Note about setting location.

@sdgilley Is this update OK? We want to show that location can be set for a new public preview functionality.

.amlignore

@@ -0,0 +1,7 @@
+.ipynb_checkpoints
+azureml-logs
+.azureml
+.git
+outputs
+azureml-setup
+docs

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "python.pythonPath": "C:\\Users\\sgilley\\.azureml\\envs\\jan3\\python.exe"
+}

00.configuration.ipynb

@@ -0,0 +1,236 @@
+{
+ "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": [
+    "from azureml.core import Workspace\n",
+    "\n",
+    "subscription_id ='<subscription-id>'\n",
+    "resource_group ='<resource-group>'\n",
+    "workspace_name = '<workspace-name>'\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. BatchAI cluster size) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
+   ]
+  },
+  {
+   "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": [
+    "workspace_region = \"eastus2\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "subscription_id = os.environ.get(\"SUBSCRIPTION_ID\", subscription_id)\n",
+    "resource_group = os.environ.get(\"RESOURCE_GROUP\", resource_group)\n",
+    "workspace_name = os.environ.get(\"WORKSPACE_NAME\", workspace_name)\n",
+    "workspace_region = os.environ.get(\"WORKSPACE_REGION\", workspace_region)"
+   ]
+  },
+  {
+   "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": [
+    "## Success!\n",
+    "Great, you are ready to move on to the rest of the sample notebooks."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

01.getting-started/01.train-within-notebook/01.train-within-notebook.ipynb

@@ -0,0 +1,816 @@
+{
+  "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": [
+        "# 01. Train in the Notebook & Deploy Model to ACI\n",
+        "\n",
+        "* Load workspace\n",
+        "* Train a simple regression model directly in the Notebook python kernel\n",
+        "* Record run history\n",
+        "* Find the best model in run history and download it.\n",
+        "* Deploy the model as an Azure Container Instance (ACI)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prerequisites\n",
+        "1. Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't. \n",
+        "\n",
+        "2. Install following pre-requisite libraries to your conda environment and restart notebook.\n",
+        "```shell\n",
+        "(myenv) $ conda install -y matplotlib tqdm scikit-learn\n",
+        "```\n",
+        "\n",
+        "3. Check that ACI is registered for your Azure Subscription.  "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!az provider show -n Microsoft.ContainerInstance -o table"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If ACI is not registered, run following command to register it. Note that you have to be a subscription owner, or this command will fail."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!az provider register -n Microsoft.ContainerInstance"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Validate Azure ML SDK installation and get version number for debugging purposes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "install"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment, Run, Workspace\n",
+        "import azureml.core\n",
+        "\n",
+        "# Check core SDK version number\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize Workspace\n",
+        "\n",
+        "Initialize a workspace object from persisted configuration."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "create workspace"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "print('Workspace name: ' + ws.name, \n",
+        "      'Azure region: ' + ws.location, \n",
+        "      'Subscription id: ' + ws.subscription_id, \n",
+        "      'Resource group: ' + ws.resource_group, sep='\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Set experiment name\n",
+        "Choose a name for experiment."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "experiment_name = 'train-in-notebook'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Start a training run in local Notebook"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# load diabetes dataset, a well-known 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",
+        "from sklearn.externals import joblib\n",
+        "\n",
+        "X, y = load_diabetes(return_X_y = True)\n",
+        "columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+        "data = {\n",
+        "    \"train\":{\"X\": X_train, \"y\": y_train},        \n",
+        "    \"test\":{\"X\": X_test, \"y\": y_test}\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Train a simple Ridge model\n",
+        "Train a very simple Ridge regression model in scikit-learn, and save it as a pickle file."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "reg = Ridge(alpha = 0.03)\n",
+        "reg.fit(X=data['train']['X'], y=data['train']['y'])\n",
+        "preds = reg.predict(data['test']['X'])\n",
+        "print('Mean Squared Error is', mean_squared_error(data['test']['y'], preds))\n",
+        "joblib.dump(value=reg, filename='model.pkl');"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Add experiment tracking\n",
+        "Now, let's add Azure ML experiment logging, and upload persisted model into run record as well."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "local run",
+          "outputs upload"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "experiment = Experiment(workspace=ws, name=experiment_name)\n",
+        "run = experiment.start_logging()\n",
+        "\n",
+        "run.tag(\"Description\",\"My first run!\")\n",
+        "run.log('alpha', 0.03)\n",
+        "reg = Ridge(alpha=0.03)\n",
+        "reg.fit(data['train']['X'], data['train']['y'])\n",
+        "preds = reg.predict(data['test']['X'])\n",
+        "run.log('mse', mean_squared_error(data['test']['y'], preds))\n",
+        "joblib.dump(value=reg, filename='model.pkl')\n",
+        "run.upload_file(name='outputs/model.pkl', path_or_stream='./model.pkl')\n",
+        "\n",
+        "run.complete()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can browse to the recorded run. Please make sure you use Chrome to navigate the run history page."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Simple parameter sweep\n",
+        "Sweep over alpha values of a sklearn ridge model, and capture metrics and trained model in the Azure ML experiment."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import os\n",
+        "from tqdm import tqdm\n",
+        "\n",
+        "model_name = \"model.pkl\"\n",
+        "\n",
+        "# list of numbers from 0 to 1.0 with a 0.05 interval\n",
+        "alphas = np.arange(0.0, 1.0, 0.05)\n",
+        "\n",
+        "# try a bunch of alpha values in a Linear Regression (Ridge) model\n",
+        "for alpha in tqdm(alphas):\n",
+        "    # create a bunch of runs, each train a model with a different alpha value\n",
+        "    with experiment.start_logging() as run:\n",
+        "        # Use Ridge algorithm to build a regression model\n",
+        "        reg = Ridge(alpha=alpha)\n",
+        "        reg.fit(X=data[\"train\"][\"X\"], y=data[\"train\"][\"y\"])\n",
+        "        preds = reg.predict(X=data[\"test\"][\"X\"])\n",
+        "        mse = mean_squared_error(y_true=data[\"test\"][\"y\"], y_pred=preds)\n",
+        "\n",
+        "        # log alpha, mean_squared_error and feature names in run history\n",
+        "        run.log(name=\"alpha\", value=alpha)\n",
+        "        run.log(name=\"mse\", value=mse)\n",
+        "        run.log_list(name=\"columns\", value=columns)\n",
+        "\n",
+        "        with open(model_name, \"wb\") as file:\n",
+        "            joblib.dump(value=reg, filename=file)\n",
+        "        \n",
+        "        # upload the serialized model into run history record\n",
+        "        run.upload_file(name=\"outputs/\" + model_name, path_or_stream=model_name)\n",
+        "\n",
+        "        # now delete the serialized model from local folder since it is already uploaded to run history \n",
+        "        os.remove(path=model_name)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# now let's take a look at the experiment in Azure portal.\n",
+        "experiment"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Select best model from the experiment\n",
+        "Load all experiment run metrics recursively from the experiment into a dictionary object."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "runs = {}\n",
+        "run_metrics = {}\n",
+        "\n",
+        "for r in tqdm(experiment.get_runs()):\n",
+        "    metrics = r.get_metrics()\n",
+        "    if 'mse' in metrics.keys():\n",
+        "        runs[r.id] = r\n",
+        "        run_metrics[r.id] = metrics"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now find the run with the lowest Mean Squared Error value"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "best_run_id = min(run_metrics, key = lambda k: run_metrics[k]['mse'])\n",
+        "best_run = runs[best_run_id]\n",
+        "print('Best run is:', best_run_id)\n",
+        "print('Metrics:', run_metrics[best_run_id])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can add tags to your runs to make them easier to catalog"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "query history"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "best_run.tag(key=\"Description\", value=\"The best one\")\n",
+        "best_run.get_tags()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Plot MSE over alpha\n",
+        "\n",
+        "Let's observe the best model visually by plotting the MSE values over alpha values:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%matplotlib inline\n",
+        "import matplotlib\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "best_alpha = run_metrics[best_run_id]['alpha']\n",
+        "min_mse = run_metrics[best_run_id]['mse']\n",
+        "\n",
+        "alpha_mse = np.array([(run_metrics[k]['alpha'], run_metrics[k]['mse']) for k in run_metrics.keys()])\n",
+        "sorted_alpha_mse = alpha_mse[alpha_mse[:,0].argsort()]\n",
+        "\n",
+        "plt.plot(sorted_alpha_mse[:,0], sorted_alpha_mse[:,1], 'r--')\n",
+        "plt.plot(sorted_alpha_mse[:,0], sorted_alpha_mse[:,1], 'bo')\n",
+        "\n",
+        "plt.xlabel('alpha', fontsize = 14)\n",
+        "plt.ylabel('mean squared error', fontsize = 14)\n",
+        "plt.title('MSE over alpha', fontsize = 16)\n",
+        "\n",
+        "# plot arrow\n",
+        "plt.arrow(x = best_alpha, y = min_mse + 39, dx = 0, dy = -26, ls = '-', lw = 0.4,\n",
+        "          width = 0, head_width = .03, head_length = 8)\n",
+        "\n",
+        "# plot \"best run\" text\n",
+        "plt.text(x = best_alpha - 0.08, y = min_mse + 50, s = 'Best Run', fontsize = 14)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Register the best model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Find the model file saved in the run record of best run."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "query history"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "for f in best_run.get_file_names():\n",
+        "    print(f)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we can register this model in the model registry of the workspace"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "register model from history"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "model = best_run.register_model(model_name='best_model', model_path='outputs/model.pkl')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Verify that the model has been registered properly. If you have done this several times you'd see the version number auto-increases each time."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "register model from history"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core.model import Model\n",
+        "models = Model.list(workspace=ws, name='best_model')\n",
+        "for m in models:\n",
+        "    print(m.name, m.version)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can also download the registered model. Afterwards, you should see a `model.pkl` file in the current directory. You can then use it for local testing if you'd like."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "download file"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "# remove the model file if it is already on disk\n",
+        "if os.path.isfile('model.pkl'): \n",
+        "    os.remove('model.pkl')\n",
+        "# download the model\n",
+        "model.download(target_dir=\"./\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Scoring script\n",
+        "\n",
+        "Now we are ready to build a Docker image and deploy the model in it as a web service. The first step is creating the scoring script. For convenience, we have created the scoring script for you. It is printed below as text, but you can also run `%pfile ./score.py` in a cell to show the file.\n",
+        "\n",
+        "Tbe scoring script consists of two functions: `init` that is used to load the model to memory when starting the container, and `run` that makes the prediction when web service is called. Please pay special attention to how the model is loaded in the `init()` function. When Docker image is built for this model, the actual model file is downloaded and placed on disk, and `get_model_path` function returns the local path where the model is placed."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "with open('./score.py', 'r') as scoring_script:\n",
+        "    print(scoring_script.read())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create environment dependency file\n",
+        "\n",
+        "We need a environment dependency file `myenv.yml` to specify which libraries are needed by the scoring script when building the Docker image for web service deployment. We can manually create this file, or we can use the `CondaDependencies` API to automatically create this file."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.conda_dependencies import CondaDependencies \n",
+        "\n",
+        "myenv = CondaDependencies()\n",
+        "myenv.add_conda_package(\"scikit-learn\")\n",
+        "print(myenv.serialize_to_string())\n",
+        "\n",
+        "with open(\"myenv.yml\",\"w\") as f:\n",
+        "    f.write(myenv.serialize_to_string())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Deploy web service into an Azure Container Instance\n",
+        "The deployment process takes the registered model and your scoring scrip, and builds a Docker image. It then deploys the Docker image into Azure Container Instance as a running container with an HTTP endpoint readying for scoring calls. Read more about [Azure Container Instance](https://azure.microsoft.com/en-us/services/container-instances/).\n",
+        "\n",
+        "Note ACI is great for quick and cost-effective dev/test deployment scenarios. For production workloads, please use [Azure Kubernentes Service (AKS)](https://azure.microsoft.com/en-us/services/kubernetes-service/) instead. Please follow in struction in [this notebook](11.production-deploy-to-aks.ipynb) to see how that can be done from Azure ML.\n",
+        " \n",
+        "** Note: ** The web service creation can take 6-7 minutes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "deploy service",
+          "aci"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import AciWebservice, Webservice\n",
+        "\n",
+        "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
+        "                                               memory_gb=1, \n",
+        "                                               tags={'sample name': 'AML 101'}, \n",
+        "                                               description='This is a great example.')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Note the below `WebService.deploy_from_model()` function takes a model object registered under the workspace. It then bakes the model file in the Docker image so it can be looked-up using the `Model.get_model_path()` function in `score.py`. \n",
+        "\n",
+        "If you have a local model file instead of a registered model object, you can also use the `WebService.deploy()` function which would register the model and then deploy."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "deploy service",
+          "aci"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core.image import ContainerImage\n",
+        "image_config = ContainerImage.image_configuration(execution_script=\"score.py\", \n",
+        "                                    runtime=\"python\", \n",
+        "                                    conda_file=\"myenv.yml\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "deploy service",
+          "aci"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "# this will take 5-10 minutes to finish\n",
+        "# you can also use \"az container list\" command to find the ACI being deployed\n",
+        "service = Webservice.deploy_from_model(name='my-aci-svc',\n",
+        "                                       deployment_config=aciconfig,\n",
+        "                                       models=[model],\n",
+        "                                       image_config=image_config,\n",
+        "                                       workspace=ws)\n",
+        "\n",
+        "service.wait_for_deployment(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n",
+        "## Test web service"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "deploy service",
+          "aci"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "print('web service is hosted in ACI:', service.scoring_uri)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Use the `run` API to call the web service with one row of data to get a prediction."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "deploy service",
+          "aci"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "import json\n",
+        "# score the first row from the test set.\n",
+        "test_samples = json.dumps({\"data\": X_test[0:1, :].tolist()})\n",
+        "service.run(input_data = test_samples)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Feed the entire test set and calculate the errors (residual values)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "deploy service",
+          "aci"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "# score the entire test set.\n",
+        "test_samples = json.dumps({'data': X_test.tolist()})\n",
+        "\n",
+        "result = json.loads(service.run(input_data = test_samples))['result']\n",
+        "residual = result - y_test"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can also send raw HTTP request to test the web service."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "deploy service",
+          "aci"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "import requests\n",
+        "import json\n",
+        "\n",
+        "# 2 rows of input data, each with 10 made-up numerical features\n",
+        "input_data = \"{\\\"data\\\": [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10], [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]]}\"\n",
+        "\n",
+        "headers = {'Content-Type':'application/json'}\n",
+        "\n",
+        "# for AKS deployment you'd need to the service key in the header as well\n",
+        "# api_key = service.get_key()\n",
+        "# headers = {'Content-Type':'application/json',  'Authorization':('Bearer '+ api_key)} \n",
+        "\n",
+        "resp = requests.post(service.scoring_uri, input_data, headers = headers)\n",
+        "print(resp.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Residual graph\n",
+        "Plot a residual value graph to chart the errors on the entire test set. Observe the nice bell curve."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios':[3, 1], 'wspace':0, 'hspace': 0})\n",
+        "f.suptitle('Residual Values', fontsize = 18)\n",
+        "\n",
+        "f.set_figheight(6)\n",
+        "f.set_figwidth(14)\n",
+        "\n",
+        "a0.plot(residual, 'bo', alpha=0.4);\n",
+        "a0.plot([0,90], [0,0], 'r', lw=2)\n",
+        "a0.set_ylabel('residue values', fontsize=14)\n",
+        "a0.set_xlabel('test data set', fontsize=14)\n",
+        "\n",
+        "a1.hist(residual, orientation='horizontal', color='blue', bins=10, histtype='step');\n",
+        "a1.hist(residual, orientation='horizontal', color='blue', alpha=0.2, bins=10);\n",
+        "a1.set_yticklabels([])\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Delete ACI to clean up"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Deleting ACI is super fast!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "deploy service",
+          "aci"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "service.delete()"
+      ]
+    },
+    {
+      "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": "3.6.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

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

@@ -0,0 +1,27 @@
+import pickle
+import json
+import numpy as np
+from sklearn.externals import joblib
+from sklearn.linear_model import Ridge
+from azureml.core.model import Model
+
+
+def init():
+    global model
+    # note here "best_model" is the name of the model registered under the workspace
+    # this call should return the path to the model.pkl file on the local disk.
+    model_path = Model.get_model_path(model_name='best_model')
+    # deserialize the model file back into a sklearn model
+    model = joblib.load(model_path)
+
+
+# note you can pass in multiple rows for scoring
+def run(raw_data):
+    try:
+        data = json.loads(raw_data)['data']
+        data = np.array(data)
+        result = model.predict(data)
+        return json.dumps({"result": result.tolist()})
+    except Exception as e:
+        result = str(e)
+        return json.dumps({"error": result})

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

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

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

@@ -6,14 +6,10 @@ 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
 import mylib
-# sklearn.externals.joblib is removed in 0.23
-try:
-    from sklearn.externals import joblib
-except ImportError:
-    import joblib
 
 os.makedirs('./outputs', exist_ok=True)
 

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

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

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

@@ -6,14 +6,9 @@ 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
-from sklearn import __version__ as sklearnver
-from packaging.version import Version
-if Version(sklearnver) < Version("0.23.0"):
-    from sklearn.externals import joblib
-else:
-    import joblib
 
 os.makedirs('./outputs', exist_ok=True)
 

01.getting-started/04.train-on-remote-vm/04.train-on-remote-vm.ipynb

@@ -9,13 +9,6 @@
         "Licensed under the MIT License."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.png)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -23,8 +16,8 @@
         "# 04. Train in a remote Linux VM\n",
         "* Create Workspace\n",
         "* Create `train.py` file\n",
-        "* Create and Attach a Remote VM (eg. DSVM) as compute resource\n",
-        "* Upload data files into default datastore\n",
+        "* Create (or attach) DSVM as compute resource.\n",
+        "* Upoad data files into default datastore\n",
         "* Configure & execute a run in a few different ways\n",
         "    - Use system-built conda\n",
         "    - Use existing Python environment\n",
@@ -37,7 +30,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) first if you haven't already to establish your connection to the AzureML Workspace."
+        "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
       ]
     },
     {
@@ -117,7 +110,7 @@
       "metadata": {},
       "source": [
         "## Upload data files into datastore\n",
-        "Every workspace comes with a default [datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data) (and you can register more) which is backed by the Azure blob storage account associated with the workspace. We can use it to transfer data from local to the cloud, and access it from the compute target."
+        "Every workspace comes with a default datastore (and you can register more) which is backed by the Azure blob storage account associated with the workspace. We can use it to transfer data from local to the cloud, and access it from the compute target."
       ]
     },
     {
@@ -126,7 +119,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# Get the default datastore\n",
+        "# get the default datastore\n",
         "ds = ws.get_default_datastore()\n",
         "print(ds.name, ds.datastore_type, ds.account_name, ds.container_name)"
       ]
@@ -168,36 +161,6 @@
         "ds.upload_files(['./features.npy', './labels.npy'], target_path='diabetes', overwrite=True)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create a Dataset for Files\n",
-        "A Dataset can reference single or multiple files in your datastores or public urls. The files can be of any format. Dataset provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. The data remains in its existing location, so no extra storage cost is incurred. [Learn More](https://aka.ms/azureml/howto/createdatasets)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# initialize file dataset \n",
-        "from azureml.core import Dataset\n",
-        "ds_paths = [(ds, 'diabetes/')]\n",
-        "dataset = Dataset.File.from_files(path = ds_paths)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# list the files referenced by the dataset\n",
-        "dataset.to_path()"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -225,17 +188,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Create and Attach a DSVM as a compute target\n",
+        "## Create Linux DSVM as a compute target\n",
         "\n",
-        "The DSVM can be created using the below single line command and then attached(like any VM) using the sample code below. Also note, that we only support Linux VMs for remote execution from AML and the commands below will spin a Linux VM only.\n",
-        "\n",
-        "```shell\n",
-        "# create a DSVM in your resource group\n",
-        "# note you need to be at least a contributor to the resource group in order to execute this command successfully\n",
-        "(myenv) $ az vm create --resource-group <resource_group_name> --name <some_vm_name> --image microsoft-dsvm:linux-data-science-vm-ubuntu:linuxdsvmubuntu:latest --admin-username <username> --admin-password <password> --generate-ssh-keys --authentication-type password\n",
-        "```\n",
-        "\n",
-        "**Note**: You can also use [this url](https://ms.portal.azure.com/#create/microsoft-dsvm.ubuntu-18041804) to create the VM using the Azure Portal\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 switch to a different port (such as 5022), you can specify the port number in the provisioning configuration object."
       ]
@@ -243,44 +198,49 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {
-        "tags": [
-          "sample-remotecompute-attach"
-        ]
-      },
+      "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import ComputeTarget, RemoteCompute\n",
+        "from azureml.core.compute import DsvmCompute\n",
         "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
-        "username = os.getenv('AZUREML_DSVM_USERNAME', default='<my_username>')\n",
-        "address = os.getenv('AZUREML_DSVM_ADDRESS', default='<ip_address_or_fqdn>')\n",
+        "compute_target_name = 'mydsvm'\n",
         "\n",
-        "compute_target_name = 'cpudsvm'\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",
         "try:\n",
-        "    attached_dsvm_compute = RemoteCompute(workspace=ws, name=compute_target_name)\n",
-        "    print('found existing:', attached_dsvm_compute.name)\n",
+        "    dsvm_compute = DsvmCompute(workspace=ws, name=compute_target_name)\n",
+        "    print('found existing:', dsvm_compute.name)\n",
         "except ComputeTargetException:\n",
-        "    attach_config = RemoteCompute.attach_configuration(address=address,\n",
+        "    print('creating new.')\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": [
+        "## Attach an existing Linux DSVM\n",
+        "You can also attach an existing Linux VM as a compute target. The default port is 22."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "'''\n",
+        "from azureml.core.compute import RemoteCompute \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",
+        "attached_dsvm_compute = RemoteCompute.attach(workspace=ws,\n",
+        "                                             name=\"attached_vm\",\n",
+        "                                             username='<usename>',\n",
+        "                                             address='<ip_adress_or_fqdn>',\n",
         "                                             ssh_port=22,\n",
-        "                                                       username=username,\n",
-        "                                                       private_key_file='./.ssh/id_rsa')\n",
-        "\n",
-        "\n",
-        "# Attaching a virtual machine using the public IP address of the VM is no longer supported.\n",
-        "# Instead, use resourceId of the VM.\n",
-        "# The resourceId of the VM can be constructed using the following string format:\n",
-        "# /subscriptions/<subscription_id>/resourceGroups/<resource_group>/providers/Microsoft.Compute/virtualMachines/<vm_name>.\n",
-        "# You can also use subscription_id, resource_group and vm_name without constructing resourceId.\n",
-        "    attach_config = RemoteCompute.attach_configuration(resource_id='<resource_id>',\n",
-        "                                                       ssh_port=22,\n",
-        "                                                       username='username',\n",
-        "                                                       private_key_file='./.ssh/id_rsa')\n",
-        "    \n",
-        "    attached_dsvm_compute = ComputeTarget.attach(ws, compute_target_name, attach_config)\n",
-        "    \n",
-        "    attached_dsvm_compute.wait_for_completion(show_output=True)"
+        "                                             password='<password>')\n",
+        "attached_dsvm_compute.wait_for_completion(show_output=True)\n",
+        "'''\n"
       ]
     },
     {
@@ -288,7 +248,26 @@
       "metadata": {},
       "source": [
         "## Configure & Run\n",
-        "\n",
+        "First let's create a `DataReferenceConfiguration` object to inform the system what data folder to download to the copmute target."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.runconfig import DataReferenceConfiguration\n",
+        "dr = DataReferenceConfiguration(datastore_name=ds.name, \n",
+        "                   path_on_datastore='diabetes', \n",
+        "                   mode='download', # download files from datastore to compute target\n",
+        "                   overwrite=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
         "Now we can try a few different ways to run the training script in the VM."
       ]
     },
@@ -306,13 +285,20 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core import Environment\n",
+        "from azureml.core.runconfig import RunConfiguration\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
         "\n",
-        "conda_env = Environment(\"conda-env\")\n",
-        "conda_env.python.conda_dependencies = CondaDependencies.create(pip_packages=['scikit-learn',\n",
-        "                                                                             'azureml-sdk',\n",
-        "                                                                             'azureml-dataset-runtime[pandas,fuse]'])"
+        "# create a new RunConfig object\n",
+        "conda_run_config = RunConfiguration(framework=\"python\")\n",
+        "\n",
+        "# Set compute target to the Linux DSVM\n",
+        "conda_run_config.target = dsvm_compute.name\n",
+        "\n",
+        "# set the data reference of the run configuration\n",
+        "conda_run_config.data_references = {ds.name: dr}\n",
+        "\n",
+        "# specify CondaDependencies obj\n",
+        "conda_run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])"
       ]
     },
     {
@@ -321,16 +307,16 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "from azureml.core import Run\n",
         "from azureml.core import ScriptRunConfig\n",
-        "from uuid import uuid4\n",
         "\n",
-        "script_arguments = ['--data-folder', dataset.as_named_input('diabetes').as_mount('/tmp/{}'.format(uuid4()))]\n",
         "src = ScriptRunConfig(source_directory=script_folder, \n",
         "                      script='train.py', \n",
-        "                      # pass the dataset as a parameter to the training script\n",
-        "                      arguments=script_arguments,\n",
-        "                      compute_target = attached_dsvm_compute,\n",
-        "                      environment = conda_env) "
+        "                      run_config=conda_run_config, \n",
+        "                      # pass the datastore reference as a parameter to the training script\n",
+        "                      arguments=['--data-folder', str(ds.as_download())] \n",
+        "                     ) \n",
+        "run = exp.submit(config=src)"
       ]
     },
     {
@@ -339,21 +325,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "run = exp.submit(config=src)\n",
-        "\n",
-        "from azureml.widgets import RunDetails\n",
-        "RunDetails(run).show()\n",
-        "\n",
         "run.wait_for_completion(show_output=True)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -384,7 +358,17 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "conda_env.python.user_managed_dependencies = True"
+        "# create a new RunConfig object\n",
+        "vm_run_config = RunConfiguration(framework=\"python\")\n",
+        "\n",
+        "# Set compute target to the Linux DSVM\n",
+        "vm_run_config.target = dsvm_compute.name\n",
+        "\n",
+        "# set the data reference of the run coonfiguration\n",
+        "conda_run_config.data_references = {ds.name: dr}\n",
+        "\n",
+        "# Let system know that you will configure the Python environment yourself.\n",
+        "vm_run_config.environment.python.user_managed_dependencies = True"
       ]
     },
     {
@@ -400,21 +384,20 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "src = ScriptRunConfig(source_directory=script_folder, \n",
+        "                      script='train.py', \n",
+        "                      run_config=vm_run_config,\n",
+        "                      # pass the datastore reference as a parameter to the training script\n",
+        "                      arguments=['--data-folder', str(ds.as_download())])\n",
         "run = exp.submit(config=src)\n",
-        "\n",
-        " from azureml.exceptions import ActivityFailedException\n",
-        "\n",
-        " try:\n",
-        "     run.wait_for_completion(show_output=True)\n",
-        " except ActivityFailedException as ex:\n",
-        "     print(ex)"
+        "run.wait_for_completion(show_output=True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "You can choose to SSH into the VM and install Azure ML SDK, and any other missing dependencies, in that Python environment. For demonstration purposes, we simply are going to use another script `train2.py` that doesn't have azureml or data store dependencies, and submit it instead."
+        "You can choose to SSH into the VM and install Azure ML SDK, and any other missing dependencies, in that Python environment. For demonstration purposes, we simply are going to create another script `train2.py` that doesn't have azureml dependencies, and submit it instead."
       ]
     },
     {
@@ -423,16 +406,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# copy train2.py into the script folder\n",
-        "shutil.copy('./train2.py', os.path.join(script_folder, 'train2.py'))\n",
+        "%%writefile $script_folder/train2.py\n",
         "\n",
-        "with open(os.path.join(script_folder, './train2.py'), 'r') as training_script:\n",
-        "    print(training_script.read())\n",
-        "\n",
-        "src = ScriptRunConfig(source_directory=script_folder, \n",
-        "                      script='train2.py', \n",
-        "                      compute_target = attached_dsvm_compute,\n",
-        "                      environment = conda_env) "
+        "print('####################################')\n",
+        "print('Hello World (without Azure ML SDK)!')\n",
+        "print('####################################')"
       ]
     },
     {
@@ -448,8 +426,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "src = ScriptRunConfig(source_directory=script_folder, \n",
+        "                      script='train2.py', \n",
+        "                      run_config=vm_run_config)\n",
         "run = exp.submit(config=src)\n",
-        "\n",
         "run.wait_for_completion(show_output=True)"
       ]
     },
@@ -483,10 +463,28 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "conda_env.docker.enabled = True\n",
-        "conda_env.python.user_managed_dependencies = False\n",
+        "from azureml.core.runconfig import RunConfiguration\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
         "\n",
-        "print('Base Docker image is:', conda_env.docker.base_image)"
+        "\n",
+        "# Load the \"cpu-dsvm.runconfig\" file (created by the above attach operation) in memory\n",
+        "docker_run_config = RunConfiguration(framework=\"python\")\n",
+        "\n",
+        "# Set compute target to the Linux DSVM\n",
+        "docker_run_config.target = dsvm_compute.name\n",
+        "\n",
+        "# Use Docker in the remote VM\n",
+        "docker_run_config.environment.docker.enabled = True\n",
+        "\n",
+        "# Use CPU base image from DockerHub\n",
+        "docker_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
+        "print('Base Docker image is:', docker_run_config.environment.docker.base_image)\n",
+        "\n",
+        "# set the data reference of the run coonfiguration\n",
+        "docker_run_config.data_references = {ds.name: dr}\n",
+        "\n",
+        "# specify CondaDependencies obj\n",
+        "docker_run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])"
       ]
     },
     {
@@ -505,40 +503,19 @@
       "source": [
         "src = ScriptRunConfig(source_directory=script_folder, \n",
         "                      script='train.py', \n",
-        "                      arguments=script_arguments,\n",
-        "                      compute_target = attached_dsvm_compute,\n",
-        "                      environment = conda_env) \n",
-        "\n",
-        "run = exp.submit(config=src)\n",
-        "\n",
-        "run.wait_for_completion(show_output=True)"
+        "                      run_config=docker_run_config,\n",
+        "                      # pass the datastore reference as a parameter to the training script\n",
+        "                      arguments=['--data-folder', str(ds.as_download())])\n",
+        "run = exp.submit(config=src)"
       ]
     },
     {
-      "cell_type": "markdown",
+      "cell_type": "code",
+      "execution_count": null,
       "metadata": {},
+      "outputs": [],
       "source": [
-        "#### Use a custom Docker image instead\n",
-        "\n",
-        "You can also specify a custom Docker image if you don't want to use the default image provided by Azure ML.\n",
-        "\n",
-        "```python\n",
-        "# use an image available in Docker Hub without authentication\n",
-        "conda_env.docker.base_image = \"continuumio/miniconda3\"\n",
-        "\n",
-        "# or, use an image available in a private Azure Container Registry\n",
-        "conda_env.docker.base_image = \"mycustomimage:1.0\"\n",
-        "conda_env.docker.base_image_registry.address = \"myregistry.azurecr.io\"\n",
-        "conda_env.docker.base_image_registry.username = \"username\"\n",
-        "conda_env.docker.base_image_registry.password = \"password\"\n",
-        "```\n",
-        "\n",
-        "When you are using a custom Docker image, you might already have your environment setup properly in a Python environment in the Docker image. In that case, you can skip specifying conda dependencies, and just use `user_managed_dependencies` option instead:\n",
-        "```python\n",
-        "conda_env.python.user_managed_dependencies = True\n",
-        "# path to the Python environment in the custom Docker image\n",
-        "conda_env.python.interpreter_path = '/opt/conda/bin/python'\n",
-        "```"
+        "run.wait_for_completion(show_output=True)"
       ]
     },
     {
@@ -602,9 +579,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Clean up compute resource\n",
-        "\n",
-        "Use ```detach()``` to detach an existing DSVM from Workspace without deleting it. Use ```delete()``` if you created a new ```DsvmCompute``` and want to delete it."
+        "## Clean up compute resource"
       ]
     },
     {
@@ -613,37 +588,20 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# dsvm_compute.detach()\n",
-        "# dsvm_compute.delete()"
+        "dsvm_compute.delete()"
       ]
     }
   ],
   "metadata": {
     "authors": [
       {
-        "name": "sanpil"
+        "name": "haining"
       }
     ],
-    "category": "training",
-    "compute": [
-      "Data Science Virtual Machine"
-    ],
-    "datasets": [
-      "Diabetes"
-    ],
-    "deployment": [
-      "None"
-    ],
-    "exclude_from_index": false,
-    "framework": [
-      "None"
-    ],
-    "friendly_name": "Train in a remote Linux virtual machine",
-    "index_order": 1,
     "kernelspec": {
-      "display_name": "Python 3.8 - AzureML",
+      "display_name": "Python 3.6",
       "language": "python",
-      "name": "python38-azureml"
+      "name": "python36"
     },
     "language_info": {
       "codemirror_mode": {
@@ -655,12 +613,8 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.7"
-    },
-    "tags": [
-      "None"
-    ],
-    "task": "Configure and execute a run"
+      "version": "3.6.6"
+    }
   },
   "nbformat": 4,
   "nbformat_minor": 2

01.getting-started/04.train-on-remote-vm/train.py

@@ -8,15 +8,10 @@ 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 numpy as np
 
-# sklearn.externals.joblib is removed in 0.23
-try:
-    from sklearn.externals import joblib
-except ImportError:
-    import joblib
-
 os.makedirs('./outputs', exist_ok=True)
 parser = argparse.ArgumentParser()
 parser.add_argument('--data-folder', type=str,

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

@@ -9,13 +9,6 @@
         "Licensed under the MIT License."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/training/train-in-spark/train-in-spark.png)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -32,7 +25,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't already to establish your connection to the AzureML Workspace."
+        "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
       ]
     },
     {
@@ -63,6 +56,7 @@
       "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')"
       ]
@@ -116,7 +110,82 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "**Note** You can use Docker-based execution to run the Spark job in local computer or a remote VM. Please see the `train-in-remote-vm` notebook for example on how to configure and run in Docker mode in a VM. Make sure you choose a Docker image that has Spark installed, such as `microsoft/mmlspark:0.12`."
+        "### 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."
       ]
     },
     {
@@ -124,7 +193,7 @@
       "metadata": {},
       "source": [
         "### Attach an HDI cluster\n",
-        "Here we will use a actual Spark cluster, HDInsight for Spark, to run this job. To use HDI commpute target:\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"
       ]
@@ -132,33 +201,20 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {
-        "tags": [
-          "sample-hdinsightcompute-attach"
-        ]
-      },
+      "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import ComputeTarget, HDInsightCompute\n",
+        "from azureml.core.compute import HDInsightCompute\n",
         "from azureml.exceptions import ComputeTargetException\n",
-        "import os\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",
-        "\n",
-        "# Attaching a HDInsight cluster using the public address of the HDInsight cluster is no longer supported.\n",
-        "# Instead, use resourceId of the HDInsight cluster.\n",
-        "# The resourceId of the HDInsight Cluster can be constructed using the following string format:\n",
-        "# /subscriptions/<subscription_id>/resourceGroups/<resource_group>/providers/Microsoft.HDInsight/clusters/<cluster_name>.\n",
-        "# You can also use subscription_id, resource_group and cluster_name without constructing resourceId.\n",
-        "    attach_config = HDInsightCompute.attach_configuration(resource_id='<resource_id>',\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=os.environ.get('hdiusername', '<ssh_username>'),\n",
-        "                                                          password=os.environ.get('hdipassword', '<my_password>'))\n",
-        "\n",
-        "    hdi_compute = ComputeTarget.attach(workspace=ws, \n",
-        "                                       name='myhdi', \n",
-        "                                       attach_configuration=attach_config)\n",
+        "                                          username='<ssh-username>', \n",
+        "                                          password='<ssh-pwd>')\n",
         "\n",
         "except ComputeTargetException as e:\n",
         "    print(\"Caught = {}\".format(e.message))\n",
@@ -174,13 +230,6 @@
         "### Configure HDI run"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Configure an execution using the HDInsight cluster with a conda environment that has `numpy`."
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -190,6 +239,7 @@
         "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",
@@ -223,35 +273,14 @@
         "run = exp.submit(config=script_run_config)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Monitor the run using a Juypter widget"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.widgets import RunDetails\n",
-        "RunDetails(run).show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "After the run is succesfully finished, you can check the metrics logged."
+        "# get the URL of the run history web page\n",
+        "run"
       ]
     },
     {
@@ -270,38 +299,19 @@
       "execution_count": null,
       "metadata": {},
       "outputs": [],
-      "source": [
-        "# register the generated model\n",
-        "model = run.register_model(model_name='iris.model', model_path='outputs/iris.model')\n"
-      ]
+      "source": []
     }
   ],
   "metadata": {
     "authors": [
       {
-        "name": "sanpil"
+        "name": "aashishb"
       }
     ],
-    "category": "training",
-    "compute": [
-      "HDI cluster"
-    ],
-    "datasets": [
-      "None"
-    ],
-    "deployment": [
-      "None"
-    ],
-    "exclude_from_index": false,
-    "framework": [
-      "PySpark"
-    ],
-    "friendly_name": "Training in Spark",
-    "index_order": 1,
     "kernelspec": {
-      "display_name": "Python 3.8 - AzureML",
+      "display_name": "Python 3.6",
       "language": "python",
-      "name": "python38-azureml"
+      "name": "python36"
     },
     "language_info": {
       "codemirror_mode": {
@@ -313,12 +323,8 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.2"
-    },
-    "tags": [
-      "None"
-    ],
-    "task": "Submiting a run on a spark cluster"
+      "version": "3.6.6"
+    }
   },
   "nbformat": 4,
   "nbformat_minor": 2

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

@@ -14,6 +14,7 @@ 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
@@ -37,11 +38,7 @@ schema = StructType([
     StructField("class", StringType())
 ])
 
-data = spark.read.format("com.databricks.spark.csv") \
-    .option("header", "true") \
-    .schema(schema) \
-    .load("iris.csv")
-
+data = spark.read.csv('iris.csv', header=False, schema=schema)
 print("First 10 rows of Iris dataset:")
 data.show(10)
 
@@ -76,8 +73,6 @@ train, test = data.randomSplit([0.70, 0.30])
 lr = pyspark.ml.classification.LogisticRegression(regParam=reg)
 model = lr.fit(train)
 
-model.save(os.path.join("outputs", "iris.model"))
-
 # predict on the test set
 prediction = model.transform(test)
 print("Prediction")

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

@@ -0,0 +1,425 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 10. Register Model, Create Image and Deploy Service\n",
+        "\n",
+        "This example shows how to deploy a web service in step-by-step fashion:\n",
+        "\n",
+        " 1. Register model\n",
+        " 2. Query versions of models and select one to deploy\n",
+        " 3. Create Docker image\n",
+        " 4. Query versions of images\n",
+        " 5. Deploy the image as web service\n",
+        " \n",
+        "**IMPORTANT**:\n",
+        " * This notebook requires you to first complete \"01.SDK-101-Train-and-Deploy-to-ACI.ipynb\" Notebook\n",
+        " \n",
+        "The 101 Notebook taught you how to deploy a web service directly from model in one step. This Notebook shows a more advanced approach that gives you more control over model versions and Docker image versions.  "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prerequisites\n",
+        "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Check core SDK version number\n",
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize Workspace\n",
+        "\n",
+        "Initialize a workspace object from persisted configuration."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "create workspace"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Register Model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can add tags and descriptions to your models. Note you need to have a `sklearn_linreg_model.pkl` file in the current directory. This file is generated by the 01 notebook. The below call registers that file as a model with the same name `sklearn_linreg_model.pkl` in the workspace.\n",
+        "\n",
+        "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "register model from file"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core.model import Model\n",
+        "import sklearn\n",
+        "\n",
+        "library_version = \"sklearn\"+sklearn.__version__.replace(\".\",\"x\")\n",
+        "\n",
+        "model = Model.register(model_path = \"sklearn_regression_model.pkl\",\n",
+        "                       model_name = \"sklearn_regression_model.pkl\",\n",
+        "                       tags = {'area': \"diabetes\", 'type': \"regression\", 'version': library_version},\n",
+        "                       description = \"Ridge regression model to predict diabetes\",\n",
+        "                       workspace = ws)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can explore the registered models within your workspace and query by tag. Models are versioned. If you call the register_model command many times with same model name, you will get multiple versions of the model with increasing version numbers."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "register model from file"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "regression_models = Model.list(workspace=ws, tags=['area'])\n",
+        "for m in regression_models:\n",
+        "    print(\"Name:\", m.name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can pick a specific model to deploy"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(model.name, model.description, model.version, sep = '\\t')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create Docker Image"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Show `score.py`. Note that the `sklearn_regression_model.pkl` in the `get_model_path` call is referring to a model named `sklearn_linreg_model.pkl` registered under the workspace. It is NOT referenceing the local file."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%writefile score.py\n",
+        "import pickle\n",
+        "import json\n",
+        "import numpy\n",
+        "from sklearn.externals import joblib\n",
+        "from sklearn.linear_model import Ridge\n",
+        "from azureml.core.model import Model\n",
+        "\n",
+        "def init():\n",
+        "    global model\n",
+        "    # note here \"sklearn_regression_model.pkl\" is the name of the model registered under\n",
+        "    # this is a different behavior than before when the code is run locally, even though the code is the same.\n",
+        "    model_path = Model.get_model_path('sklearn_regression_model.pkl')\n",
+        "    # deserialize the model file back into a sklearn model\n",
+        "    model = joblib.load(model_path)\n",
+        "\n",
+        "# note you can pass in multiple rows for scoring\n",
+        "def run(raw_data):\n",
+        "    try:\n",
+        "        data = json.loads(raw_data)['data']\n",
+        "        data = numpy.array(data)\n",
+        "        result = model.predict(data)\n",
+        "    except Exception as e:\n",
+        "        result = str(e)\n",
+        "    return json.dumps({\"result\": result.tolist()})"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.conda_dependencies import CondaDependencies \n",
+        "\n",
+        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
+        "\n",
+        "with open(\"myenv.yml\",\"w\") as f:\n",
+        "    f.write(myenv.serialize_to_string())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Note that following command can take few minutes. \n",
+        "\n",
+        "You can add tags and descriptions to images. Also, an image can contain multiple models."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "create image"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core.image import Image, ContainerImage\n",
+        "\n",
+        "image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
+        "                                 execution_script=\"score.py\",\n",
+        "                                 conda_file=\"myenv.yml\",\n",
+        "                                 tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
+        "                                 description = \"Image with ridge regression model\")\n",
+        "\n",
+        "image = Image.create(name = \"myimage1\",\n",
+        "                     # this is the model object \n",
+        "                     models = [model],\n",
+        "                     image_config = image_config, \n",
+        "                     workspace = ws)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "create image"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "image.wait_for_creation(show_output = True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "List images by tag and find out the detailed build log for debugging."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "create image"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "for i in Image.list(workspace = ws,tags = [\"area\"]):\n",
+        "    print('{}(v.{} [{}]) stored at {} with build log {}'.format(i.name, i.version, i.creation_state, i.image_location, i.image_build_log_uri))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Deploy image as web service on Azure Container Instance\n",
+        "\n",
+        "Note that the service creation can take few minutes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "deploy service",
+          "aci"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import AciWebservice\n",
+        "\n",
+        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
+        "                                               memory_gb = 1, \n",
+        "                                               tags = {'area': \"diabetes\", 'type': \"regression\"}, \n",
+        "                                               description = 'Predict diabetes using regression model')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "deploy service",
+          "aci"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import Webservice\n",
+        "\n",
+        "aci_service_name = 'my-aci-service-2'\n",
+        "print(aci_service_name)\n",
+        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
+        "                                           image = image,\n",
+        "                                           name = aci_service_name,\n",
+        "                                           workspace = ws)\n",
+        "aci_service.wait_for_deployment(True)\n",
+        "print(aci_service.state)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Test web service"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Call the web service with some dummy input data to get a prediction."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "deploy service",
+          "aci"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "import json\n",
+        "\n",
+        "test_sample = json.dumps({'data': [\n",
+        "    [1,2,3,4,5,6,7,8,9,10], \n",
+        "    [10,9,8,7,6,5,4,3,2,1]\n",
+        "]})\n",
+        "test_sample = bytes(test_sample,encoding = 'utf8')\n",
+        "\n",
+        "prediction = aci_service.run(input_data = test_sample)\n",
+        "print(prediction)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Delete ACI to clean up"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "deploy service",
+          "aci"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "aci_service.delete()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "raymondl"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.5"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

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

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

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

@@ -0,0 +1,452 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Enabling Data Collection for Models in Production\n",
+    "With this notebook, you can learn how to collect input model data from your Azure Machine Learning service in an Azure Blob storage. Once enabled, this data collected gives you the opportunity:\n",
+    "\n",
+    "* Monitor data drifts as production data enters your model\n",
+    "* Make better decisions on when to retrain or optimize your model\n",
+    "* Retrain your model with the data collected\n",
+    "\n",
+    "## What data is collected?\n",
+    "* Model input data (voice, images, and video are not supported) from services deployed in Azure Kubernetes Cluster (AKS)\n",
+    "* Model predictions using production input data.\n",
+    "\n",
+    "**Note:** pre-aggregation or pre-calculations on this data are done by user and not included in this version of the product.\n",
+    "\n",
+    "## What is different compared to standard production deployment process?\n",
+    "1. Update scoring file.\n",
+    "2. Update yml file with new dependency.\n",
+    "3. Update aks configuration.\n",
+    "4. Build new image and deploy it. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Import your dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace, Run\n",
+    "from azureml.core.compute import AksCompute, ComputeTarget\n",
+    "from azureml.core.webservice import Webservice, AksWebservice\n",
+    "from azureml.core.image import Image\n",
+    "from azureml.core.model import Model\n",
+    "\n",
+    "import azureml.core\n",
+    "print(azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Set up your configuration and create a workspace\n",
+    "Follow Notebook 00 instructions to do this.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Register Model\n",
+    "Register an existing trained model, add descirption and tags."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Register the model\n",
+    "from azureml.core.model import Model\n",
+    "model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n",
+    "                       model_name = \"sklearn_regression_model.pkl\", # this is the name the model is registered as\n",
+    "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
+    "                       description = \"Ridge regression model to predict diabetes\",\n",
+    "                       workspace = ws)\n",
+    "\n",
+    "print(model.name, model.description, model.version)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. *Update your scoring file with Data Collection*\n",
+    "The file below, compared to the file used in notebook 11, has the following changes:\n",
+    "### a. Import the module\n",
+    "```python \n",
+    "from azureml.monitoring import ModelDataCollector```\n",
+    "### b. In your init function add:\n",
+    "```python \n",
+    "global inputs_dc, prediction_d\n",
+    "inputs_dc = ModelDataCollector(\"best_model\", identifier=\"inputs\", feature_names=[\"feat1\", \"feat2\", \"feat3\", \"feat4\", \"feat5\", \"Feat6\"])\n",
+    "prediction_dc = ModelDataCollector(\"best_model\", identifier=\"predictions\", feature_names=[\"prediction1\", \"prediction2\"])```\n",
+    "    \n",
+    "* Identifier: Identifier is later used for building the folder structure in your Blob, it can be used to divide \"raw\" data versus \"processed\".\n",
+    "* CorrelationId: is an optional parameter, you do not need to set it up if your model doesn't require it. Having a correlationId in place does help you for easier mapping with other data. (Examples include: LoanNumber, CustomerId, etc.)\n",
+    "* Feature Names: These need to be set up in the order of your features in order for them to have column names when the .csv is created.\n",
+    "\n",
+    "### c. In your run function add:\n",
+    "```python\n",
+    "inputs_dc.collect(data)\n",
+    "prediction_dc.collect(result)```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile score.py\n",
+    "import pickle\n",
+    "import json\n",
+    "import numpy \n",
+    "from sklearn.externals import joblib\n",
+    "from sklearn.linear_model import Ridge\n",
+    "from azureml.core.model import Model\n",
+    "from azureml.monitoring import ModelDataCollector\n",
+    "import time\n",
+    "\n",
+    "def init():\n",
+    "    global model\n",
+    "    print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))\n",
+    "    # note here \"sklearn_regression_model.pkl\" is the name of the model registered under the workspace\n",
+    "    # this call should return the path to the model.pkl file on the local disk.\n",
+    "    model_path = Model.get_model_path(model_name = 'sklearn_regression_model.pkl')\n",
+    "    # deserialize the model file back into a sklearn model\n",
+    "    model = joblib.load(model_path)\n",
+    "    global inputs_dc, prediction_dc\n",
+    "    # this setup will help us save our inputs under the \"inputs\" path in our Azure Blob\n",
+    "    inputs_dc = ModelDataCollector(model_name=\"sklearn_regression_model\", identifier=\"inputs\", feature_names=[\"feat1\", \"feat2\"]) \n",
+    "    # this setup will help us save our ipredictions under the \"predictions\" path in our Azure Blob\n",
+    "    prediction_dc = ModelDataCollector(\"sklearn_regression_model\", identifier=\"predictions\", feature_names=[\"prediction1\", \"prediction2\"]) \n",
+    "  \n",
+    "# note you can pass in multiple rows for scoring\n",
+    "def run(raw_data):\n",
+    "    global inputs_dc, prediction_dc\n",
+    "    try:\n",
+    "        data = json.loads(raw_data)['data']\n",
+    "        data = numpy.array(data)\n",
+    "        result = model.predict(data)\n",
+    "        print (\"saving input data\" + time.strftime(\"%H:%M:%S\"))\n",
+    "        inputs_dc.collect(data) #this call is saving our input data into our blob\n",
+    "        prediction_dc.collect(result)#this call is saving our prediction data into our blob\n",
+    "        print (\"saving prediction data\" + time.strftime(\"%H:%M:%S\"))\n",
+    "        return json.dumps({\"result\": result.tolist()})\n",
+    "    except Exception as e:\n",
+    "        result = str(e)\n",
+    "        print (result + time.strftime(\"%H:%M:%S\"))\n",
+    "        return json.dumps({\"error\": result})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. *Update your myenv.yml file with the required module*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.conda_dependencies import CondaDependencies \n",
+    "\n",
+    "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
+    "myenv.add_pip_package(\"azureml-monitoring\")\n",
+    "\n",
+    "with open(\"myenv.yml\",\"w\") as f:\n",
+    "    f.write(myenv.serialize_to_string())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Create your new Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.image import ContainerImage\n",
+    "\n",
+    "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
+    "                                                  runtime = \"python\",\n",
+    "                                                  conda_file = \"myenv.yml\",\n",
+    "                                                  description = \"Image with ridge regression model\",\n",
+    "                                                  tags = {'area': \"diabetes\", 'type': \"regression\"}\n",
+    "                                                 )\n",
+    "\n",
+    "image = ContainerImage.create(name = \"myimage1\",\n",
+    "                              # this is the model object\n",
+    "                              models = [model],\n",
+    "                              image_config = image_config,\n",
+    "                              workspace = ws)\n",
+    "\n",
+    "image.wait_for_creation(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(model.name, model.description, model.version)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Deploy to AKS service"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create AKS compute if you haven't done so (Notebook 11)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use the default configuration (can also provide parameters to customize)\n",
+    "prov_config = AksCompute.provisioning_configuration()\n",
+    "\n",
+    "aks_name = 'my-aks-test1' \n",
+    "# Create the cluster\n",
+    "aks_target = ComputeTarget.create(workspace = ws, \n",
+    "                                  name = aks_name, \n",
+    "                                  provisioning_configuration = prov_config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "aks_target.wait_for_completion(show_output = True)\n",
+    "print(aks_target.provisioning_state)\n",
+    "print(aks_target.provisioning_errors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you already have a cluster you can attach the service to it:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "scrolled": true
+   },
+   "source": [
+    "```python \n",
+    "    %%time\n",
+    "    resource_id = '/subscriptions/<subscriptionid>/resourcegroups/<resourcegroupname>/providers/Microsoft.ContainerService/managedClusters/<aksservername>'\n",
+    "    create_name= 'myaks4'\n",
+    "    aks_target = AksCompute.attach(workspace = ws, \n",
+    "                                      name = create_name, \n",
+    "                                      resource_id=resource_id)\n",
+    "    ## Wait for the operation to complete\n",
+    "    aks_target.wait_for_provisioning(True)```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### a. *Activate Data Collection and App Insights through updating AKS Webservice configuration*\n",
+    "In order to enable Data Collection and App Insights in your service you will need to update your AKS configuration file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Set the web service configuration\n",
+    "aks_config = AksWebservice.deploy_configuration(collect_model_data=True, enable_app_insights=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### b. Deploy your service"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "aks_service_name ='aks-w-dc2'\n",
+    "\n",
+    "aks_service = Webservice.deploy_from_image(workspace = ws, \n",
+    "                                           name = aks_service_name,\n",
+    "                                           image = image,\n",
+    "                                           deployment_config = aks_config,\n",
+    "                                           deployment_target = aks_target\n",
+    "                                           )\n",
+    "aks_service.wait_for_deployment(show_output = True)\n",
+    "print(aks_service.state)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 8. Test your service and send some data\n",
+    "**Note**: It will take around 15 mins for your data to appear in your blob.\n",
+    "The data will appear in your Azure Blob following this format:\n",
+    "\n",
+    "/modeldata/subscriptionid/resourcegroupname/workspacename/webservicename/modelname/modelversion/identifier/year/month/day/data.csv "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "import json\n",
+    "\n",
+    "test_sample = json.dumps({'data': [\n",
+    "    [1,2,3,4,54,6,7,8,88,10], \n",
+    "    [10,9,8,37,36,45,4,33,2,1]\n",
+    "]})\n",
+    "test_sample = bytes(test_sample,encoding = 'utf8')\n",
+    "\n",
+    "prediction = aks_service.run(input_data = test_sample)\n",
+    "print(prediction)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 9. Validate you data and analyze it\n",
+    "You can look into your data following this path format in your Azure Blob (it takes up to 15 minutes for the data to appear):\n",
+    "\n",
+    "/modeldata/**subscriptionid>**/**resourcegroupname>**/**workspacename>**/**webservicename>**/**modelname>**/**modelversion>>**/**identifier>**/*year/month/day*/data.csv \n",
+    "\n",
+    "For doing further analysis you have multiple options:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### a. Create DataBricks cluter and connect it to your blob\n",
+    "https://docs.microsoft.com/en-us/azure/azure-databricks/quickstart-create-databricks-workspace-portal or in your databricks workspace you can look for the template \"Azure Blob Storage Import Example Notebook\".\n",
+    "\n",
+    "\n",
+    "Here is an example for setting up the file location to extract the relevant data:\n",
+    "\n",
+    "<code> file_location = \"wasbs://mycontainer@storageaccountname.blob.core.windows.net/unknown/unknown/unknown-bigdataset-unknown/my_iterate_parking_inputs/2018/&deg;/&deg;/data.csv\" \n",
+    "file_type = \"csv\"</code>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### b. Connect Blob to Power Bi (Small Data only)\n",
+    "1. Download and Open PowerBi Desktop\n",
+    "2. Select “Get Data” and click on “Azure Blob Storage” >> Connect\n",
+    "3. Add your storage account and enter your storage key.\n",
+    "4. Select the container where your Data Collection is stored and click on Edit. \n",
+    "5. In the query editor, click under “Name” column and add your Storage account Model path into the filter. Note: if you want to only look into files from a specific year or month, just expand the filter path. For example, just look into March data: /modeldata/subscriptionid>/resourcegroupname>/workspacename>/webservicename>/modelname>/modelversion>/identifier>/year>/3\n",
+    "6. Click on the double arrow aside the “Content” column to combine the files. \n",
+    "7. Click OK and the data will preload.\n",
+    "8. You can now click Close and Apply and start building your custom reports on your Model Input data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Disable Data Collection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aks_service.update(collect_model_data=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "marthalc"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -0,0 +1,415 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Enabling App Insights for Services in Production\n",
+        "With this notebook, you can learn how to enable App Insights for standard service monitoring, plus, we provide examples for doing custom logging within a scoring files in a model. \n",
+        "\n",
+        "\n",
+        "## What does Application Insights monitor?\n",
+        "It monitors request rates, response times, failure rates, etc. For more information visit [App Insights docs.](https://docs.microsoft.com/en-us/azure/application-insights/app-insights-overview)\n",
+        "\n",
+        "\n",
+        "## What is different compared to standard production deployment process?\n",
+        "If you want to enable generic App Insights for a service run:\n",
+        "```python\n",
+        "aks_service= Webservice(ws, \"aks-w-dc2\")\n",
+        "aks_service.update(enable_app_insights=True)```\n",
+        "Where \"aks-w-dc2\" is your service name. You can also do this from the Azure Portal under your Workspace--> deployments--> Select deployment--> Edit--> Advanced Settings--> Select \"Enable AppInsights diagnostics\"\n",
+        "\n",
+        "If you want to log custom traces, you will follow the standard deplyment process for AKS and you will:\n",
+        "1. Update scoring file.\n",
+        "2. Update aks configuration.\n",
+        "3. Build new image and deploy it. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 1. Import your dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace, Run\n",
+        "from azureml.core.compute import AksCompute, ComputeTarget\n",
+        "from azureml.core.webservice import Webservice, AksWebservice\n",
+        "from azureml.core.image import Image\n",
+        "from azureml.core.model import Model\n",
+        "\n",
+        "import azureml.core\n",
+        "print(azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 2. Set up your configuration and create a workspace\n",
+        "Follow Notebook 00 instructions to do this.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 3. Register Model\n",
+        "Register an existing trained model, add descirption and tags."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#Register the model\n",
+        "from azureml.core.model import Model\n",
+        "model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n",
+        "                       model_name = \"sklearn_regression_model.pkl\", # this is the name the model is registered as\n",
+        "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
+        "                       description = \"Ridge regression model to predict diabetes\",\n",
+        "                       workspace = ws)\n",
+        "\n",
+        "print(model.name, model.description, model.version)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 4. *Update your scoring file with custom print statements*\n",
+        "Here is an example:\n",
+        "### a. In your init function add:\n",
+        "```python\n",
+        "print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))```\n",
+        "\n",
+        "### b. In your run function add:\n",
+        "```python\n",
+        "print (\"saving input data\" + time.strftime(\"%H:%M:%S\"))\n",
+        "print (\"saving prediction data\" + time.strftime(\"%H:%M:%S\"))```"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%writefile score.py\n",
+        "import pickle\n",
+        "import json\n",
+        "import numpy \n",
+        "from sklearn.externals import joblib\n",
+        "from sklearn.linear_model import Ridge\n",
+        "from azureml.core.model import Model\n",
+        "from azureml.monitoring import ModelDataCollector\n",
+        "import time\n",
+        "\n",
+        "def init():\n",
+        "    global model\n",
+        "    #Print statement for appinsights custom traces:\n",
+        "    print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))\n",
+        "    \n",
+        "    # note here \"sklearn_regression_model.pkl\" is the name of the model registered under the workspace\n",
+        "    # this call should return the path to the model.pkl file on the local disk.\n",
+        "    model_path = Model.get_model_path(model_name = 'sklearn_regression_model.pkl')\n",
+        "    \n",
+        "    # deserialize the model file back into a sklearn model\n",
+        "    model = joblib.load(model_path)\n",
+        "    \n",
+        "    global inputs_dc, prediction_dc\n",
+        "    \n",
+        "    # this setup will help us save our inputs under the \"inputs\" path in our Azure Blob\n",
+        "    inputs_dc = ModelDataCollector(model_name=\"sklearn_regression_model\", identifier=\"inputs\", feature_names=[\"feat1\", \"feat2\"]) \n",
+        "    \n",
+        "    # this setup will help us save our ipredictions under the \"predictions\" path in our Azure Blob\n",
+        "    prediction_dc = ModelDataCollector(\"sklearn_regression_model\", identifier=\"predictions\", feature_names=[\"prediction1\", \"prediction2\"]) \n",
+        "  \n",
+        "# note you can pass in multiple rows for scoring\n",
+        "def run(raw_data):\n",
+        "    global inputs_dc, prediction_dc\n",
+        "    try:\n",
+        "        data = json.loads(raw_data)['data']\n",
+        "        data = numpy.array(data)\n",
+        "        result = model.predict(data)\n",
+        "        \n",
+        "        #Print statement for appinsights custom traces:\n",
+        "        print (\"saving input data\" + time.strftime(\"%H:%M:%S\"))\n",
+        "        \n",
+        "        #this call is saving our input data into our blob\n",
+        "        inputs_dc.collect(data) \n",
+        "        #this call is saving our prediction data into our blob\n",
+        "        prediction_dc.collect(result)\n",
+        "        \n",
+        "        #Print statement for appinsights custom traces:\n",
+        "        print (\"saving prediction data\" + time.strftime(\"%H:%M:%S\"))\n",
+        "        \n",
+        "        return json.dumps({\"result\": result.tolist()})\n",
+        "    \n",
+        "    except Exception as e:\n",
+        "        result = str(e)\n",
+        "        print (result + time.strftime(\"%H:%M:%S\"))\n",
+        "        return json.dumps({\"error\": result})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 5. *Create myenv.yml file*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.conda_dependencies import CondaDependencies \n",
+        "\n",
+        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
+        "\n",
+        "with open(\"myenv.yml\",\"w\") as f:\n",
+        "    f.write(myenv.serialize_to_string())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 6. Create your new Image"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.image import ContainerImage\n",
+        "\n",
+        "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
+        "                                                  runtime = \"python\",\n",
+        "                                                  conda_file = \"myenv.yml\",\n",
+        "                                                  description = \"Image with ridge regression model\",\n",
+        "                                                  tags = {'area': \"diabetes\", 'type': \"regression\"}\n",
+        "                                                 )\n",
+        "\n",
+        "image = ContainerImage.create(name = \"myimage1\",\n",
+        "                              # this is the model object\n",
+        "                              models = [model],\n",
+        "                              image_config = image_config,\n",
+        "                              workspace = ws)\n",
+        "\n",
+        "image.wait_for_creation(show_output = True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 7. Deploy to AKS service"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create AKS compute if you haven't done so (Notebook 11)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Use the default configuration (can also provide parameters to customize)\n",
+        "prov_config = AksCompute.provisioning_configuration()\n",
+        "\n",
+        "aks_name = 'my-aks-test1' \n",
+        "# Create the cluster\n",
+        "aks_target = ComputeTarget.create(workspace = ws, \n",
+        "                                  name = aks_name, \n",
+        "                                  provisioning_configuration = prov_config)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "aks_target.wait_for_completion(show_output = True)\n",
+        "print(aks_target.provisioning_state)\n",
+        "print(aks_target.provisioning_errors)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If you already have a cluster you can attach the service to it:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "```python \n",
+        "%%time\n",
+        "resource_id = '/subscriptions/<subscriptionid>/resourcegroups/<resourcegroupname>/providers/Microsoft.ContainerService/managedClusters/<aksservername>'\n",
+        "create_name= 'myaks4'\n",
+        "aks_target = AksCompute.attach(workspace = ws, \n",
+        "                                  name = create_name, \n",
+        "                                  #esource_id=resource_id)\n",
+        "## Wait for the operation to complete\n",
+        "aks_target.wait_for_provisioning(True)```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### a. *Activate App Insights through updating AKS Webservice configuration*\n",
+        "In order to enable App Insights in your service you will need to update your AKS configuration file:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#Set the web service configuration\n",
+        "aks_config = AksWebservice.deploy_configuration(enable_app_insights=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### b. Deploy your service"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "aks_service_name ='aks-w-dc3'\n",
+        "\n",
+        "aks_service = Webservice.deploy_from_image(workspace = ws, \n",
+        "                                           name = aks_service_name,\n",
+        "                                           image = image,\n",
+        "                                           deployment_config = aks_config,\n",
+        "                                           deployment_target = aks_target\n",
+        "                                           )\n",
+        "aks_service.wait_for_deployment(show_output = True)\n",
+        "print(aks_service.state)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 8. Test your service "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "import json\n",
+        "\n",
+        "test_sample = json.dumps({'data': [\n",
+        "    [1,28,13,45,54,6,57,8,8,10], \n",
+        "    [101,9,8,37,6,45,4,3,2,41]\n",
+        "]})\n",
+        "test_sample = bytes(test_sample,encoding = 'utf8')\n",
+        "\n",
+        "prediction = aks_service.run(input_data = test_sample)\n",
+        "print(prediction)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 9. See your service telemetry in App Insights\n",
+        "1. Go to the [Azure Portal](https://portal.azure.com/)\n",
+        "2. All resources--> Select the subscription/resource group where you created your Workspace--> Select the App Insights type\n",
+        "3. Click on the AppInsights resource. You'll see a highlevel dashboard with information on Requests, Server response time and availability.\n",
+        "4. Click on the top banner \"Analytics\"\n",
+        "5. In the \"Schema\" section select \"traces\" and run your query.\n",
+        "6. Voila! All your custom traces should be there."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Disable App Insights"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "aks_service.update(enable_app_insights=False)"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "marthalc"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

CODE_OF_CONDUCT.md

@@ -1,9 +0,0 @@
-# Microsoft Open Source Code of Conduct
-
-This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
-
-Resources:
-
-- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/)
-- [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
-- Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns

Dockerfiles/1.0.10/Dockerfile

@@ -1,29 +0,0 @@
-FROM continuumio/miniconda:4.5.11
-
-# install git
-RUN apt-get update && apt-get upgrade -y && apt-get install -y git
-
-# create a new conda environment named azureml
-RUN conda create -n azureml -y -q Python=3.6
-
-# install additional packages used by sample notebooks. this is optional
-RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
-
-# install azurmel-sdk components
-RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.10"]
-
-# clone Azure ML GitHub sample notebooks
-RUN cd /home && git clone -b "azureml-sdk-1.0.10" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
-
-# generate jupyter configuration file
-RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
-
-# set an emtpy token for Jupyter to remove authentication. 
-# this is NOT recommended for production environment
-RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
-
-# open up port 8887 on the container
-EXPOSE 8887
-
-# start Jupyter notebook server on port 8887 when the container starts
-CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"

Dockerfiles/1.0.15/Dockerfile

@@ -1,29 +0,0 @@
-FROM continuumio/miniconda:4.5.11
-
-# install git
-RUN apt-get update && apt-get upgrade -y && apt-get install -y git
-
-# create a new conda environment named azureml
-RUN conda create -n azureml -y -q Python=3.6
-
-# install additional packages used by sample notebooks. this is optional
-RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
-
-# install azurmel-sdk components
-RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.15"]
-
-# clone Azure ML GitHub sample notebooks
-RUN cd /home && git clone -b "azureml-sdk-1.0.15" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
-
-# generate jupyter configuration file
-RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
-
-# set an emtpy token for Jupyter to remove authentication. 
-# this is NOT recommended for production environment
-RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
-
-# open up port 8887 on the container
-EXPOSE 8887
-
-# start Jupyter notebook server on port 8887 when the container starts
-CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"

Dockerfiles/1.0.17/Dockerfile

@@ -1,29 +0,0 @@
-FROM continuumio/miniconda:4.5.11
-
-# install git
-RUN apt-get update && apt-get upgrade -y && apt-get install -y git
-
-# create a new conda environment named azureml
-RUN conda create -n azureml -y -q Python=3.6
-
-# install additional packages used by sample notebooks. this is optional
-RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
-
-# install azurmel-sdk components
-RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.17"]
-
-# clone Azure ML GitHub sample notebooks
-RUN cd /home && git clone -b "azureml-sdk-1.0.17" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
-
-# generate jupyter configuration file
-RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
-
-# set an emtpy token for Jupyter to remove authentication. 
-# this is NOT recommended for production environment
-RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
-
-# open up port 8887 on the container
-EXPOSE 8887
-
-# start Jupyter notebook server on port 8887 when the container starts
-CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"

Dockerfiles/1.0.18/Dockerfile

@@ -1,29 +0,0 @@
-FROM continuumio/miniconda:4.5.11
-
-# install git
-RUN apt-get update && apt-get upgrade -y && apt-get install -y git
-
-# create a new conda environment named azureml
-RUN conda create -n azureml -y -q Python=3.6
-
-# install additional packages used by sample notebooks. this is optional
-RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
-
-# install azurmel-sdk components
-RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.18"]
-
-# clone Azure ML GitHub sample notebooks
-RUN cd /home && git clone -b "azureml-sdk-1.0.18" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
-
-# generate jupyter configuration file
-RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
-
-# set an emtpy token for Jupyter to remove authentication. 
-# this is NOT recommended for production environment
-RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
-
-# open up port 8887 on the container
-EXPOSE 8887
-
-# start Jupyter notebook server on port 8887 when the container starts
-CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"

Dockerfiles/1.0.2/Dockerfile

@@ -1,29 +0,0 @@
-FROM continuumio/miniconda:4.5.11
-
-# install git
-RUN apt-get update && apt-get upgrade -y && apt-get install -y git
-
-# create a new conda environment named azureml
-RUN conda create -n azureml -y -q Python=3.6
-
-# install additional packages used by sample notebooks. this is optional
-RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
-
-# install azurmel-sdk components
-RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.2"]
-
-# clone Azure ML GitHub sample notebooks
-RUN cd /home && git clone -b "azureml-sdk-1.0.2" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
-
-# generate jupyter configuration file
-RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
-
-# set an emtpy token for Jupyter to remove authentication. 
-# this is NOT recommended for production environment
-RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
-
-# open up port 8887 on the container
-EXPOSE 8887
-
-# start Jupyter notebook server on port 8887 when the container starts
-CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"

Dockerfiles/1.0.21/Dockerfile

@@ -1,29 +0,0 @@
-FROM continuumio/miniconda:4.5.11
-
-# install git
-RUN apt-get update && apt-get upgrade -y && apt-get install -y git
-
-# create a new conda environment named azureml
-RUN conda create -n azureml -y -q Python=3.6
-
-# install additional packages used by sample notebooks. this is optional
-RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
-
-# install azurmel-sdk components
-RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.21"]
-
-# clone Azure ML GitHub sample notebooks
-RUN cd /home && git clone -b "azureml-sdk-1.0.21" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
-
-# generate jupyter configuration file
-RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
-
-# set an emtpy token for Jupyter to remove authentication. 
-# this is NOT recommended for production environment
-RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
-
-# open up port 8887 on the container
-EXPOSE 8887
-
-# start Jupyter notebook server on port 8887 when the container starts
-CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"

Dockerfiles/1.0.23/Dockerfile

@@ -1,29 +0,0 @@
-FROM continuumio/miniconda:4.5.11
-
-# install git
-RUN apt-get update && apt-get upgrade -y && apt-get install -y git
-
-# create a new conda environment named azureml
-RUN conda create -n azureml -y -q Python=3.6
-
-# install additional packages used by sample notebooks. this is optional
-RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
-
-# install azurmel-sdk components
-RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.23"]
-
-# clone Azure ML GitHub sample notebooks
-RUN cd /home && git clone -b "azureml-sdk-1.0.23" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
-
-# generate jupyter configuration file
-RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
-
-# set an emtpy token for Jupyter to remove authentication. 
-# this is NOT recommended for production environment
-RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
-
-# open up port 8887 on the container
-EXPOSE 8887
-
-# start Jupyter notebook server on port 8887 when the container starts
-CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"

Dockerfiles/1.0.30/Dockerfile

@@ -1,29 +0,0 @@
-FROM continuumio/miniconda:4.5.11
-
-# install git
-RUN apt-get update && apt-get upgrade -y && apt-get install -y git
-
-# create a new conda environment named azureml
-RUN conda create -n azureml -y -q Python=3.6
-
-# install additional packages used by sample notebooks. this is optional
-RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
-
-# install azurmel-sdk components
-RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.30"]
-
-# clone Azure ML GitHub sample notebooks
-RUN cd /home && git clone -b "azureml-sdk-1.0.30" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
-
-# generate jupyter configuration file
-RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
-
-# set an emtpy token for Jupyter to remove authentication. 
-# this is NOT recommended for production environment
-RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
-
-# open up port 8887 on the container
-EXPOSE 8887
-
-# start Jupyter notebook server on port 8887 when the container starts
-CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"

Dockerfiles/1.0.33/Dockerfile

@@ -1,29 +0,0 @@
-FROM continuumio/miniconda:4.5.11
-
-# install git
-RUN apt-get update && apt-get upgrade -y && apt-get install -y git
-
-# create a new conda environment named azureml
-RUN conda create -n azureml -y -q Python=3.6
-
-# install additional packages used by sample notebooks. this is optional
-RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
-
-# install azurmel-sdk components
-RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.33"]
-
-# clone Azure ML GitHub sample notebooks
-RUN cd /home && git clone -b "azureml-sdk-1.0.33" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
-
-# generate jupyter configuration file
-RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
-
-# set an emtpy token for Jupyter to remove authentication. 
-# this is NOT recommended for production environment
-RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
-
-# open up port 8887 on the container
-EXPOSE 8887
-
-# start Jupyter notebook server on port 8887 when the container starts
-CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"

Dockerfiles/1.0.41/Dockerfile

@@ -1,29 +0,0 @@
-FROM continuumio/miniconda:4.5.11
-
-# install git
-RUN apt-get update && apt-get upgrade -y && apt-get install -y git
-
-# create a new conda environment named azureml
-RUN conda create -n azureml -y -q Python=3.6
-
-# install additional packages used by sample notebooks. this is optional
-RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
-
-# install azurmel-sdk components
-RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.41"]
-
-# clone Azure ML GitHub sample notebooks
-RUN cd /home && git clone -b "azureml-sdk-1.0.41" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
-
-# generate jupyter configuration file
-RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
-
-# set an emtpy token for Jupyter to remove authentication. 
-# this is NOT recommended for production environment
-RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
-
-# open up port 8887 on the container
-EXPOSE 8887
-
-# start Jupyter notebook server on port 8887 when the container starts
-CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"

Dockerfiles/1.0.43/Dockerfile

@@ -1,29 +0,0 @@
-FROM continuumio/miniconda:4.5.11
-
-# install git
-RUN apt-get update && apt-get upgrade -y && apt-get install -y git
-
-# create a new conda environment named azureml
-RUN conda create -n azureml -y -q Python=3.6
-
-# install additional packages used by sample notebooks. this is optional
-RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
-
-# install azurmel-sdk components
-RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.43"]
-
-# clone Azure ML GitHub sample notebooks
-RUN cd /home && git clone -b "azureml-sdk-1.0.43" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
-
-# generate jupyter configuration file
-RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
-
-# set an emtpy token for Jupyter to remove authentication. 
-# this is NOT recommended for production environment
-RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
-
-# open up port 8887 on the container
-EXPOSE 8887
-
-# start Jupyter notebook server on port 8887 when the container starts
-CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"

Dockerfiles/1.0.6/Dockerfile

@@ -1,29 +0,0 @@
-FROM continuumio/miniconda:4.5.11
-
-# install git
-RUN apt-get update && apt-get upgrade -y && apt-get install -y git
-
-# create a new conda environment named azureml
-RUN conda create -n azureml -y -q Python=3.6
-
-# install additional packages used by sample notebooks. this is optional
-RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
-
-# install azurmel-sdk components
-RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.6"]
-
-# clone Azure ML GitHub sample notebooks
-RUN cd /home && git clone -b "azureml-sdk-1.0.6" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
-
-# generate jupyter configuration file
-RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
-
-# set an emtpy token for Jupyter to remove authentication. 
-# this is NOT recommended for production environment
-RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
-
-# open up port 8887 on the container
-EXPOSE 8887
-
-# start Jupyter notebook server on port 8887 when the container starts
-CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"

Dockerfiles/1.0.8/Dockerfile

@@ -1,29 +0,0 @@
-FROM continuumio/miniconda:4.5.11
-
-# install git
-RUN apt-get update && apt-get upgrade -y && apt-get install -y git
-
-# create a new conda environment named azureml
-RUN conda create -n azureml -y -q Python=3.6
-
-# install additional packages used by sample notebooks. this is optional
-RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
-
-# install azurmel-sdk components
-RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.8"]
-
-# clone Azure ML GitHub sample notebooks
-RUN cd /home && git clone -b "azureml-sdk-1.0.8" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
-
-# generate jupyter configuration file
-RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
-
-# set an emtpy token for Jupyter to remove authentication. 
-# this is NOT recommended for production environment
-RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
-
-# open up port 8887 on the container
-EXPOSE 8887
-
-# start Jupyter notebook server on port 8887 when the container starts
-CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"

Licenses/sdk-license/LICENSE

@@ -1,14 +0,0 @@
-    
-This software is made available to you on the condition that you agree to
-[your agreement][1] governing your use of Azure.
-If you do not have an existing agreement governing your use of Azure, you agree that 
-your agreement governing use of Azure is the [Microsoft Online Subscription Agreement][2]
-(which incorporates the [Online Services Terms][3]).
-By using the software you agree to these terms. This software may collect data
-that is transmitted to Microsoft. Please see the [Microsoft Privacy Statement][4]
-to learn more about how Microsoft processes personal data.
-
-[1]: https://azure.microsoft.com/en-us/support/legal/
-[2]: https://azure.microsoft.com/en-us/support/legal/subscription-agreement/
-[3]: http://www.microsoftvolumelicensing.com/DocumentSearch.aspx?Mode=3&DocumentTypeId=46
-[4]: http://go.microsoft.com/fwlink/?LinkId=248681 

NBSETUP.md

@@ -1,95 +0,0 @@
-# Set up your notebook environment for Azure Machine Learning
-
-To run the notebooks in this repository use one of following options.
-
-## **Option 1: Use Azure Notebooks**
-Azure Notebooks is a hosted Jupyter-based notebook service in the Azure cloud. Azure Machine Learning Python SDK is already pre-installed in the Azure Notebooks `Python 3.6` kernel.
-
-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 [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 by choosing Kernel > Change Kernel > Python 3.6 from the menus.
-
-## **Option 2: Use your own notebook server**
-
-### Quick installation
-We recommend you create a Python virtual environment ([Miniconda](https://conda.io/miniconda.html) preferred but [virtualenv](https://virtualenv.pypa.io/en/latest/) works too) and install the SDK in it.
-```sh
-# install just the base SDK
-pip install azureml-sdk
-
-# clone the sample repoistory
-git clone https://github.com/Azure/MachineLearningNotebooks.git
-
-# below steps are optional
-# install the base SDK, Jupyter notebook server and tensorboard
-pip install azureml-sdk[notebooks,tensorboard]
-
-# install model explainability component
-pip install azureml-sdk[interpret]
-
-# install automated ml components
-pip install azureml-sdk[automl]
-
-# install experimental features (not ready for production use)
-pip install azureml-sdk[contrib]
-```
-
-Note the _extras_ (the keywords inside the square brackets) can be combined. For example:
-```sh
-# install base SDK, Jupyter notebook and automated ml components
-pip install azureml-sdk[notebooks,automl]
-```
-
-### Full instructions
-[Install the Azure Machine Learning SDK](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python)
-
-Please make sure you start with the [Configuration](configuration.ipynb) notebook to create and connect to a workspace.
-
-
-### Video walkthrough:
-
-[!VIDEO https://youtu.be/VIsXeTuW3FU]
-
-## **Option 3: Use Docker**
-
-You need to have Docker engine installed locally and running. Open a command line window and type the following command. 
-
-__Note:__ We use version `1.0.10` below as an exmaple, but you can replace that with any available version number you like.
-
-```sh
-# clone the sample repoistory
-git clone https://github.com/Azure/MachineLearningNotebooks.git
-
-# change current directory to the folder 
-# where Dockerfile of the specific SDK version is located.
-cd MachineLearningNotebooks/Dockerfiles/1.0.10
-
-# build a Docker image with the a name (azuremlsdk for example) 
-# and a version number tag (1.0.10 for example).
-# this can take several minutes depending on your computer speed and network bandwidth.
-docker build . -t azuremlsdk:1.0.10
-
-# launch the built Docker container which also automatically starts
-# a Jupyter server instance listening on port 8887 of the host machine
-docker run -it -p 8887:8887 azuremlsdk:1.0.10
-```
-
-Now you can point your browser to http://localhost:8887. We recommend that you start from the `configuration.ipynb` notebook at the root directory.
-
-If you need additional Azure ML SDK components, you can either modify the Docker files before you build the Docker images to add additional steps, or install them through command line in the live container after you build the Docker image. For example:
-
-```sh
-# install the core SDK and automated ml components
-pip install azureml-sdk[automl]
-
-# install the core SDK and model explainability component
-pip install azureml-sdk[interpret]
-
-# install the core SDK and experimental components
-pip install azureml-sdk[contrib]
-```
-Drag and Drop
-The image will be downloaded by Fatkun

README.md

@@ -1,43 +1,47 @@
-# Azure Machine Learning Python SDK notebooks
+Get the full documentation for Azure Machine Learning service at:
 
-### **With the introduction of AzureML SDK v2, this samples repository for the v1 SDK is now deprecated and will not be monitored or updated. Users are encouraged to visit the [v2 SDK samples repository](https://github.com/Azure/azureml-examples) instead for up-to-date and enhanced examples of how to build, train, and deploy machine learning models with AzureML's newest features.**
+https://docs.microsoft.com/azure/machine-learning/service/
 
-Welcome to the Azure Machine Learning Python SDK notebooks repository!
+<br>
 
-## Getting started
+# Sample notebooks for Azure Machine Learning service
 
-These notebooks are recommended for use in an Azure Machine Learning [Compute Instance](https://docs.microsoft.com/azure/machine-learning/concept-compute-instance), where you can run them without any additional set up.
+To run the notebooks in this repository use one of these methods:
 
-However, the notebooks can be run in any development environment with the correct `azureml` packages installed.
+## Use Azure Notebooks - Jupyter based notebooks in the Azure cloud
 
-Install the `azureml.core` Python package:
+1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
+[Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks.
+1. Follow the instructions in the [00.configuration](00.configuration.ipynb) notebook to create and connect to a workspace.
+1. Open one of the sample notebooks.
     
-```sh
-pip install azureml-core
-```
+    **Make sure the Azure Notebook kernel is set to `Python 3.6`** when you open a notebook.  
     
-Install additional packages as needed:
+    ![set kernel to Python 3.6](images/python36.png)
 
-```sh
-pip install azureml-mlflow
-pip install azureml-dataset-runtime
-pip install azureml-automl-runtime
-pip install azureml-pipeline
-pip install azureml-pipeline-steps
-...
-```
 
-We recommend starting with one of the [quickstarts](tutorials/compute-instance-quickstarts).
+## **Use your own notebook server**
 
-## Contributing
+1. Setup a Jupyter Notebook server and [install the Azure Machine Learning SDK](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python).
+1. Clone [this repository](https://aka.ms/aml-notebooks).
+1. You may need to install other packages for specific notebooks
+1. Start your notebook server.
+1. Follow the instructions in the [00.configuration](00.configuration.ipynb) notebook to create and connect to a workspace.
+1. Open one of the sample notebooks.
 
-This repository is a push-only mirror. Pull requests are ignored.
+> Note: **Looking for automated machine learning samples?**
+> For your convenience, you can use an installation script instead of the steps below for the automated ML notebooks. Go to the [automl folder README](automl/README.md) and follow the instructions.  The script installs all  packages needed for notebooks in that folder.
 
-## Code of Conduct
+# Contributing
 
-This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). Please see the [code of conduct](CODE_OF_CONDUCT.md) for details.
+This project welcomes contributions and suggestions.  Most contributions require you to agree to a
+Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
+the rights to use your contribution. For details, visit https://cla.microsoft.com.
 
-## Reference
-
-- [Documentation](https://docs.microsoft.com/azure/machine-learning)
+When you submit a pull request, a CLA-bot will automatically determine whether you need to provide
+a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions
+provided by the bot. You will only need to do this once across all repos using our CLA.
 
+This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
+For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
+contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

SECURITY.md

@@ -1,41 +0,0 @@
-<!-- BEGIN MICROSOFT SECURITY.MD V0.0.7 BLOCK -->
-
-## Security
-
-Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), and [our GitHub organizations](https://opensource.microsoft.com/).
-
-If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://aka.ms/opensource/security/definition), please report it to us as described below.
-
-## Reporting Security Issues
-
-**Please do not report security vulnerabilities through public GitHub issues.**
-
-Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://aka.ms/opensource/security/create-report).
-
-If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com).  If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://aka.ms/opensource/security/pgpkey).
-
-You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://aka.ms/opensource/security/msrc). 
-
-Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue:
-
-  * Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.)
-  * Full paths of source file(s) related to the manifestation of the issue
-  * The location of the affected source code (tag/branch/commit or direct URL)
-  * Any special configuration required to reproduce the issue
-  * Step-by-step instructions to reproduce the issue
-  * Proof-of-concept or exploit code (if possible)
-  * Impact of the issue, including how an attacker might exploit the issue
-
-This information will help us triage your report more quickly.
-
-If you are reporting for a bug bounty, more complete reports can contribute to a higher bounty award. Please visit our [Microsoft Bug Bounty Program](https://aka.ms/opensource/security/bounty) page for more details about our active programs.
-
-## Preferred Languages
-
-We prefer all communications to be in English.
-
-## Policy
-
-Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://aka.ms/opensource/security/cvd).
-
-<!-- END MICROSOFT SECURITY.MD BLOCK -->

aml_config/conda_dependencies.yml

@@ -0,0 +1,15 @@
+# Conda environment specification. The dependencies defined in this file will
+
+# be automatically provisioned for runs with userManagedDependencies=False.
+
+
+# Details about the Conda environment file format:
+
+# https://conda.io/docs/user-guide/tasks/manage-environments.html#create-env-file-manually
+
+
+name: project_environment
+dependencies:
+  # The python interpreter version.
+
+  # Currently Azure ML only supports 3.5.2 and later.

aml_config/docker.runconfig

@@ -0,0 +1,115 @@
+# The script to run.
+script: train.py
+# The arguments to the script file.
+arguments: []
+# The name of the compute target to use for this run.
+target: local
+# Framework to execute inside. Allowed values are "Python" ,  "PySpark", "CNTK",  "TensorFlow", and "PyTorch".
+framework: PySpark
+# Communicator for the given framework. Allowed values are "None" ,  "ParameterServer", "OpenMpi", and "IntelMpi".
+communicator: None
+# Automatically prepare the run environment as part of the run itself.
+autoPrepareEnvironment: true
+# Maximum allowed duration for the run.
+maxRunDurationSeconds:
+# Number of nodes to use for running job.
+nodeCount: 1
+# Environment details.
+environment:
+# Environment variables set for the run.
+  environmentVariables:
+    EXAMPLE_ENV_VAR: EXAMPLE_VALUE
+# Python details
+  python:
+# user_managed_dependencies=True indicates that the environmentwill be user managed. False indicates that AzureML willmanage the user environment.
+    userManagedDependencies: false
+# The python interpreter path
+    interpreterPath: python
+# Path to the conda dependencies file to use for this run. If a project
+# contains multiple programs with different sets of dependencies, it may be
+# convenient to manage those environments with separate files.
+    condaDependenciesFile: aml_config/conda_dependencies.yml
+# Docker details
+  docker:
+# Set True to perform this run inside a Docker container.
+    enabled: true
+# Base image used for Docker-based runs.
+    baseImage: mcr.microsoft.com/azureml/base:0.2.0
+# Set False if necessary to work around shared volume bugs.
+    sharedVolumes: true
+# Run with NVidia Docker extension to support GPUs.
+    gpuSupport: false
+# Extra arguments to the Docker run command.
+    arguments: []
+# Image registry that contains the base image.
+    baseImageRegistry:
+# DNS name or IP address of azure container registry(ACR)
+      address:
+# The username for ACR
+      username:
+# The password for ACR
+      password:
+# Spark details
+  spark:
+# List of spark repositories.
+    repositories:
+    - https://mmlspark.azureedge.net/maven
+    packages:
+    - group: com.microsoft.ml.spark
+      artifact: mmlspark_2.11
+      version: '0.12'
+    precachePackages: true
+# Databricks details
+  databricks:
+# List of maven libraries.
+    mavenLibraries: []
+# List of PyPi libraries
+    pypiLibraries: []
+# List of RCran libraries
+    rcranLibraries: []
+# List of JAR libraries
+    jarLibraries: []
+# List of Egg libraries
+    eggLibraries: []
+# History details.
+history:
+# Enable history tracking -- this allows status, logs, metrics, and outputs
+# to be collected for a run.
+  outputCollection: true
+# whether to take snapshots for history.
+  snapshotProject: true
+# Spark configuration details.
+spark:
+  configuration:
+    spark.app.name: Azure ML Experiment
+    spark.yarn.maxAppAttempts: 1
+# HDI details.
+hdi:
+# Yarn deploy mode. Options are cluster and client.
+  yarnDeployMode: cluster
+# Tensorflow details.
+tensorflow:
+# The number of worker tasks.
+  workerCount: 1
+# The number of parameter server tasks.
+  parameterServerCount: 1
+# Mpi details.
+mpi:
+# When using MPI, number of processes per node.
+  processCountPerNode: 1
+# data reference configuration details
+dataReferences: {}
+# Project share datastore reference.
+sourceDirectoryDataStore:
+# AmlCompute details.
+amlcompute:
+# VM size of the Cluster to be created.Allowed values are Azure vm sizes.The list of vm sizes is available in 'https://docs.microsoft.com/en-us/azure/cloud-services/cloud-services-sizes-specs
+  vmSize:
+# VM priority of the Cluster to be created.Allowed values are "dedicated" , "lowpriority".
+  vmPriority:
+# A bool that indicates if the cluster has to be retained after job completion.
+  retainCluster: false
+# Name of the cluster to be created. If not specified, runId will be used as cluster name.
+  name:
+# Maximum number of nodes in the AmlCompute cluster to be created. Minimum number of nodes will always be set to 0.
+  clusterMaxNodeCount: 1

aml_config/local.runconfig

@@ -0,0 +1,115 @@
+# The script to run.
+script: train.py
+# The arguments to the script file.
+arguments: []
+# The name of the compute target to use for this run.
+target: local
+# Framework to execute inside. Allowed values are "Python" ,  "PySpark", "CNTK",  "TensorFlow", and "PyTorch".
+framework: Python
+# Communicator for the given framework. Allowed values are "None" ,  "ParameterServer", "OpenMpi", and "IntelMpi".
+communicator: None
+# Automatically prepare the run environment as part of the run itself.
+autoPrepareEnvironment: true
+# Maximum allowed duration for the run.
+maxRunDurationSeconds:
+# Number of nodes to use for running job.
+nodeCount: 1
+# Environment details.
+environment:
+# Environment variables set for the run.
+  environmentVariables:
+    EXAMPLE_ENV_VAR: EXAMPLE_VALUE
+# Python details
+  python:
+# user_managed_dependencies=True indicates that the environmentwill be user managed. False indicates that AzureML willmanage the user environment.
+    userManagedDependencies: false
+# The python interpreter path
+    interpreterPath: python
+# Path to the conda dependencies file to use for this run. If a project
+# contains multiple programs with different sets of dependencies, it may be
+# convenient to manage those environments with separate files.
+    condaDependenciesFile: aml_config/conda_dependencies.yml
+# Docker details
+  docker:
+# Set True to perform this run inside a Docker container.
+    enabled: false
+# Base image used for Docker-based runs.
+    baseImage: mcr.microsoft.com/azureml/base:0.2.0
+# Set False if necessary to work around shared volume bugs.
+    sharedVolumes: true
+# Run with NVidia Docker extension to support GPUs.
+    gpuSupport: false
+# Extra arguments to the Docker run command.
+    arguments: []
+# Image registry that contains the base image.
+    baseImageRegistry:
+# DNS name or IP address of azure container registry(ACR)
+      address:
+# The username for ACR
+      username:
+# The password for ACR
+      password:
+# Spark details
+  spark:
+# List of spark repositories.
+    repositories:
+    - https://mmlspark.azureedge.net/maven
+    packages:
+    - group: com.microsoft.ml.spark
+      artifact: mmlspark_2.11
+      version: '0.12'
+    precachePackages: true
+# Databricks details
+  databricks:
+# List of maven libraries.
+    mavenLibraries: []
+# List of PyPi libraries
+    pypiLibraries: []
+# List of RCran libraries
+    rcranLibraries: []
+# List of JAR libraries
+    jarLibraries: []
+# List of Egg libraries
+    eggLibraries: []
+# History details.
+history:
+# Enable history tracking -- this allows status, logs, metrics, and outputs
+# to be collected for a run.
+  outputCollection: true
+# whether to take snapshots for history.
+  snapshotProject: true
+# Spark configuration details.
+spark:
+  configuration:
+    spark.app.name: Azure ML Experiment
+    spark.yarn.maxAppAttempts: 1
+# HDI details.
+hdi:
+# Yarn deploy mode. Options are cluster and client.
+  yarnDeployMode: cluster
+# Tensorflow details.
+tensorflow:
+# The number of worker tasks.
+  workerCount: 1
+# The number of parameter server tasks.
+  parameterServerCount: 1
+# Mpi details.
+mpi:
+# When using MPI, number of processes per node.
+  processCountPerNode: 1
+# data reference configuration details
+dataReferences: {}
+# Project share datastore reference.
+sourceDirectoryDataStore:
+# AmlCompute details.
+amlcompute:
+# VM size of the Cluster to be created.Allowed values are Azure vm sizes.The list of vm sizes is available in 'https://docs.microsoft.com/en-us/azure/cloud-services/cloud-services-sizes-specs
+  vmSize:
+# VM priority of the Cluster to be created.Allowed values are "dedicated" , "lowpriority".
+  vmPriority:
+# A bool that indicates if the cluster has to be retained after job completion.
+  retainCluster: false
+# Name of the cluster to be created. If not specified, runId will be used as cluster name.
+  name:
+# Maximum number of nodes in the AmlCompute cluster to be created. Minimum number of nodes will always be set to 0.
+  clusterMaxNodeCount: 1

aml_config/project.json

@@ -0,0 +1 @@
+{"Id": "local-compute", "Scope": "/subscriptions/65a1016d-0f67-45d2-b838-b8f373d6d52e/resourceGroups/sheri/providers/Microsoft.MachineLearningServices/workspaces/sheritestqs3/projects/local-compute"}

automl/00.configuration.ipynb

@@ -0,0 +1,224 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# AutoML 00. Configuration\n",
+        "\n",
+        "In this example you will create an Azure Machine Learning `Workspace` object and initialize your notebook directory to easily reload this object from a configuration file.  Typically you will only need to run this once per notebook directory, and all other notebooks in this directory or any sub-directories will automatically use the settings you indicate here.\n",
+        "\n",
+        "\n",
+        "## Prerequisites:\n",
+        "\n",
+        "Before running this notebook, run the `automl_setup` script described in README.md.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Register Machine Learning Services Resource Provider\n",
+        "\n",
+        "Microsoft.MachineLearningServices only needs to be registed once in the subscription.\n",
+        "To register it:\n",
+        "1. Start the Azure portal.\n",
+        "2. Select your `All services` and then `Subscription`.\n",
+        "3. Select the subscription that you want to use.\n",
+        "4. Click on `Resource providers`\n",
+        "3. Click the `Register` link next to Microsoft.MachineLearningServices"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Check the Azure ML Core SDK Version to Validate Your Installation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK Version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize an Azure ML Workspace\n",
+        "### What is an Azure ML Workspace and Why Do I Need One?\n",
+        "\n",
+        "An Azure ML workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an Azure ML workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, operationalization, and the monitoring of operationalized models.\n",
+        "\n",
+        "\n",
+        "### What do I Need?\n",
+        "\n",
+        "To create or access an Azure ML workspace, you will need to import the Azure ML library and specify following information:\n",
+        "* A name for your workspace. You can choose one.\n",
+        "* Your subscription id. Use the `id` value from the `az account show` command output above.\n",
+        "* The resource group name. The resource group organizes Azure resources and provides a default region for the resources in the group. The resource group will be created if it doesn't exist. Resource groups can be created and viewed in the [Azure portal](https://portal.azure.com)\n",
+        "* Supported regions include `eastus2`, `eastus`,`westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "subscription_id = \"<subscription_id>\"\n",
+        "resource_group = \"myrg\"\n",
+        "workspace_name = \"myws\"\n",
+        "workspace_region = \"eastus2\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Creating a Workspace\n",
+        "If you already have access to an Azure ML workspace you want to use, you can skip this cell.  Otherwise, this cell will create an Azure ML workspace for you in the specified subscription, provided you have the correct permissions for the given `subscription_id`.\n",
+        "\n",
+        "This will fail when:\n",
+        "1. The workspace already exists.\n",
+        "2. You do not have permission to create a workspace in the resource group.\n",
+        "3. You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription.\n",
+        "\n",
+        "If workspace creation fails for any reason other than already existing, please work with your IT administrator to provide you with the appropriate permissions or to provision the required resources.\n",
+        "\n",
+        "**Note:** Creation of a new workspace can take several minutes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Import the Workspace class and check the Azure ML SDK version.\n",
+        "from azureml.core import Workspace\n",
+        "\n",
+        "ws = Workspace.create(name = workspace_name,\n",
+        "                      subscription_id = subscription_id,\n",
+        "                      resource_group = resource_group, \n",
+        "                      location = workspace_region)\n",
+        "ws.get_details()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Configuring Your Local Environment\n",
+        "You can validate that you have access to the specified workspace and write a configuration file to the default configuration location, `./aml_config/config.json`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace\n",
+        "\n",
+        "ws = Workspace(workspace_name = workspace_name,\n",
+        "               subscription_id = subscription_id,\n",
+        "               resource_group = resource_group)\n",
+        "\n",
+        "# Persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
+        "ws.write_config()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can then load the workspace from this config file from any notebook in the current directory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Load workspace configuration from ./aml_config/config.json file.\n",
+        "my_workspace = Workspace.from_config()\n",
+        "my_workspace.get_details()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create a Folder to Host All Sample Projects\n",
+        "Finally, create a folder where all the sample projects will be hosted."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "sample_projects_folder = './sample_projects'\n",
+        "\n",
+        "if not os.path.isdir(sample_projects_folder):\n",
+        "    os.mkdir(sample_projects_folder)\n",
+        "    \n",
+        "print('Sample projects will be created in {}.'.format(sample_projects_folder))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Success!\n",
+        "Great, you are ready to move on to the rest of the sample notebooks."
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "savitam"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

automl/01.auto-ml-classification.ipynb

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

automl/02.auto-ml-regression.ipynb

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

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

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

automl/03b.auto-ml-remote-batchai.ipynb

@@ -0,0 +1,511 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# AutoML 03: Remote Execution using 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 [00.configuration](00.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 BatchAiCompute\n",
+        "from azureml.core.compute import ComputeTarget\n",
+        "\n",
+        "# Choose a name for your cluster.\n",
+        "batchai_cluster_name = \"mybatchai\"\n",
+        "\n",
+        "found = False\n",
+        "# Check if this compute target already exists in the workspace.\n",
+        "for ct_name, ct in ws.compute_targets().items():\n",
+        "    print(ct.name, ct.type)\n",
+        "    if (ct.name == batchai_cluster_name and ct.type == 'BatchAI'):\n",
+        "        found = True\n",
+        "        print('Found existing compute target.')\n",
+        "        compute_target = ct\n",
+        "        break\n",
+        "        \n",
+        "if not found:\n",
+        "    print('Creating a new compute target...')\n",
+        "    provisioning_config = BatchAiCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
+        "                                                                #vm_priority = 'lowpriority', # optional\n",
+        "                                                                autoscale_enabled = True,\n",
+        "                                                                cluster_min_nodes = 1, \n",
+        "                                                                cluster_max_nodes = 4)\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": "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>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
+        "|**max_time_sec**|Time limit in seconds for each iteration.|\n",
+        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+        "|**n_cross_validations**|Number of cross validation splits.|\n",
+        "|**concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of cores on the DSVM.|"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "automl_settings = {\n",
+        "    \"max_time_sec\": 120,\n",
+        "    \"iterations\": 20,\n",
+        "    \"n_cross_validations\": 5,\n",
+        "    \"primary_metric\": 'AUC_weighted',\n",
+        "    \"preprocess\": False,\n",
+        "    \"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",
+        "                             compute_target = compute_target,\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.train.widgets import RunDetails\n",
+        "RunDetails(remote_run).show() "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Wait until the run finishes.\n",
+        "remote_run.wait_for_completion(show_output = True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n",
+        "#### Retrieve All Child Runs\n",
+        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "children = list(remote_run.get_children())\n",
+        "metricslist = {}\n",
+        "for run in children:\n",
+        "    properties = run.get_properties()\n",
+        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+        "    metricslist[int(properties['iteration'])] = metrics\n",
+        "\n",
+        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+        "rundata"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Cancelling Runs\n",
+        "\n",
+        "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Cancel the ongoing experiment and stop scheduling new iterations.\n",
+        "# remote_run.cancel()\n",
+        "\n",
+        "# Cancel iteration 1 and move onto iteration 2.\n",
+        "# remote_run.cancel_iteration(1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Retrieve the Best Model\n",
+        "\n",
+        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "best_run, fitted_model = remote_run.get_output()\n",
+        "print(best_run)\n",
+        "print(fitted_model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Best Model Based on Any Other Metric\n",
+        "Show the run and the model which has the smallest `log_loss` value:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "lookup_metric = \"log_loss\"\n",
+        "best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
+        "print(best_run)\n",
+        "print(fitted_model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Model from a Specific Iteration\n",
+        "Show the run and the model from the third iteration:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "iteration = 3\n",
+        "third_run, third_model = remote_run.get_output(iteration=iteration)\n",
+        "print(third_run)\n",
+        "print(third_model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 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",
+      "version": "3.6.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

automl/13.auto-ml-dataprep.ipynb

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

automl/README.md

@@ -0,0 +1,232 @@
+# Table of Contents
+1. [Auto ML Introduction](#introduction)
+2. [Running samples in a Local Conda environment](#localconda)
+3. [Auto ML SDK Sample Notebooks](#samples)
+4. [Documentation](#documentation)
+5. [Running using python command](#pythoncommand)
+6. [Troubleshooting](#troubleshooting)
+
+# Auto ML Introduction <a name="introduction"></a>
+AutoML builds high quality Machine Learning models for you by automating model and hyperparameter selection. Bring a labelled dataset that you want to build a model for, AutoML will give you a high quality machine learning model that you can use for predictions.
+
+If you are new to Data Science, AutoML will help you get jumpstarted by simplifying machine learning model building. It abstracts you from needing to perform model selection, hyperparameter selection and in one step creates a high quality trained model for you to use.
+
+If you are an experienced data scientist, AutoML will help increase your productivity by intelligently performing the model and hyperparameter selection for your training and generates high quality models much quicker than manually specifying several combinations of the parameters and running training jobs. AutoML provides visibility and access to all the training jobs and the performance characteristics of the models to help you further tune the pipeline if you desire.
+
+
+# Running samples in a Local Conda environment <a name="localconda"></a>
+
+You can run these notebooks in Azure Notebooks without any extra installation.  To run these notebook on your own notebook server, use these installation instructions.
+
+It is best if you create a new conda environment locally to try this SDK, so it doesn't mess up with your existing Python environment. 
+
+### 1. Install mini-conda from [here](https://conda.io/miniconda.html), choose Python 3.7 or higher. 
+- **Note**: if you already have conda installed, you can keep using it but it should be version 4.4.10 or later (as shown by: conda -V).  If you have a previous version installed, you can update it using the command: conda update conda.
+There's no need to install mini-conda specifically.
+
+### 2. Downloading the sample notebooks
+- Download the sample notebooks from [GitHub](https://github.com/Azure/MachineLearningNotebooks) as zip and extract the contents to a local directory.  The AutoML sample notebooks are in the "automl" folder.
+
+### 3. Setup a new conda environment
+The **automl/automl_setup** script creates a new conda environment, installs the necessary packages, configures the widget and starts a jupyter notebook.
+It takes the conda environment name as an optional parameter.  The default conda environment name is azure_automl.  The exact command depends on the operating system.  It can take about 30 minutes to execute.
+## Windows
+Start a conda command windows, cd to the **automl** folder where the sample notebooks were extracted and then run: 
+```
+automl_setup
+```
+## Mac
+Install "Command line developer tools" if it is not already installed (you can use the command: `xcode-select --install`).
+
+Start a Terminal windows, cd to the **automl** folder where the sample notebooks were extracted and then run: 
+
+```
+bash automl_setup_mac.sh
+```
+
+## Linux
+cd to the **automl** folder where the sample notebooks were extracted and then run: 
+
+```
+automl_setup_linux.sh
+```
+
+### 4. Running configuration.ipynb
+- Before running any samples you next need to run the configuration notebook. Click on 00.configuration.ipynb notebook
+- Please make sure you use the Python [conda env:azure_automl] kernel when running this notebook.
+- Execute the cells in the notebook to Register Machine Learning Services Resource Provider and create a workspace. (*instructions in notebook*)
+
+### 5. Running Samples
+- Please make sure you use the Python [conda env:azure_automl] kernel when trying the sample Notebooks.
+- Follow the instructions in the individual notebooks to explore various features in AutoML
+
+# Auto ML SDK Sample Notebooks <a name="samples"></a>
+- [00.configuration.ipynb](00.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)
+    - 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)
+    - 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)
+    - 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
+    - Async tracking of progress
+    - Cancelling individual iterations or entire run
+    - 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)
+    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
+    - Example of using Auto ML for classification using a remote Batch AI compute for training
+    - Parallel execution of iterations
+    - Async tracking of progress
+    - Cancelling individual iterations or entire run
+    - 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)
+    - 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)
+    - 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-custom-cv-split.ipynb](06.auto-ml-sparse-data-custom-cv-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)
+    - List all projects for the workspace
+    - List all AutoML Runs for a given project
+    - Get details for a AutoML Run. (Automl settings, run widget & all metrics)
+    - Downlaod fitted pipeline for any iteration
+
+- [08.auto-ml-remote-execution-with-text-file-on-DSVM](08.auto-ml-remote-execution-with-text-file-on-DSVM.ipynb)
+    - Dataset: scikit learn's [digit dataset](https://innovate.burningman.org/datasets-page/)
+    - Download the data and store it in the DSVM to improve performance.
+
+- [09.auto-ml-classification-with-deployment.ipynb](09.auto-ml-classification-with-deployment.ipynb)
+    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
+    - 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)
+    - 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)
+    - How to get current and training env SDK versions
+
+- [13.auto-ml-dataprep.ipynb](13.auto-ml-dataprep.ipynb)
+    - Using DataPrep for reading data
+
+- [14a.auto-ml-classification-ensemble.ipynb](14a.auto-ml-classification-ensemble.ipynb)
+    - Classification with ensembling
+
+- [14b.auto-ml-regression-ensemble.ipynb](14b.auto-ml-regression-ensemble.ipynb)
+    - Regression with ensembling
+
+# Documentation <a name="documentation"></a>
+## Table of Contents
+1. [Auto ML Settings ](#automlsettings)
+2. [Cross validation split options](#cvsplits)
+3. [Get Data Syntax](#getdata)
+4. [Data pre-processing and featurization](#preprocessing)
+
+## Auto ML Settings <a name="automlsettings"></a>
+|Property|Description|Default|
+|-|-|-|
+|**primary_metric**|This is the metric that you want to optimize.<br><br> Classification supports the following primary metrics <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i><br><br> Regression supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i><br><i>normalized_root_mean_squared_log_error</i>| Classification: accuracy <br><br> Regression: spearman_correlation
+|**max_time_sec**|Time limit in seconds for each iteration|None|
+|**iterations**|Number of iterations. In each iteration trains the data with a specific pipeline.  To get the best result, use at least 100. |100|
+|**n_cross_validations**|Number of cross validation splits|None|
+|**validation_size**|Size of validation set as percentage of all training samples|None|
+|**concurrent_iterations**|Max number of iterations that would be executed in parallel|1|
+|**preprocess**|*True/False* <br>Setting this to *True* enables preprocessing <br>on the input to handle missing data, and perform some common feature extraction<br>*Note: If input data is Sparse you cannot use preprocess=True*|False|
+|**max_cores_per_iteration**| Indicates how many cores on the compute target would be used to train a single pipeline.<br> You can set it to *-1* to use all cores|1|
+|**exit_score**|*double* value indicating the target for *primary_metric*. <br> Once the target is surpassed the run terminates|None|
+|**blacklist_algos**|*Array* of *strings* indicating pipelines to ignore for Auto ML.<br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGDClassifierWrapper</i><br><i>NBWrapper</i><br><i>BernoulliNB</i><br><i>SVCWrapper</i><br><i>LinearSVMWrapper</i><br><i>KNeighborsClassifier</i><br><i>DecisionTreeClassifier</i><br><i>RandomForestClassifier</i><br><i>ExtraTreesClassifier</i><br><i>gradient boosting</i><br><i>LightGBMClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoostingRegressor</i><br><i>DecisionTreeRegressor</i><br><i>KNeighborsRegressor</i><br><i>LassoLars</i><br><i>SGDRegressor</i><br><i>RandomForestRegressor</i><br><i>ExtraTreesRegressor</i>|None|
+
+## Cross validation split options <a name="cvsplits"></a>
+### K-Folds Cross Validation
+Use *n_cross_validations* setting to specify the number of cross validations. The training data set will be randomly split into *n_cross_validations* folds of equal size. During each cross validation round, one of the folds will be used for validation of the model trained on the remaining folds. This process repeats for *n_cross_validations* rounds until each fold is used once as validation set. Finally, the average scores accross all *n_cross_validations* rounds will be reported, and the corresponding model will be retrained on the whole training data set.
+
+### Monte Carlo Cross Validation (a.k.a. Repeated Random Sub-Sampling)
+Use *validation_size* to specify the percentage of the training data set that should be used for validation, and use *n_cross_validations* to specify the number of cross validations. During each cross validation round, a subset of size *validation_size* will be randomly selected for validation of the model trained on the remaining data. Finally, the average scores accross all *n_cross_validations* rounds will be reported, and the corresponding model will be retrained on the whole training data set.
+
+### Custom train and validation set
+You can specify seperate train and validation set either through the get_data() or directly to the fit method.
+
+## get_data() syntax <a name="getdata"></a>
+The *get_data()* function can be used to return a dictionary with these values:
+
+|Key|Type|Dependency|Mutually Exclusive with|Description|
+|:-|:-|:-|:-|:-|
+|X|Pandas Dataframe or Numpy Array|y|data_train, label, columns|All features to train with|
+|y|Pandas Dataframe or Numpy Array|X|label|Label data to train with.  For classification, this should be an array of integers. |
+|X_valid|Pandas Dataframe or Numpy Array|X, y, y_valid|data_train, label|*Optional* All features to validate with.  If this is not specified, X is split between train and validate|
+|y_valid|Pandas Dataframe or Numpy Array|X, y, X_valid|data_train, label|*Optional* The label data to validate with.  If this is not specified, y is split between train and validate|
+|sample_weight|Pandas Dataframe or Numpy Array|y|data_train, label, columns|*Optional* A weight value for each label. Higher values indicate that the sample is more important.|
+|sample_weight_valid|Pandas Dataframe or Numpy Array|y_valid|data_train, label, columns|*Optional* A weight value for each validation label. Higher values indicate that the sample is more important.  If this is not specified, sample_weight is split between train and validate|
+|data_train|Pandas Dataframe|label|X, y, X_valid, y_valid|All data (features+label) to train with|
+|label|string|data_train|X, y, X_valid, y_valid|Which column in data_train represents the label|
+|columns|Array of strings|data_train||*Optional* Whitelist of columns to use for features|
+|cv_splits_indices|Array of integers|data_train||*Optional* List of indexes to split the data for cross validation|
+
+## Data pre-processing and featurization <a name="preprocessing"></a>
+If you use "preprocess=True", the following data preprocessing steps are performed automatically for you:
+### 1. Dropping high cardinality or no variance features
+- Features with no useful information are dropped from training and validation sets. These include features with all values missing, same value across all rows or with extremely high cardinality (e.g., hashes, IDs or GUIDs).
+### 2. Missing value imputation
+- For numerical features, missing values are imputed with average of values in the column.
+- For categorical features, missing values are imputed with most frequent value.
+### 3. Generating additional features
+- For DateTime features: Year, Month, Day, Day of week, Day of year, Quarter, Week of the year, Hour, Minute, Second.
+- For Text features: Term frequency based on bi-grams and tri-grams, Count vectorizer.
+### 4. Transformations and encodings
+- Numeric features with very few unique values are transformed into categorical features.
+- Depending on cardinality of categorical features label encoding or (hashing) one-hot encoding is performed.
+
+# Running using python command <a name="pythoncommand"></a>
+Jupyter notebook provides a File / Download as / Python (.py) option for saving the notebook as a Python file.
+You can then run this file using the python command.
+However, on Windows the file needs to be modified before it can be run.
+The following condition must be added to the main code in the file:
+
+    if __name__ == "__main__":
+
+The main code of the file must be indented so that it is under this condition.
+
+# Troubleshooting <a name="troubleshooting"></a>
+## Iterations fail and the log contains "MemoryError"
+This can be caused by insufficient memory on the DSVM.  AutoML loads all training data into memory.  So, the available memory should be more than the training data size.
+If you are using a remote DSVM, memory is needed for each concurrent iteration.  The concurrent_iterations setting specifies the maximum concurrent iterations.  For example, if the training data size is 8Gb and concurrent_iterations is set to 10, the minimum memory required is at least 80Gb.
+To resolve this issue, allocate a DSVM with more memory or reduce the value specified for concurrent_iterations.
+
+## Iterations show as "Not Responding" in the RunDetails widget.
+This can be caused by too many concurrent iterations for a remote DSVM.  Each concurrent iteration usually takes 100% of a core when it is running.  Some iterations can use multiple cores.  So, the concurrent_iterations setting should always be less than the number of cores of the DSVM.
+To resolve this issue, try reducing the value specified for the concurrent_iterations setting.
+

automl/automl_env.yml

@@ -0,0 +1,19 @@
+name: azure_automl
+dependencies:
+  # The python interpreter version.
+  # Currently Azure ML only supports 3.5.2 and later.
+- python=3.6
+- nb_conda
+- matplotlib
+- numpy>=1.11.0,<1.16.0
+- scipy>=0.19.0,<0.20.0
+- scikit-learn>=0.18.0,<=0.19.1
+- pandas>=0.22.0,<0.23.0
+
+- pip:
+    # Required packages for AzureML execution, history, and data preparation.
+  - --extra-index-url https://pypi.python.org/simple
+  - azureml-sdk[automl]
+  - azureml-train-widgets
+  - pandas_ml
+  

automl/automl_setup.cmd

@@ -0,0 +1,42 @@
+@echo off
+set conda_env_name=%1
+
+IF "%conda_env_name%"=="" SET conda_env_name="azure_automl"
+
+call conda activate %conda_env_name% 2>nul:
+
+if not errorlevel 1 (
+  echo Upgrading azureml-sdk[automl] in existing conda environment %conda_env_name%
+  call pip install --upgrade azureml-sdk[automl]
+  if errorlevel 1 goto ErrorExit
+) else (
+  call conda env create -f automl_env.yml -n %conda_env_name%
+)
+
+call conda activate %conda_env_name% 2>nul:
+if errorlevel 1 goto ErrorExit
+
+call pip install psutil
+
+call jupyter nbextension install --py azureml.train.widgets
+if errorlevel 1 goto ErrorExit
+
+call jupyter nbextension enable --py azureml.train.widgets
+if errorlevel 1 goto ErrorExit
+
+echo.
+echo.
+echo ***************************************
+echo * AutoML setup completed successfully *
+echo ***************************************
+echo.
+echo Starting jupyter notebook - please run notebook 00.configuration
+echo.
+jupyter notebook --log-level=50
+
+goto End
+
+:ErrorExit
+echo Install failed
+
+:End

automl/automl_setup_linux.sh

@@ -0,0 +1,35 @@
+#!/bin/bash
+
+CONDA_ENV_NAME=$1
+
+if [ "$CONDA_ENV_NAME" == "" ]
+then
+  CONDA_ENV_NAME="azure_automl"
+fi
+
+if source activate $CONDA_ENV_NAME 2> /dev/null
+then
+   echo "Upgrading azureml-sdk[automl] in existing conda environment" $CONDA_ENV_NAME
+   pip install --upgrade azureml-sdk[automl]
+else
+   conda env create -f automl_env.yml -n $CONDA_ENV_NAME &&
+   source activate $CONDA_ENV_NAME &&
+   jupyter nbextension install --py azureml.train.widgets --user &&
+   jupyter nbextension enable --py azureml.train.widgets --user &&
+   echo "" &&
+   echo "" &&
+   echo "***************************************" &&
+   echo "* AutoML setup completed successfully *" &&
+   echo "***************************************" &&
+   echo "" &&
+   echo "Starting jupyter notebook - please run notebook 00.configuration" &&
+   echo "" &&
+   jupyter notebook --log-level=50
+fi
+
+if [ $? -gt 0 ]
+then
+   echo "Installation failed"
+fi
+
+

automl/automl_setup_mac.sh

@@ -0,0 +1,36 @@
+#!/bin/bash
+
+CONDA_ENV_NAME=$1
+
+if [ "$CONDA_ENV_NAME" == "" ]
+then
+  CONDA_ENV_NAME="azure_automl"
+fi
+
+if source activate $CONDA_ENV_NAME 2> /dev/null
+then
+   echo "Upgrading azureml-sdk[automl] in existing conda environment" $CONDA_ENV_NAME
+   pip install --upgrade azureml-sdk[automl]
+else
+   conda env create -f automl_env.yml -n $CONDA_ENV_NAME &&
+   source activate $CONDA_ENV_NAME &&
+   conda install lightgbm -c conda-forge -y &&
+   jupyter nbextension install --py azureml.train.widgets --user &&
+   jupyter nbextension enable --py azureml.train.widgets --user &&
+   echo "" &&
+   echo "" &&
+   echo "***************************************" &&
+   echo "* AutoML setup completed successfully *" &&
+   echo "***************************************" &&
+   echo "" &&
+   echo "Starting jupyter notebook - please run notebook 00.configuration" &&
+   echo "" &&
+   jupyter notebook --log-level=50
+fi
+
+if [ $? -gt 0 ]
+then
+   echo "Installation failed"
+fi
+
+

configuration.ipynb

@@ -1,389 +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": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/configuration.png)"
-      ]
-    },
-    {
-      "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, inference, 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 1.58.0 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.\n",
-        "\n",
-        "**Note**: A Basic workspace is created by default. If you would like to create an Enterprise workspace, please specify sku = 'enterprise'.\n",
-        "Please visit our [pricing page](https://azure.microsoft.com/en-us/pricing/details/machine-learning/) for more details on our Enterprise edition.\n"
-      ]
-    },
-    {
-      "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",
-        "                      sku = 'basic',\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",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\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 not 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 = \"cpu-cluster\"\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 cpu-cluster\")\n",
-        "except ComputeTargetException:\n",
-        "    print(\"Creating new cpu-cluster\")\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 = \"gpu-cluster\"\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 gpu-cluster\")\n",
-        "    \n",
-        "    # Specify the configuration for the new cluster\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"Standard_NC6s_v3\",\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 within notebook](./how-to-use-azureml/training/train-within-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": "ninhu"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.8 - AzureML",
-      "language": "python",
-      "name": "python38-azureml"
-    },
-    "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
-}

configuration.yml

@@ -1,4 +0,0 @@
-name: configuration
-dependencies:
-- pip:
-  - azureml-sdk

contrib/RAPIDS/README.md

@@ -1,305 +0,0 @@
-## How to use the RAPIDS on AzureML materials
-### Setting up requirements
-The material requires the use of the Azure ML SDK and of the Jupyter Notebook Server to run the interactive execution. Please refer to instructions to [setup the environment.](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#local "Local Computer Set Up") Follow the instructions under **Local Computer**, make sure to run the last step: <span style="font-family: Courier New;">pip install \<new package\></span> with <span style="font-family: Courier New;">new package = progressbar2  (pip install progressbar2)</span>
-  
-After following the directions, the user should end up setting a conda environment (<span style="font-family: Courier New;">myenv</span>)that can be activated in an Anaconda prompt
-
-The user would also require an Azure Subscription with a Machine Learning Services quota on the desired region for 24 nodes or more (to be able to select a vmSize with 4 GPUs as it is used on the Notebook) on the desired VM family ([NC\_v3](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv3-series),  [NC\_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv2-series), [ND](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#nd-series) or [ND_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ndv2-series-preview)), the specific vmSize to be used within the chosen family would also need to be whitelisted for Machine Learning Services usage.  
-
-&nbsp;  
-### Getting and running the material 
-Clone the AzureML Notebooks repository in GitHub by running the following command on a local_directory: 
-
-* C:\local_directory>git clone https://github.com/Azure/MachineLearningNotebooks.git
-
-On a conda prompt navigate to the local directory, activate the conda environment (<span style="font-family: Courier New;">myenv</span>), where the Azure ML SDK was installed and launch Jupyter Notebook. 
-
-* (<span style="font-family: Courier New;">myenv</span>) C:\local_directory>jupyter notebook
-
-From the resulting browser at http://localhost:8888/tree, navigate to the master notebook: 
-
-* http://localhost:8888/tree/MachineLearningNotebooks/contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb
-
-&nbsp;  
-The following notebook will appear:  
-
-![](imgs/NotebookHome.png)
-
-&nbsp;  
-### Master Jupyter Notebook
-The notebook can be executed interactively step by step, by pressing the Run button (In a red circle in the above image.)
-
-The first couple of functional steps import the necessary AzureML libraries.  If you experience any errors please refer back to the [setup the environment.](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#local "Local Computer Set Up") instructions.
-
-&nbsp;  
-#### Setting up a Workspace
-The following step gathers the information necessary to set up a workspace to execute the RAPIDS script. This needs to be done only once, or not at all if you already have a workspace you can use set up on the Azure Portal:
-
-![](imgs/WorkSpaceSetUp.png)
-
-
-It is important to be sure to set the correct values for the subscription\_id, resource\_group, workspace\_name, and region before executing the step. An example is:
-
-    subscription_id = os.environ.get("SUBSCRIPTION_ID", "1358e503-xxxx-4043-xxxx-65b83xxxx32d")
-    resource_group = os.environ.get("RESOURCE_GROUP", "AML-Rapids-Testing")
-    workspace_name = os.environ.get("WORKSPACE_NAME", "AML_Rapids_Tester")
-    workspace_region = os.environ.get("WORKSPACE_REGION", "West US 2")
-
-&nbsp;  
-The resource\_group and workspace_name could take any value, the region should match the region for which the subscription has the required Machine Learning Services node quota.
-
-The first time the code is executed it will redirect to the Azure Portal to validate subscription credentials. After the workspace is created, its related information is stored on a local file so that this step can be subsequently skipped. The immediate step will just load the saved workspace
-
-![](imgs/saved_workspace.png)
-
-Once a workspace has been created the user could skip its creation and just jump to this step. The configuration file resides in:
-
-* C:\local_directory\\MachineLearningNotebooks\contrib\RAPIDS\aml_config\config.json
-
-&nbsp;  
-#### Creating an AML Compute Target 
-Following step, creates an AML Compute Target 
-
-![](imgs/target_creation.png)
-
-Parameter vm\_size on function call AmlCompute.provisioning\_configuration() has to be a member of the VM families ([NC\_v3](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv3-series),  [NC\_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv2-series), [ND](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#nd-series) or [ND_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ndv2-series-preview)) that are the ones provided with P40 or V100 GPUs, that are the ones supported by RAPIDS. In this particular case an Standard\_NC24s\_V2 was used.
-
-&nbsp;  
-If the output of running the step has an error of the form:
-
-![](imgs/targeterror1.png)
-
-It is an indication that even though the subscription has a node quota for VMs for that family, it does not have a node quota for Machine Learning Services for that family. 
-You will need to request an increase node quota for that family in that region for **Machine Learning Services**.
-
-&nbsp;  
-Another possible error is the following: 
-
-![](imgs/targeterror2.png)
-
-Which indicates that specified vmSize has not been whitelisted for usage on Machine Learning Services and a request to do so should be filled.
-
-The successful creation of the compute target would have an output like the following:
-
-![](imgs/targetsuccess.png)
-&nbsp;  
-#### RAPIDS script uploading and viewing
-The next step copies the RAPIDS script process_data.py, which is a slightly modified implementation of the [RAPIDS E2E example](https://github.com/rapidsai/notebooks/blob/master/mortgage/E2E.ipynb), into a script processing folder and it presents its contents to the user. (The script is discussed in the next section in detail). 
-If the user wants to use a different RAPIDS script, the references to the  <span style="font-family: Courier New;">process_data.py</span> script have to be changed
-
-![](imgs/scriptuploading.png)
-&nbsp;  
-#### Data Uploading
-The RAPIDS script loads and extracts features from the Fannie Mae’s Mortgage Dataset to train an XGBoost prediction model. The script uses two years of data
-
-The next few steps download and decompress the data and is made available to the  script as an [Azure Machine Learning Datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data).
-
-&nbsp;  
-The following functions are used to download and decompress the input data
-
-
-![](imgs/dcf1.png)
-![](imgs/dcf2.png)
-![](imgs/dcf3.png)
-![](imgs/dcf4.png)
-
-&nbsp;  
-The next step uses those functions to download locally file: 
-http://rapidsai-data.s3-website.us-east-2.amazonaws.com/notebook-mortgage-data/mortgage_2000-2001.tgz'
-And to decompress it, into local folder path = .\mortgage_2000-2001
-The step takes several minutes, the intermediate outputs provide progress indicators.
-
-![](imgs/downamddecom.png)
-
-&nbsp;  
-The decompressed data should have the following structure:
-* .\mortgage_2000-2001\acq\Acquisition_<year>Q<num>.txt 
-* .\mortgage_2000-2001\perf\Performance_<year>Q<num>.txt 
-* .\mortgage_2000-2001\names.csv
-
-The data is divided in partitions that roughly correspond to yearly quarters. RAPIDS includes support for multi-node, multi-GPU deployments, enabling scaling up and out on much larger dataset sizes. The user will be able to verify that the number of partitions that the script is able to process increases with the number of GPUs used. The RAPIDS script is implemented for single-machine scenarios. An example supporting multiple nodes will be published later. 
-
-&nbsp;  
-The next step upload the data into the [Azure Machine Learning Datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data) under reference <span style="font-family: Courier New;">fileroot = mortgage_2000-2001</span>
-
-The step takes several minutes to load the data, the output provides a progress indicator.
-
-![](imgs/datastore.png)
-
-Once the data has been loaded into the Azure Machine LEarning Data Store, in subsequent run, the user can comment out the ds.upload line and just make reference to the <span style="font-family: Courier New;">mortgage_2000-2001</blog> data store reference  
-
-&nbsp;  
-#### Setting up required libraries and environment to run RAPIDS code
-There are two options to setup the environment to run RAPIDS code. The following steps shows how to ues a prebuilt conda environment. A recommended alternative is to specify a base Docker image and package dependencies. You can find sample code for that in the notebook.
-
-![](imgs/install2.png)
-
-&nbsp;  
-#### Wrapper function to submit the RAPIDS script as an Azure Machine Learning experiment
-
-The next step consists of the definition of a wrapper function to be used when the user attempts to run the RAPIDS script with different arguments. It takes as arguments: <span style="font-family: Times New Roman;">*cpu\_training*</span>;  a flag that indicates if the run is meant to be processed with CPU-only, <span style="font-family: Times New Roman;">*gpu\_count*</span>; the number of GPUs to be used if they are meant to be used and part_count: the number of data partitions to be used
-
-![](imgs/wrapper.png)
-
-&nbsp;  
-The core of the function resides in configuring the run by the instantiation of a ScriptRunConfig object, which defines the source_directory for the script to be executed, the name of the script and the arguments to be passed to the script.
-In addition to the wrapper function arguments, two other arguments are passed: <span style="font-family: Times New Roman;">*data\_dir*</span>, the directory where the data is stored and <span style="font-family: Times New Roman;">*end_year*</span> is the largest year to use partition from.
-
-
-As mentioned earlier the size of the data that can be processed increases with the number of gpus, in the function, dictionary <span style="font-family: Times New Roman;">*max\_gpu\_count\_data\_partition_mapping*</span> maps the maximum number of partitions that we empirically found that the system can handle given the number of GPUs used. The function throws a warning when the number of partitions for a given number of gpus exceeds the maximum but the script is still executed, however the user should expect an error as an out of memory situation would be encountered
-If the user wants to use a different RAPIDS script, the reference to the process_data.py script has to be changed
-
-&nbsp;  
-#### Submitting Experiments
-We are ready to submit experiments: launching the RAPIDS script with different sets of parameters.
-
-&nbsp;  
-The following couple of steps submit experiments under different conditions. 
-
-![](imgs/submission1.png)
-
-&nbsp;  
-The user can change variable num\_gpu between one and the number of GPUs supported by the chosen vmSize. Variable part\_count can take any value between 1 and 11, but if it exceeds the maximum for num_gpu, the run would result in an error
-
-&nbsp;  
-If the experiment is successfully submitted, it would be placed on a queue for processing, its status would appeared as Queued and an output like the following would appear 
-
-![](imgs/queue.png)
-
-&nbsp;  
-When the experiment starts running, its status would appeared as Running and the output would change to something like this:
-
-![](imgs/running.png)
-
-&nbsp;  
-#### Reproducing the performance gains plot results on the Blog Post
-When the run has finished successfully, its status would appeared as Completed and the output would change to something like this:
-
-&nbsp; 
-![](imgs/completed.png)
-
-Which is the output for an experiment run with three partitions and one GPU, notice that the reported processing time is 49.16 seconds just as depicted on the performance gains plot on the blog post
-
-&nbsp;  
-
-![](imgs/2GPUs.png)
-
-
-This output corresponds to a run with three partitions and two GPUs, notice that the reported processing time is 37.50 seconds just as depicted on the performance gains plot on the blog post
-
-&nbsp;  
-![](imgs/3GPUs.png)
-
-This output corresponds to an experiment run with three partitions and three GPUs, notice that the reported processing time is 24.40 seconds just as depicted on the performance gains plot on the blog post
-
-&nbsp;  
-![](imgs/4gpus.png)
-
-This output corresponds to an experiment run with three partitions and four GPUs, notice that the reported processing time is 23.33 seconds just as depicted on the performance gains plot on the blogpost
-
-&nbsp;  
-![](imgs/CPUBase.png)
-
-This output corresponds to an experiment run with three partitions and using only CPU, notice that the reported processing time is 9 minutes and 1.21 seconds or 541.21 second just as depicted on the performance gains plot on the blog post
-
-&nbsp;  
-![](imgs/OOM.png)
-
-This output corresponds to an experiment run with nine partitions and four GPUs, notice that the notebook throws a warning signaling that the number of partitions exceed the maximum that the system can handle with those many GPUs and the run ends up failing, hence having and status of Failed. 
-
-&nbsp;  
-##### Freeing Resources
-In the last step the notebook deletes the compute target. (This step is optional especially if the min_nodes in the cluster is set to 0 with which the cluster will scale down to 0 nodes when there is no usage.)
-
-![](imgs/clusterdelete.png)
-
-&nbsp;  
-### RAPIDS Script
-The Master Notebook runs experiments by launching a RAPIDS script with different sets of parameters. In this section, the RAPIDS script, process_data.py in the material, is analyzed
-
-The script first imports all the necessary libraries and parses the arguments passed by the Master Notebook.
-
-The all internal functions to be used by the script are defined.
-
-&nbsp;  
-#### Wrapper Auxiliary Functions:
-The below functions are wrappers for a configuration module for librmm, the RAPIDS Memory Manager python interface:
-
-![](imgs/wap1.png)![](imgs/wap2.png)
-
-&nbsp;  
-A couple of other functions are wrappers for the submission of jobs to the DASK client:
-
-![](imgs/wap3.png)
-![](imgs/wap4.png)
-
-&nbsp;  
-#### Data Loading Functions:
-The data is loaded through the use of the following three functions 
-
-![](imgs/DLF1.png)![](imgs/DLF2.png)![](imgs/DLF3.png)
-
-All three functions use library function cudf.read_csv(), cuDF version for the well known counterpart on Pandas.
-
-&nbsp;  
-#### Data Transformation and Feature Extraction Functions:
-The raw data is transformed and processed to extract features by joining, slicing, grouping, aggregating, factoring, etc, the original dataframes just as is done with Pandas. The following functions in the script are used for that purpose:
-![](imgs/fef1.png)![](imgs/fef2.png)![](imgs/fef3.png)![](imgs/fef4.png)![](imgs/fef5.png)
-
-![](imgs/fef6.png)![](imgs/fef7.png)![](imgs/fef8.png)![](imgs/fef9.png)
-
-&nbsp;  
-#### Main() Function
-The previous functions are used in the Main function to accomplish several steps: Set up the Dask client, do all ETL operations, set up and train an XGBoost model, the function also assigns which data needs to be processed by each Dask client
-
-&nbsp;  
-##### Setting Up DASK client:
-The following lines:
-
-![](imgs/daskini.png)
-
-&nbsp;  
-Initialize and set up a DASK client with a number of workers corresponding to the number of GPUs to be used on the run. A successful execution of the set up will result on the following output:
-
-![](imgs/daskoutput.png)
-
-##### All ETL functions are used on single calls to process\_quarter_gpu, one per data partition
-
-![](imgs/ETL.png)
-
-&nbsp;  
-##### Concentrating the data assigned to each DASK worker
-The partitions assigned to each worker are concatenated and set up for training.
-
-![](imgs/Dask2.png)
-
-&nbsp;  
-##### Setting Training Parameters
-The parameters used for the training of a gradient boosted decision tree model are set up in the following code block:
-![](imgs/PArameters.png)
-
-Notice how the parameters are modified when using the CPU-only mode.
-
-&nbsp;  
-##### Launching the training of a gradient boosted decision tree model using XGBoost.
-
-![](imgs/training.png)
-
-The outputs of the script can be observed in the master notebook as the script is executed
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-

contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb

@@ -1,547 +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": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/RAPIDS/azure-ml-with-nvidia-rapids/azure-ml-with-nvidia-rapids.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# NVIDIA RAPIDS in Azure Machine Learning"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The [RAPIDS](https://www.developer.nvidia.com/rapids) suite of software libraries from NVIDIA enables the execution of end-to-end data science and analytics pipelines entirely on GPUs. In many machine learning projects, a significant portion of the model training time is spent in setting up the data; this stage of the process is known as Extraction, Transformation and Loading, or ETL. By using the DataFrame API for ETL\u00c2\u00a0and GPU-capable ML algorithms in RAPIDS, data preparation and training models can be done in GPU-accelerated end-to-end pipelines without incurring serialization costs between the pipeline stages. This notebook demonstrates how to use NVIDIA RAPIDS to prepare data and train model\u00c3\u201a\u00c2\u00a0in Azure.\n",
-        " \n",
-        "In this notebook, we will do the following:\n",
-        " \n",
-        "* Create an Azure Machine Learning Workspace\n",
-        "* Create an AMLCompute target\n",
-        "* Use a script to process our data and train a model\n",
-        "* Obtain the data required to run this sample\n",
-        "* Create an AML run configuration to launch a machine learning job\n",
-        "* Run the script to prepare data for training and train the model\n",
-        " \n",
-        "Prerequisites:\n",
-        "* An Azure subscription to create a Machine Learning Workspace\n",
-        "* Familiarity with the Azure ML SDK (refer to [notebook samples](https://github.com/Azure/MachineLearningNotebooks))\n",
-        "* A Jupyter notebook environment with Azure Machine Learning SDK installed. Refer to instructions to [setup the environment](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#local)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Verify if Azure ML SDK is installed"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.core\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "from azureml.core import Workspace, Experiment\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "from azureml.core.compute import AmlCompute, ComputeTarget\n",
-        "from azureml.data.data_reference import DataReference\n",
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "from azureml.core import ScriptRunConfig\n",
-        "from azureml.widgets import RunDetails"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create Azure ML Workspace"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The following step is optional if you already have a workspace. If you want to use an existing workspace, then\n",
-        "skip this workspace creation step and move on to the next step to load the workspace.\n",
-        " \n",
-        "<font color='red'>Important</font>: in the code cell below, be sure to set the correct values for the subscription_id, \n",
-        "resource_group, workspace_name, region before executing this code cell."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "subscription_id = os.environ.get(\"SUBSCRIPTION_ID\", \"<subscription_id>\")\n",
-        "resource_group = os.environ.get(\"RESOURCE_GROUP\", \"<resource_group>\")\n",
-        "workspace_name = os.environ.get(\"WORKSPACE_NAME\", \"<workspace_name>\")\n",
-        "workspace_region = os.environ.get(\"WORKSPACE_REGION\", \"<region>\")\n",
-        "\n",
-        "ws = Workspace.create(workspace_name, subscription_id=subscription_id, resource_group=resource_group, location=workspace_region)\n",
-        "\n",
-        "# write config to a local directory for future use\n",
-        "ws.write_config()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Load existing Workspace"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "# if a locally-saved configuration file for the workspace is not available, use the following to load workspace\n",
-        "# ws = Workspace(subscription_id=subscription_id, resource_group=resource_group, workspace_name=workspace_name)\n",
-        "\n",
-        "print('Workspace name: ' + ws.name, \n",
-        "      'Azure region: ' + ws.location, \n",
-        "      'Subscription id: ' + ws.subscription_id, \n",
-        "      'Resource group: ' + ws.resource_group, sep = '\\n')\n",
-        "\n",
-        "scripts_folder = \"scripts_folder\"\n",
-        "\n",
-        "if not os.path.isdir(scripts_folder):\n",
-        "    os.mkdir(scripts_folder)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create AML Compute Target"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Because NVIDIA RAPIDS requires P40 or V100 GPUs, the user needs to specify compute targets from one of [NC_v3](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv3-series), [NC_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv2-series), [ND](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#nd-series) or [ND_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ndv2-series-preview) virtual machine types in Azure; these are the families of virtual machines in Azure that are provisioned with these GPUs.\n",
-        " \n",
-        "Pick one of the supported VM SKUs based on the number of GPUs you want to use for ETL and training in RAPIDS.\n",
-        " \n",
-        "The script in this notebook is implemented for single-machine scenarios. An example supporting multiple nodes will be published later."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "gpu_cluster_name = \"gpucluster\"\n",
-        "\n",
-        "if gpu_cluster_name in ws.compute_targets:\n",
-        "    gpu_cluster = ws.compute_targets[gpu_cluster_name]\n",
-        "    if gpu_cluster and type(gpu_cluster) is AmlCompute:\n",
-        "        print('Found compute target. Will use {0} '.format(gpu_cluster_name))\n",
-        "else:\n",
-        "    print(\"creating new cluster\")\n",
-        "    # vm_size parameter below could be modified to one of the RAPIDS-supported VM types\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"Standard_NC6s_v3\", min_nodes=1, max_nodes = 1)\n",
-        "\n",
-        "    # create the cluster\n",
-        "    gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, provisioning_config)\n",
-        "    gpu_cluster.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Script to process data and train model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# copy process_data.py into the script folder\n",
-        "import shutil\n",
-        "shutil.copy('./process_data.py', os.path.join(scripts_folder, 'process_data.py'))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Data required to run this sample"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This sample uses [Fannie Mae's Single-Family Loan Performance Data](http://www.fanniemae.com/portal/funding-the-market/data/loan-performance-data.html). Once you obtain access to the data, you will need to make this data available in an [Azure Machine Learning Datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data), for use in this sample. The following code shows how to do that."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Downloading Data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import tarfile\n",
-        "import hashlib\n",
-        "from urllib.request import urlretrieve\n",
-        "\n",
-        "def validate_downloaded_data(path):\n",
-        "    if(os.path.isdir(path) and os.path.exists(path + '//names.csv')) :\n",
-        "        if(os.path.isdir(path + '//acq' ) and len(os.listdir(path + '//acq')) == 8):\n",
-        "            if(os.path.isdir(path + '//perf' ) and len(os.listdir(path + '//perf')) == 11):\n",
-        "                print(\"Data has been downloaded and decompressed at: {0}\".format(path))\n",
-        "                return True\n",
-        "    print(\"Data has not been downloaded and decompressed\")\n",
-        "    return False\n",
-        "\n",
-        "def show_progress(count, block_size, total_size):\n",
-        "    global pbar\n",
-        "    global processed\n",
-        "    \n",
-        "    if count == 0:\n",
-        "        pbar = ProgressBar(maxval=total_size)\n",
-        "        processed = 0\n",
-        "    \n",
-        "    processed += block_size\n",
-        "    processed = min(processed,total_size)\n",
-        "    pbar.update(processed)\n",
-        "\n",
-        "        \n",
-        "def download_file(fileroot):\n",
-        "    filename = fileroot + '.tgz'\n",
-        "    if(not os.path.exists(filename) or hashlib.md5(open(filename, 'rb').read()).hexdigest() != '82dd47135053303e9526c2d5c43befd5' ):\n",
-        "        url_format = 'http://rapidsai-data.s3-website.us-east-2.amazonaws.com/notebook-mortgage-data/{0}.tgz'\n",
-        "        url = url_format.format(fileroot)\n",
-        "        print(\"...Downloading file :{0}\".format(filename))\n",
-        "        urlretrieve(url, filename)\n",
-        "        pbar.finish()\n",
-        "        print(\"...File :{0} finished downloading\".format(filename))\n",
-        "    else:\n",
-        "        print(\"...File :{0} has been downloaded already\".format(filename))\n",
-        "    return filename\n",
-        "\n",
-        "def decompress_file(filename,path):\n",
-        "    tar = tarfile.open(filename)\n",
-        "    print(\"...Getting information from {0} about files to decompress\".format(filename))\n",
-        "    members = tar.getmembers()\n",
-        "    numFiles = len(members)\n",
-        "    so_far = 0\n",
-        "    for member_info in members:\n",
-        "        tar.extract(member_info,path=path)\n",
-        "        so_far += 1\n",
-        "    print(\"...All {0} files have been decompressed\".format(numFiles))\n",
-        "    tar.close()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "fileroot = 'mortgage_2000-2001'\n",
-        "path = '.\\\\{0}'.format(fileroot)\n",
-        "pbar = None\n",
-        "processed = 0\n",
-        "\n",
-        "if(not validate_downloaded_data(path)):\n",
-        "    print(\"Downloading and Decompressing Input Data\")\n",
-        "    filename = download_file(fileroot)\n",
-        "    decompress_file(filename,path)\n",
-        "    print(\"Input Data has been Downloaded and Decompressed\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Uploading Data to Workspace"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ds = ws.get_default_datastore()\n",
-        "\n",
-        "# download and uncompress data in a local directory before uploading to data store\n",
-        "# directory specified in src_dir parameter below should have the acq, perf directories with data and names.csv file\n",
-        "\n",
-        "# ---->>>> UNCOMMENT THE BELOW LINE TO UPLOAD YOUR DATA IF NOT DONE SO ALREADY <<<<----\n",
-        "# ds.upload(src_dir=path, target_path=fileroot, overwrite=True, show_progress=True)\n",
-        "\n",
-        "# data already uploaded to the datastore\n",
-        "data_ref = DataReference(data_reference_name='data', datastore=ds, path_on_datastore=fileroot)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create AML run configuration to launch a machine learning job"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "RunConfiguration is used to submit jobs to Azure Machine Learning service. When creating RunConfiguration for a job, users can either \n",
-        "1. specify a Docker image with prebuilt conda environment and use it without any modifications to run the job, or \n",
-        "2. specify a Docker image as the base image and conda or pip packages as dependnecies to let AML build a new Docker image with a conda environment containing specified dependencies to use in the job\n",
-        "\n",
-        "The second option is the recommended option in AML. \n",
-        "The following steps have code for both options. You can pick the one that is more appropriate for your requirements. "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Specify prebuilt conda environment"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The following code shows how to install RAPIDS using conda. The `rapids.yml` file contains the list of packages necessary to run this tutorial. **NOTE:** Initial build of the image might take up to 20 minutes as the service needs to build and cache the new image; once the image is built the subequent runs use the cached image and the overhead is minimal."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "cd = CondaDependencies(conda_dependencies_file_path='rapids.yml')\n",
-        "run_config = RunConfiguration(conda_dependencies=cd)\n",
-        "run_config.framework = 'python'\n",
-        "run_config.target = gpu_cluster_name\n",
-        "run_config.environment.docker.enabled = True\n",
-        "run_config.environment.docker.gpu_support = True\n",
-        "run_config.environment.docker.base_image = \"mcr.microsoft.com/azureml/openmpi4.1.0-cuda11.1-cudnn8-ubuntu20.04\"\n",
-        "run_config.environment.spark.precache_packages = False\n",
-        "run_config.data_references={'data':data_ref.to_config()}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Using Docker"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Alternatively, you can specify RAPIDS Docker image."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# run_config = RunConfiguration()\n",
-        "# run_config.framework = 'python'\n",
-        "# run_config.environment.python.user_managed_dependencies = True\n",
-        "# run_config.environment.python.interpreter_path = '/conda/envs/rapids/bin/python'\n",
-        "# run_config.target = gpu_cluster_name\n",
-        "# run_config.environment.docker.enabled = True\n",
-        "# run_config.environment.docker.gpu_support = True\n",
-        "# run_config.environment.docker.base_image = \"rapidsai/rapidsai:cuda9.2-runtime-ubuntu20.04\"\n",
-        "# # run_config.environment.docker.base_image_registry.address = '<registry_url>' # not required if the base_image is in Docker hub\n",
-        "# # run_config.environment.docker.base_image_registry.username = '<user_name>' # needed only for private images\n",
-        "# # run_config.environment.docker.base_image_registry.password = '<password>' # needed only for private images\n",
-        "# run_config.environment.spark.precache_packages = False\n",
-        "# run_config.data_references={'data':data_ref.to_config()}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Wrapper function to submit Azure Machine Learning experiment"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# parameter cpu_predictor indicates if training should be done on CPU. If set to true, GPUs are used *only* for ETL and *not* for training\n",
-        "# parameter num_gpu indicates number of GPUs to use among the GPUs available in the VM for ETL and if cpu_predictor is false, for training as well \n",
-        "def run_rapids_experiment(cpu_training, gpu_count, part_count):\n",
-        "    # any value between 1-4 is allowed here depending the type of VMs available in gpu_cluster\n",
-        "    if gpu_count not in [1, 2, 3, 4]:\n",
-        "        raise Exception('Value specified for the number of GPUs to use {0} is invalid'.format(gpu_count))\n",
-        "\n",
-        "    # following data partition mapping is empirical (specific to GPUs used and current data partitioning scheme) and may need to be tweaked\n",
-        "    max_gpu_count_data_partition_mapping = {1: 3, 2: 4, 3: 6, 4: 8}\n",
-        "    \n",
-        "    if part_count > max_gpu_count_data_partition_mapping[gpu_count]:\n",
-        "        print(\"Too many partitions for the number of GPUs, exceeding memory threshold\")\n",
-        "        \n",
-        "    if part_count > 11:\n",
-        "        print(\"Warning: Maximum number of partitions available is 11\")\n",
-        "        part_count = 11\n",
-        "        \n",
-        "    end_year = 2000\n",
-        "    \n",
-        "    if part_count > 4:\n",
-        "        end_year = 2001 # use more data with more GPUs\n",
-        "\n",
-        "    src = ScriptRunConfig(source_directory=scripts_folder, \n",
-        "                          script='process_data.py', \n",
-        "                          arguments = ['--num_gpu', gpu_count, '--data_dir', str(data_ref),\n",
-        "                                      '--part_count', part_count, '--end_year', end_year,\n",
-        "                                      '--cpu_predictor', cpu_training\n",
-        "                                      ],\n",
-        "                          run_config=run_config\n",
-        "                         )\n",
-        "\n",
-        "    exp = Experiment(ws, 'rapidstest')\n",
-        "    run = exp.submit(config=src)\n",
-        "    RunDetails(run).show()\n",
-        "    return run"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Submit experiment (ETL & training on GPU)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "cpu_predictor = False\n",
-        "# the value for num_gpu should be less than or equal to the number of GPUs available in the VM\n",
-        "num_gpu = 1\n",
-        "data_part_count = 1\n",
-        "# train using CPU, use GPU for both ETL and training\n",
-        "run = run_rapids_experiment(cpu_predictor, num_gpu, data_part_count)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Submit experiment (ETL on GPU, training on CPU)\n",
-        "\n",
-        "To observe performance difference between GPU-accelerated RAPIDS based training with CPU-only training, set 'cpu_predictor' predictor to 'True' and rerun the experiment"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "cpu_predictor = True\n",
-        "# the value for num_gpu should be less than or equal to the number of GPUs available in the VM\n",
-        "num_gpu = 1\n",
-        "data_part_count = 1\n",
-        "# train using CPU, use GPU for ETL\n",
-        "run = run_rapids_experiment(cpu_predictor, num_gpu, data_part_count)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Delete cluster"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# delete the cluster\n",
-        "# gpu_cluster.delete()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "ksivas"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.8 - AzureML",
-      "language": "python",
-      "name": "python38-azureml"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.8"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 4
-}