml\"\n",
    "\n",
    "# image creation\n",
    "from azureml.core.image import ContainerImage\n",
    "myimage_config = ContainerImage.image_configuration(execution_script = driver_file, \n",
    "                                    runtime = runtime, \n",
    "                                    conda_file = my_conda_file)\n",
    "\n",
    "# Webservice creation\n",
    "myservice = Webservice.deploy_from_model(\n",
    "  workspace=ws, \n",
    "  name=service_name,\n",
    "  deployment_config = myaci_config,\n",
    "  models = [mymodel],\n",
    "  image_config = myimage_config\n",
    "    )\n",
    "\n",
    "myservice.wait_for_deployment(show_output=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "help(Webservice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List images by ws\n",
    "\n",
    "for i in ContainerImage.list(workspace = ws):\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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for using the Web HTTP API \n",
    "print(myservice.scoring_uri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "#get the some sample data\n",
    "test_data_path = \"AdultCensusIncomeTest\"\n",
    "test = spark.read.parquet(test_data_path).limit(5)\n",
    "\n",
    "test_json = json.dumps(test.toJSON().collect())\n",
    "\n",
    "print(test_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using data defined above predict if income is >50K (1) or <=50K (0)\n",
    "myservice.run(input_data=test_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#comment to not delete the web service\n",
    "#myservice.delete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "pasha"
   },
   {
    "name": "wamartin"
   }
  ],
  "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": "3.7.0"
  },
  "name": "04.DeploytoACI",
  "notebookId": 3836944406456376
 },
 "nbformat": 4,
 "nbformat_minor": 1
 }
--- a/how-to-use-azureml/azure-databricks/06.AutoML_Databricks_local.ipynb
+++ b/how-to-use-azureml/azure-databricks/06.AutoML_Databricks_local.ipynb
@@ -0,0 +1,634 @@
 {
 "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 : Classification with Local Compute on Azure DataBricks\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",
    "In this notebook you will learn how to:\n",
    "1. Create Azure Machine Learning Workspace object and initialize your notebook directory to easily reload this object from a configuration file.\n",
    "2. Create an `Experiment` in an existing `Workspace`.\n",
    "3. Configure AutoML using `AutoMLConfig`.\n",
    "4. Train the model using AzureDataBricks.\n",
    "5. Explore the results.\n",
    "6. Test the best fitted model.\n",
    "\n",
    "Prerequisites:\n",
    "Before running this notebook, please follow the readme for installing necessary libraries to your cluster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Register Machine Learning Services Resource Provider\n",
    "Microsoft.MachineLearningServices only needs to be registed once in the subscription. To register it:\n",
    "Start the Azure portal.\n",
    "Select your All services and then Subscription.\n",
    "Select the subscription that you want to use.\n",
    "Click on Resource providers\n",
    "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": [
    "##PUBLISHONLY\n",
    "#subscription_id = \"<Your SubscriptionId>\"\n",
    "#resource_group = \"<Resource group - new or existing>\"\n",
    "#workspace_name = \"<workspace to be created>\"\n",
    "#workspace_region = \"<azureregion>\" #eg. eastus2, westcentralus, westeurope"
   ]
  },
  {
   "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": [
    "##TESTONLY\n",
    "# import auth creds from notebook parameters\n",
    "tenant = dbutils.widgets.get('tenant_id')\n",
    "username = dbutils.widgets.get('service_principal_id')\n",
    "password = dbutils.widgets.get('service_principal_password')\n",
    "\n",
    "auth = azureml.core.authentication.ServicePrincipalAuthentication(tenant, username, password)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##TESTONLY\n",
    "subscription_id  = dbutils.widgets.get('subscription_id')\n",
    "resource_group = dbutils.widgets.get('resource_group')\n",
    "workspace_name = dbutils.widgets.get('workspace_name')\n",
    "workspace_region = dbutils.widgets.get('workspace_region')"
   ]
  },
  {
   "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",
    "                      auth = auth,\n",
    "                      exist_ok=True)\n",
    "ws.get_details()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##PUBLISHONLY\n",
    "#from azureml.core import Workspace\n",
    "#import azureml.core\n",
    "#\n",
    "## Check core SDK version number\n",
    "#print(\"SDK version:\", azureml.core.VERSION)\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')\n",
    "##'''"
   ]
  },
  {
   "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": [
    "##TESTONLY\n",
    "from azureml.core import Workspace\n",
    "\n",
    "ws = Workspace(workspace_name = workspace_name,\n",
    "               subscription_id = subscription_id,\n",
    "               resource_group = resource_group,\n",
    "               auth = auth)\n",
    "\n",
    "# Persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
    "ws.write_config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##PUBLISHONLY\n",
    "#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": [
    "## Create a Folder to Host 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": [
    "## 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",
    "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",
    "\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": [
    "##TESTONLY\n",
    "ws = Workspace.from_config(auth = auth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##PUBLISHONLY\n",
    "#ws = Workspace.from_config(auth = auth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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 Using DataPrep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.dataprep as dprep\n",
    "# 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_train = 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_train = 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",
    "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_train.skip(1).head(5)"
   ]
  },
  {
   "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>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\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",
    "|**spark_context**|Spark Context object. for Databricks, use spark_context=sc|\n",
    "|**max_cuncurrent_iterations**|Maximum number of iterations to execute in parallel. This should be less than the number of cores on the ADB..|\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.|\n",
    "|**concurrent_iterations**|number of concurrent runs <= total cores in all worker nodes in your Databricks cluster|\n",
    "|**exit_score**|Target score for experiment. It is associated with the metric. eg. exit_score=0.995 will exit experiment after that|"
   ]
  },
  {
   "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 = 10,\n",
    "                             iterations = 30,\n",
    "                             n_cross_validations = 10,\n",
    "                             max_concurrent_iterations = 8, #change it based on number of cores in worker nodes\n",
    "                             verbosity = logging.INFO,\n",
    "                             spark_context=sc, #databricks/spark related\n",
    "                             X = X_train, \n",
    "                             y = y_train,\n",
    "                             enable_cache=False,\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) # for higher runs please use show_output=False and use the below"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Portal URL for Monitoring Runs\n",
    "\n",
    "The following will provide a link to the web interface to explore individual run details and status. In the future we might support output displayed in the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(local_run.get_portal_url())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following will show the child runs and waits for the parent run to complete."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retrieve All Child Runs after the experiment is completed (in portal)\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 after the above run is complete \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 after the above run is complete based on the child run\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": [
    "### Test the Best Fitted Model\n",
    "\n",
    "#### Load Test Data - you can split the dataset beforehand & pass Train dataset to AutoML and use Test dataset to evaluate the best model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\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 digits and see how our model works. This is just an example to show you."
   ]
  },
  {
   "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",
    "    display(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "savitam"
   },
   {
    "name": "wamartin"
   }
  ],
  "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": "3.7.0"
  },
  "name": "auto-ml-classification-local-adb",
  "notebookId": 3836944406456411
 },
 "nbformat": 4,
 "nbformat_minor": 1
 }
--- a/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb
+++ b/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb
@@ -244,7 +244,7 @@
    "# Use the default configuration (can also provide parameters to customize)\n",
    "prov_config = AksCompute.provisioning_configuration()\n",
    "\n",
-    "aks_name = 'my-aks-test1' \n",
+    "aks_name = 'my-aks-test2' \n",
    "# Create the cluster\n",
    "aks_target = ComputeTarget.create(workspace = ws, \n",
    "                                  name = aks_name, \n",
@@ -278,9 +278,10 @@
    "%%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",
+    "attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
    "aks_target = ComputeTarget.attach(workspace = ws, \n",
    "                                  name = create_name, \n",
-    "                                  #esource_id=resource_id)\n",
+    "                                  attach_configuration=attach_config)\n",
    "## Wait for the operation to complete\n",
    "aks_target.wait_for_provisioning(True)```"
   ]
--- a/how-to-use-azureml/deployment/enable-app-insights-in-production-service/sklearn_regression_model.pkl
+++ b/how-to-use-azureml/deployment/enable-app-insights-in-production-service/sklearn_regression_model.pkl
--- a/how-to-use-azureml/deployment/enable-data-collection-for-models-in-aks/enable-data-collection-for-models-in-aks.ipynb
+++ b/how-to-use-azureml/deployment/enable-data-collection-for-models-in-aks/enable-data-collection-for-models-in-aks.ipynb
@@ -285,9 +285,10 @@
    "    %%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",
+    "    attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
    "    aks_target = ComputeTarget.attach(workspace = ws, \n",
    "                                      name = create_name, \n",
-    "                                      resource_id=resource_id)\n",
+    "                                      attach_configuration=attach_config)\n",
    "    ## Wait for the operation to complete\n",
    "    aks_target.wait_for_provisioning(True)```"
   ]
--- a/how-to-use-azureml/deployment/enable-data-collection-for-models-in-aks/sklearn_regression_model.pkl
+++ b/how-to-use-azureml/deployment/enable-data-collection-for-models-in-aks/sklearn_regression_model.pkl
--- a/how-to-use-azureml/deployment/onnx/README.md
+++ b/how-to-use-azureml/deployment/onnx/README.md
--- a/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb
+++ b/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb
--- a/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb
+++ b/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb
--- a/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb
+++ b/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb
--- a/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb
+++ b/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb
--- a/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb
+++ b/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb
@@ -224,7 +224,8 @@
    "\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",
+    "attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
    "aks_target = ComputeTarget.attach(workspace=ws, name=create_name, attach_configuration=attach_config)\n",
    "# Wait for the operation to complete\n",
    "aks_target.wait_for_completion(True)\n",
    "'''"
--- a/01.getting-started/12.enable-data-collection-for-models-in-aks/sklearn_regression_model.pkl
+++ b/01.getting-started/12.enable-data-collection-for-models-in-aks/sklearn_regression_model.pkl
--- a/how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb
+++ b/how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb
--- a/how-to-use-azureml/deployment/register-model-create-image-deploy-service/sklearn_regression_model.pkl
+++ b/how-to-use-azureml/deployment/register-model-create-image-deploy-service/sklearn_regression_model.pkl
--- a/how-to-use-azureml/machine-learning-pipelines/00.pipeline-setup.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/00.pipeline-setup.ipynb
--- a/how-to-use-azureml/machine-learning-pipelines/README.md
+++ b/how-to-use-azureml/machine-learning-pipelines/README.md
@@ -36,13 +36,13 @@ 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)
+1. aml-pipelines-getting-started.ipynb
-2. [aml-pipelines-with-data-dependency-steps.ipynb](aml-pipelines-with-data-dependency-steps.ipynb)
+2. 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)
+3. aml-pipelines-publish-and-run-using-rest-endpoint.ipynb
-4. [aml-pipelines-data-transfer.ipynb](aml-pipelines-data-transfer.ipynb)
+4. aml-pipelines-data-transfer.ipynb
-5. [aml-pipelines-use-databricks-as-compute-target.ipynb](aml-pipelines-use-databricks-as-compute-target.ipynb)
+5. 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)
+6. 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-batch-scoring.ipynb
-  - [pipeline-style-transfer.ipynb](pipeline-style-transfer.ipynb)
+  - pipeline-style-transfer.ipynb
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/20news.pkl
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/20news.pkl
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb
@@ -98,18 +98,15 @@
   "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",
    "workspace = ws.name\n",
    "datastore_name='MyAdlsDatastore'\n",
    "subscription_id=os.getenv(\"ADL_SUBSCRIPTION_62\" \"<my-subscription-id>\"), # subscription id of ADLS account\n",
    "resource_group=os.getenv(\"ADL_RESOURCE_GROUP_62\" \"<my-resource-group>\"), # resource group of ADLS account\n",
    "store_name=os.getenv(\"ADL_STORENAME_62\", \"<my-datastore-name>\"), # ADLS account name\n",
    "tenant_id=os.getenv(\"ADL_TENANT_62\", \"<my-tenant-id>\") # tenant id of service principal\n",
    "client_id=os.getenv(\"ADL_CLIENTID_62\", \"<my-client-id>\") # client id of service principal\n",
    "client_secret=os.getenv(\"ADL_CLIENT_SECRET_62\", \"<my-client-secret>\") # the secret of service principal\n",
    "\n",
    "try:\n",
    "    adls_datastore = Datastore.get(ws, datastore_name)\n",
@@ -126,13 +123,12 @@
    "        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",
+    "\n",
-    "# account_name = \"<my-blob-account-name>\"\n",
+    "blob_datastore_name='MyBlobDatastore'\n",
-    "# container_name = \"<my-blob-container-name>\"\n",
+    "account_name=os.getenv(\"BLOB_ACCOUNTNAME_62\", \"<my-account-name>\") # Storage account name\n",
-    "# account_key = \"<my-blob-account-key>\"\n",
+    "container_name=os.getenv(\"BLOB_CONTAINER_62\", \"<my-container-name>\") # Name of Azure blob container\n",
    "account_key=os.getenv(\"BLOB_ACCOUNT_KEY_62\", \"<my-account-key>\") # Storage account key\n",
    "\n",
    "try:\n",
    "    blob_datastore = Datastore.get(ws, blob_datastore_name)\n",
@@ -314,9 +310,9 @@
   }
  ],
  "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.6",
   "language": "python",
-   "name": "python3"
+   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb
@@ -16,7 +16,7 @@
    "\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",
+    "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. "
@@ -34,45 +34,9 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "## Installing Packages\n",
+    "### Azure Machine Learning Imports\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."
+    "In this first code cell, we import key Azure Machine Learning modules that we will use below. "
   ]
  },
  {
   "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"
   ]
  },
  {
@@ -96,7 +60,9 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "### Pipeline SDK-specific imports"
+    "### Pipeline-specific SDK imports\n",
    "\n",
    "Here, we import key pipeline modules, whose use will be illustrated in the examples below."
   ]
  },
  {
@@ -609,9 +575,9 @@
   }
  ],
  "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.6",
   "language": "python",
-   "name": "python3"
+   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb
@@ -218,7 +218,8 @@
   "outputs": [],
   "source": [
    "# We will use this later in publishing pipeline\n",
-    "pipeline_param = PipelineParameter(name=\"pipeline_arg\", default_value=10)"
+    "pipeline_param = PipelineParameter(name=\"pipeline_arg\", default_value=10)\n",
    "print(\"pipeline parameter created\")"
   ]
  },
  {
@@ -336,9 +337,9 @@
   }
  ],
  "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.6",
   "language": "python",
-   "name": "python3"
+   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb
@@ -91,18 +91,15 @@
   "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",
    "workspace = ws.name\n",
    "datastore_name='MyAdlsDatastore'\n",
    "subscription_id=os.getenv(\"ADL_SUBSCRIPTION_62\" \"<my-subscription-id>\"), # subscription id of ADLS account\n",
    "resource_group=os.getenv(\"ADL_RESOURCE_GROUP_62\" \"<my-resource-group>\"), # resource group of ADLS account\n",
    "store_name=os.getenv(\"ADL_STORENAME_62\", \"<my-datastore-name>\"), # ADLS account name\n",
    "tenant_id=os.getenv(\"ADL_TENANT_62\", \"<my-tenant-id>\") # tenant id of service principal\n",
    "client_id=os.getenv(\"ADL_CLIENTID_62\", \"<my-client-id>\") # client id of service principal\n",
    "client_secret=os.getenv(\"ADL_CLIENT_62_SECRET\", \"<my-client-secret>\") # the secret of service principal\n",
    "\n",
    "try:\n",
    "    adls_datastore = Datastore.get(ws, datastore_name)\n",
@@ -135,7 +132,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "datastorename = \"TestAdlsDatastore\"\n",
+    "datastorename = \"MyAdlsDatastore\"\n",
    "\n",
    "adls_datastore = Datastore(workspace=ws, name=datastorename)\n",
    "script_input = DataReference(\n",
@@ -228,7 +225,32 @@
    "- **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)*"
+    "- **hash_paths:** list of paths to hash to detect a change (script file is always hashed) *(optional)*\n",
    "\n",
    "### Remarks\n",
    "\n",
    "You can use `@@name@@` syntax in your script to refer to inputs, outputs, resources, and params.\n",
    "\n",
    "* if `name` is the name of an input or output port binding, any occurences of `@@name@@` in the script\n",
    "are replaced with actual data path of corresponding port binding.\n",
    "* if `name` is the name of a resource input port binding, any occurences of `@@name@@` in the script\n",
    "are replaced with local path of resource after it's downloaded to script directory on a worker node.\n",
    "* if `name` matches any key in `params` dict, any occurences of `@@name@@` will be replaced with\n",
    "corresponding value in dict.\n",
    "\n",
    "#### Sample script\n",
    "\n",
    "```\n",
    "@resourcereader =\n",
    "    EXTRACT query string\n",
    "    FROM \"@@script_input@@\"\n",
    "    USING Extractors.Csv();\n",
    "\n",
    "\n",
    "OUTPUT @resourcereader\n",
    "TO \"@@script_output@@\"\n",
    "USING Outputters.Csv();\n",
    "```"
   ]
  },
  {
@@ -326,9 +348,9 @@
   }
  ],
  "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.6",
   "language": "python",
-   "name": "python3"
+   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb
@@ -137,24 +137,28 @@
   "outputs": [],
   "source": [
    "# Replace with your account info before running.\n",
-    "\n",
+    " \n",
-    "# db_compute_name = \"<my-databricks_compute_name>\"\n",
+    "db_compute_name=os.getenv(\"DATABRICKS_COMPUTE_NAME\", \"<my-databricks-compute-name>\") # Databricks compute name\n",
-    "# aml_resource_group = \"<my-aml-resource-group>\"\n",
+    "db_resource_group=os.getenv(\"DATABRICKS_RESOURCE_GROUP\", \"<my-db-resource-group>\") # Databricks resource group\n",
-    "# db_workspace_name = \"<my-databricks_workspace_name>\"\n",
+    "db_workspace_name=os.getenv(\"DATABRICKS_WORKSPACE_NAME\", \"<my-db-workspace-name>\") # Databricks workspace name\n",
-    "# access_token = \"<my-databricks_access_token>\"\n",
+    "db_access_token=os.getenv(\"DATABRICKS_ACCESS_TOKEN\", \"<my-access-token>\") # Databricks access token\n",
-    "\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('Compute not found, will use below parameters to attach new one')\n",
-    "    print('databricks_compute_name {}'.format(db_compute_name))\n",
+    "    print('db_compute_name {}'.format(db_compute_name))\n",
-    "    print('databricks_resource_id {}'.format(db_workspace_name))\n",
+    "    print('db_resource_group {}'.format(db_resource_group))\n",
-    "    print('databricks_access_token {}'.format(access_token))\n",
+    "    print('db_workspace_name {}'.format(db_workspace_name))\n",
-    "\n",
+    "    print('db_access_token {}'.format(db_access_token))\n",
-    "    config = DatabricksCompute.attach_configuration(aml_resource_group, db_workspace_name, access_token)\n",
+    " \n",
-    "    ComputeTarget.attach(ws, db_compute_name, config)\n",
+    "    config = DatabricksCompute.attach_configuration(\n",
-    "    databricks_compute.wait_for_completion(True)"
+    "        resource_group = db_resource_group,\n",
    "        workspace_name = db_workspace_name,\n",
    "        access_token= db_access_token)\n",
    "    databricks_compute=ComputeTarget.attach(ws, db_compute_name, config)\n",
    "    databricks_compute.wait_for_completion(True)\n"
   ]
  },
  {
@@ -342,7 +346,7 @@
   "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\"`:"
+    "Create a notebook in the Azure Databricks workspace, and provide the path to that notebook as the value associated with the environment variable \"DATABRICKS_NOTEBOOK_PATH\". This will then set the variable notebook_path when you run the code cell below:"
   ]
  },
  {
@@ -351,7 +355,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "# notebook_path = \"/Users/you@company.com/demo_notebook\"\n",
+    "notebook_path=os.getenv(\"DATABRICKS_NOTEBOOK_PATH\", \"<my-databricks-notebook-path>\") # Databricks notebook path\n",
    "\n",
    "dbNbStep = DatabricksStep(\n",
    "    name=\"DBNotebookInWS\",\n",
@@ -629,9 +633,9 @@
   }
  ],
  "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.6",
   "language": "python",
-   "name": "python3"
+   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb
@@ -241,7 +241,7 @@
   "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:"
+    "Machine learning pipelines can have many steps and these steps could use or reuse datasources and intermediate data. Here's how we construct such a pipeline:"
   ]
  },
  {
@@ -387,9 +387,9 @@
   }
  ],
  "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.6",
   "language": "python",
-   "name": "python3"
+   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/compare.py
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/compare.py
@@ -10,12 +10,14 @@ 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")
 parser.add_argument("--pipeline_param", type=int, help="pipeline parameter")
 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)
 print("Argument 4: %s" % args.pipeline_param)
 if not (args.output_compare is None):
    os.makedirs(args.output_compare, exist_ok=True)
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/extract.py
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/extract.py
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/train-db-dbfs.py
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/train-db-dbfs.py
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/train-db-local.py
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/train-db-local.py
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/train.py
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/train.py
--- a/how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.ipynb
@@ -551,9 +551,9 @@
   }
  ],
  "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.6",
   "language": "python",
-   "name": "python3"
+   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
--- a/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/neural_style.py
+++ b/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/neural_style.py
@@ -0,0 +1,187 @@
 # Copyright (c) Microsoft. All rights reserved.
 # Licensed under the MIT license.
 # Original source: https://github.com/pytorch/examples/blob/master/fast_neural_style/neural_style/neural_style.py
 import argparse
 import os
 import sys
 import re
 from PIL import Image
 import torch
 from torchvision import transforms
 def load_image(filename, size=None, scale=None):
    img = Image.open(filename)
    if size is not None:
        img = img.resize((size, size), Image.ANTIALIAS)
    elif scale is not None:
        img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), Image.ANTIALIAS)
    return img
 def save_image(filename, data):
    img = data.clone().clamp(0, 255).numpy()
    img = img.transpose(1, 2, 0).astype("uint8")
    img = Image.fromarray(img)
    img.save(filename)
 class TransformerNet(torch.nn.Module):
    def __init__(self):
        super(TransformerNet, self).__init__()
        # Initial convolution layers
        self.conv1 = ConvLayer(3, 32, kernel_size=9, stride=1)
        self.in1 = torch.nn.InstanceNorm2d(32, affine=True)
        self.conv2 = ConvLayer(32, 64, kernel_size=3, stride=2)
        self.in2 = torch.nn.InstanceNorm2d(64, affine=True)
        self.conv3 = ConvLayer(64, 128, kernel_size=3, stride=2)
        self.in3 = torch.nn.InstanceNorm2d(128, affine=True)
        # Residual layers
        self.res1 = ResidualBlock(128)
        self.res2 = ResidualBlock(128)
        self.res3 = ResidualBlock(128)
        self.res4 = ResidualBlock(128)
        self.res5 = ResidualBlock(128)
        # Upsampling Layers
        self.deconv1 = UpsampleConvLayer(128, 64, kernel_size=3, stride=1, upsample=2)
        self.in4 = torch.nn.InstanceNorm2d(64, affine=True)
        self.deconv2 = UpsampleConvLayer(64, 32, kernel_size=3, stride=1, upsample=2)
        self.in5 = torch.nn.InstanceNorm2d(32, affine=True)
        self.deconv3 = ConvLayer(32, 3, kernel_size=9, stride=1)
        # Non-linearities
        self.relu = torch.nn.ReLU()
    def forward(self, X):
        y = self.relu(self.in1(self.conv1(X)))
        y = self.relu(self.in2(self.conv2(y)))
        y = self.relu(self.in3(self.conv3(y)))
        y = self.res1(y)
        y = self.res2(y)
        y = self.res3(y)
        y = self.res4(y)
        y = self.res5(y)
        y = self.relu(self.in4(self.deconv1(y)))
        y = self.relu(self.in5(self.deconv2(y)))
        y = self.deconv3(y)
        return y
 class ConvLayer(torch.nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride):
        super(ConvLayer, self).__init__()
        reflection_padding = kernel_size // 2
        self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
        self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride)
    def forward(self, x):
        out = self.reflection_pad(x)
        out = self.conv2d(out)
        return out
 class ResidualBlock(torch.nn.Module):
    """ResidualBlock
    introduced in: https://arxiv.org/abs/1512.03385
    recommended architecture: http://torch.ch/blog/2016/02/04/resnets.html
    """
    def __init__(self, channels):
        super(ResidualBlock, self).__init__()
        self.conv1 = ConvLayer(channels, channels, kernel_size=3, stride=1)
        self.in1 = torch.nn.InstanceNorm2d(channels, affine=True)
        self.conv2 = ConvLayer(channels, channels, kernel_size=3, stride=1)
        self.in2 = torch.nn.InstanceNorm2d(channels, affine=True)
        self.relu = torch.nn.ReLU()
    def forward(self, x):
        residual = x
        out = self.relu(self.in1(self.conv1(x)))
        out = self.in2(self.conv2(out))
        out = out + residual
        return out
 class UpsampleConvLayer(torch.nn.Module):
    """UpsampleConvLayer
    Upsamples the input and then does a convolution. This method gives better results
    compared to ConvTranspose2d.
    ref: http://distill.pub/2016/deconv-checkerboard/
    """
    def __init__(self, in_channels, out_channels, kernel_size, stride, upsample=None):
        super(UpsampleConvLayer, self).__init__()
        self.upsample = upsample
        if upsample:
            self.upsample_layer = torch.nn.Upsample(mode='nearest', scale_factor=upsample)
        reflection_padding = kernel_size // 2
        self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
        self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride)
    def forward(self, x):
        x_in = x
        if self.upsample:
            x_in = self.upsample_layer(x_in)
        out = self.reflection_pad(x_in)
        out = self.conv2d(out)
        return out
 def stylize(args):
    device = torch.device("cuda" if args.cuda else "cpu")
    with torch.no_grad():
        style_model = TransformerNet()
        state_dict = torch.load(os.path.join(args.model_dir, args.style+".pth"))
        # remove saved deprecated running_* keys in InstanceNorm from the checkpoint
        for k in list(state_dict.keys()):
            if re.search(r'in\d+\.running_(mean|var)$', k):
                del state_dict[k]
        style_model.load_state_dict(state_dict)
        style_model.to(device)
        filenames = os.listdir(args.content_dir)
        for filename in filenames:
            print("Processing {}".format(filename))
            full_path = os.path.join(args.content_dir, filename)
            content_image = load_image(full_path, scale=args.content_scale)
            content_transform = transforms.Compose([
                transforms.ToTensor(),
                transforms.Lambda(lambda x: x.mul(255))
            ])
            content_image = content_transform(content_image)
            content_image = content_image.unsqueeze(0).to(device)
            output = style_model(content_image).cpu()
            output_path = os.path.join(args.output_dir, filename)
            save_image(output_path, output[0])
 def main():
    arg_parser = argparse.ArgumentParser(description="parser for fast-neural-style")
    arg_parser.add_argument("--content-scale", type=float, default=None,
                                 help="factor for scaling down the content image")
    arg_parser.add_argument("--model-dir", type=str, required=True,
                                 help="saved model to be used for stylizing the image.")
    arg_parser.add_argument("--cuda", type=int, required=True,
                                 help="set it to 1 for running on GPU, 0 for CPU")
    arg_parser.add_argument("--style", type=str,
                                 help="style name")
    arg_parser.add_argument("--content-dir", type=str, required=True,
            help="directory holding the images")
    arg_parser.add_argument("--output-dir", type=str, required=True,
            help="directory holding the output images")
    args = arg_parser.parse_args()
    if args.cuda and not torch.cuda.is_available():
        print("ERROR: cuda is not available, try running on CPU")
        sys.exit(1)
    os.makedirs(args.output_dir, exist_ok=True)
    stylize(args)
 if __name__ == "__main__":
    main()
--- a/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer.ipynb
@@ -588,9 +588,9 @@
   }
  ],
  "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.6",
   "language": "python",
-   "name": "python3"
+   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
rastala	ce59d79133	Update 12/2 2	2018-12-02 19:21:15 -05:00
rastala	13a5d0baac	Update 12/2	2018-12-02 14:46:52 -05:00
rastala	74309f91f7	Update 12-1-19	2018-12-01 20:37:45 -05:00
Roope Astala	96523ec751	Notebook folder restructuring	2018-11-29 11:32:16 -05:00
		`@@ -1 +0,0 @@`
			{"cells":[{"cell_type":"markdown","source":["Azure ML & Azure Databricks notebooks by Parashar Shah.\n\nCopyright (c) Microsoft Corporation. All rights reserved.\n\nLicensed under the MIT License."],"metadata":{}},{"cell_type":"markdown","source":["Please ensure you have run this notebook before proceeding."],"metadata":{}},{"cell_type":"markdown","source":["Now we support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package (during private preview). You can select the option to attach the library to all clusters or just one cluster.\n\nProvide this full string to install the SDK:\n\nazureml-sdk[databricks]"],"metadata":{}},{"cell_type":"code","source":["import azureml.core\n\n# Check core SDK version number - based on build number of preview/master.\nprint(\"SDK version:\", azureml.core.VERSION)"],"metadata":{},"outputs":[],"execution_count":4},{"cell_type":"code","source":["subscription_id = \"<your-subscription-id>\"\nresource_group = \"<your-existing-resource-group>\"\nworkspace_name = \"<a-new-or-existing-workspace; it is unrelated to Databricks workspace>\"\nworkspace_region = \"<your-resource group-region>\""],"metadata":{},"outputs":[],"execution_count":5},{"cell_type":"code","source":["# import the Workspace class and check the azureml SDK version\n# exist_ok checks if workspace exists or not.\n\nfrom azureml.core import Workspace\n\nws = Workspace.create(name = workspace_name,\n subscription_id = subscription_id,\n resource_group = resource_group, \n location = workspace_region,\n exist_ok=True)\n\nws.get_details()"],"metadata":{},"outputs":[],"execution_count":6},{"cell_type":"code","source":["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.\nws.write_config()"],"metadata":{},"outputs":[],"execution_count":7},{"cell_type":"code","source":["%sh\ncat /databricks/driver/aml_config/config.json"],"metadata":{},"outputs":[],"execution_count":8},{"cell_type":"code","source":["# import the Workspace class and check the azureml SDK version\nfrom azureml.core import Workspace\n\nws = Workspace.from_config()\nprint('Workspace name: ' + ws.name, \n 'Azure region: ' + ws.location, \n 'Subscription id: ' + ws.subscription_id, \n 'Resource group: ' + ws.resource_group, sep = '\\n')"],"metadata":{},"outputs":[],"execution_count":9},{"cell_type":"code","source":["dbutils.notebook.exit(\"success\")"],"metadata":{},"outputs":[],"execution_count":10},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":11}],"metadata":{"name":"01.Installation_and_Configuration","notebookId":3874566296719377},"nbformat":4,"nbformat_minor":0}