Keras Sample

dockefile reorg
dockerfiles added
2025-12-20 17:45:10 -05:00 · 2019-02-14 16:55:43 -05:00 · 2019-01-31 09:21:06 -05:00 · 2019-01-30 17:04:18 -05:00 · 2019-01-28 18:10:20 -05:00 · 2019-01-28 18:09:36 -05:00
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--- a/00.configuration.ipynb
+++ b/00.configuration.ipynb
@@ -1,236 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 00. Installation and configuration\n",
    "This notebook configures your library of notebooks to connect to an Azure Machine Learning Workspace.  In this case, a library contains all of the notebooks in the current folder and any nested folders.  You can configure this notebook to use an existing workspace or create a new workspace.\n",
    "\n",
    "## What is an Azure ML Workspace and why do I need one?\n",
    "\n",
    "An AML Workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an AML Workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, operationalization, and the monitoring of operationalized models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Access Azure Subscription\n",
    "\n",
    "In order to create an AML Workspace, first you need access to an Azure Subscription.  You can [create your own](https://azure.microsoft.com/en-us/free/) or get your existing subscription information from the [Azure portal](https://portal.azure.com).\n",
    "\n",
    "### 2. If you're running on your own local environment, install Azure ML SDK and other libraries\n",
    "\n",
    "If you are running in your own environment, follow [SDK installation instructions](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment).  If you are running in Azure Notebooks or another Microsoft managed environment, the SDK is already installed.\n",
    "\n",
    "Also install following libraries to your environment. Many of the example notebooks depend on them\n",
    "\n",
    "```\n",
    "(myenv) $ conda install -y matplotlib tqdm scikit-learn\n",
    "```\n",
    "\n",
    "Once installation is complete, check the Azure ML SDK version:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "install"
    ]
   },
   "outputs": [],
   "source": [
    "import azureml.core\n",
    "\n",
    "print(\"SDK Version:\", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Make sure your subscription is registered to use ACI\n",
    "Azure Machine Learning makes use of Azure Container Instance (ACI). You need to ensure your subscription has been registered to use ACI in order be able to deploy a dev/test web service.  If you have run through the quickstart experience you have already performed this step.  Otherwise you will need to use the [Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest) and execute the following commands.\n",
    "\n",
    "```shell\n",
    "# check to see if ACI is already registered\n",
    "(myenv) $ az provider show -n Microsoft.ContainerInstance -o table\n",
    "\n",
    "# if ACI is not registered, run this command.\n",
    "# note you need to be the subscription owner in order to execute this command successfully.\n",
    "(myenv) $ az provider register -n Microsoft.ContainerInstance\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up your Azure Machine Learning workspace\n",
    "\n",
    "### Option 1: You have workspace already\n",
    "If you ran the Azure Machine Learning [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) in Azure Notebooks, you already have a configured workspace!  You can go to your Azure Machine Learning Getting Started library, view *config.json* file, and copy-paste the values for subscription ID, resource group and workspace name below.\n",
    "\n",
    "If you have a workspace created another way, [these instructions](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#create-workspace-configuration-file) describe how to get your subscription and workspace information.\n",
    "\n",
    "If this cell succeeds, you're done configuring this library!  Otherwise continue to follow the instructions in the rest of the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "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."
   ]
  }
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--- a/01.getting-started/01.train-within-notebook/01.train-within-notebook.ipynb
+++ b/01.getting-started/01.train-within-notebook/01.train-within-notebook.ipynb
@@ -1,816 +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": [
        "# 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
 }
--- a/01.getting-started/03.train-on-aci/03.train-on-aci.ipynb
+++ b/01.getting-started/03.train-on-aci/03.train-on-aci.ipynb
@@ -1,289 +0,0 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 03. Train on Azure Container Instance\n",
        "\n",
        "* Create Workspace\n",
        "* Create `train.py` in the project folder.\n",
        "* Configure an ACI (Azure Container Instance) run\n",
        "* Execute in ACI"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Prerequisites\n",
        "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Check core SDK version number\n",
        "import azureml.core\n",
        "\n",
        "print(\"SDK version:\", azureml.core.VERSION)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Initialize Workspace\n",
        "\n",
        "Initialize a workspace object from persisted configuration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "create workspace"
        ]
      },
      "outputs": [],
      "source": [
        "from azureml.core import Workspace\n",
        "\n",
        "ws = Workspace.from_config()\n",
        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create An Experiment\n",
        "\n",
        "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import Experiment\n",
        "experiment_name = 'train-on-aci'\n",
        "experiment = Experiment(workspace = ws, name = experiment_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Remote execution on ACI\n",
        "\n",
        "The training script `train.py` is already created for you. Let's have a look."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "with open('./train.py', 'r') as f:\n",
        "    print(f.read())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Configure for using ACI\n",
        "Linux-based ACI is available in `West US`, `East US`, `West Europe`, `North Europe`, `West US 2`, `Southeast Asia`, `Australia East`, `East US 2`, and `Central US` regions. See details [here](https://docs.microsoft.com/en-us/azure/container-instances/container-instances-quotas#region-availability)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "configure run"
        ]
      },
      "outputs": [],
      "source": [
        "from azureml.core.runconfig import RunConfiguration\n",
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "\n",
        "# create a new runconfig object\n",
        "run_config = RunConfiguration()\n",
        "\n",
        "# signal that you want to use ACI to execute script.\n",
        "run_config.target = \"containerinstance\"\n",
        "\n",
        "# ACI container group is only supported in certain regions, which can be different than the region the Workspace is in.\n",
        "run_config.container_instance.region = 'eastus2'\n",
        "\n",
        "# set the ACI CPU and Memory \n",
        "run_config.container_instance.cpu_cores = 1\n",
        "run_config.container_instance.memory_gb = 2\n",
        "\n",
        "# enable Docker \n",
        "run_config.environment.docker.enabled = True\n",
        "\n",
        "# set Docker base image to the default CPU-based image\n",
        "run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
        "\n",
        "# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
        "run_config.environment.python.user_managed_dependencies = False\n",
        "\n",
        "# auto-prepare the Docker image when used for execution (if it is not already prepared)\n",
        "run_config.auto_prepare_environment = True\n",
        "\n",
        "# specify CondaDependencies obj\n",
        "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Submit the Experiment\n",
        "Finally, run the training job on the ACI"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "remote run",
          "aci"
        ]
      },
      "outputs": [],
      "source": [
        "%%time \n",
        "from azureml.core.script_run_config import ScriptRunConfig\n",
        "\n",
        "script_run_config = ScriptRunConfig(source_directory='./',\n",
        "                                    script='train.py',\n",
        "                                    run_config=run_config)\n",
        "\n",
        "run = experiment.submit(script_run_config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "query history"
        ]
      },
      "outputs": [],
      "source": [
        "# Show run details\n",
        "run"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "remote run",
          "aci"
        ]
      },
      "outputs": [],
      "source": [
        "%%time\n",
        "# Shows output of the run on stdout.\n",
        "run.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "get metrics"
        ]
      },
      "outputs": [],
      "source": [
        "# get all metris logged in the run\n",
        "run.get_metrics()\n",
        "metrics = run.get_metrics()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n",
        "    min(metrics['mse']), \n",
        "    metrics['alpha'][np.argmin(metrics['mse'])]\n",
        "))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# show all the files stored within the run record\n",
        "run.get_file_names()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now you can take a model produced here, register it and then deploy as a web service."
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "roastala"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/01.getting-started/05.train-in-spark/05.train-in-spark.ipynb
+++ b/01.getting-started/05.train-in-spark/05.train-in-spark.ipynb
@@ -1,331 +0,0 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 05. Train in Spark\n",
        "* Create Workspace\n",
        "* Create Experiment\n",
        "* Copy relevant files to the script folder\n",
        "* Configure and Run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Prerequisites\n",
        "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Check core SDK version number\n",
        "import azureml.core\n",
        "\n",
        "print(\"SDK version:\", azureml.core.VERSION)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Initialize Workspace\n",
        "\n",
        "Initialize a workspace object from persisted configuration."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import Workspace\n",
        "\n",
        "ws = Workspace.from_config()\n",
        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create Experiment\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "experiment_name = 'train-on-spark'\n",
        "\n",
        "from azureml.core import Experiment\n",
        "exp = Experiment(workspace=ws, name=experiment_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## View `train-spark.py`\n",
        "\n",
        "For convenience, we created a training script for you. It is printed below as a text, but you can also run `%pfile ./train-spark.py` in a cell to show the file."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "with open('train-spark.py', 'r') as training_script:\n",
        "    print(training_script.read())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Configure & Run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Configure an ACI run\n",
        "Before you try running on an actual Spark cluster, you can use a Docker image with Spark already baked in, and run it in ACI(Azure Container Registry)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.runconfig import RunConfiguration\n",
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "\n",
        "# use pyspark framework\n",
        "aci_run_config = RunConfiguration(framework=\"pyspark\")\n",
        "\n",
        "# use ACI to run the Spark job\n",
        "aci_run_config.target = 'containerinstance'\n",
        "aci_run_config.container_instance.region = 'eastus2'\n",
        "aci_run_config.container_instance.cpu_cores = 1\n",
        "aci_run_config.container_instance.memory_gb = 2\n",
        "\n",
        "# specify base Docker image to use\n",
        "aci_run_config.environment.docker.enabled = True\n",
        "aci_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_MMLSPARK_CPU_IMAGE\n",
        "\n",
        "# specify CondaDependencies\n",
        "cd = CondaDependencies()\n",
        "cd.add_conda_package('numpy')\n",
        "aci_run_config.environment.python.conda_dependencies = cd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Submit script to ACI to run"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import ScriptRunConfig\n",
        "\n",
        "script_run_config = ScriptRunConfig(source_directory = '.',\n",
        "                                    script= 'train-spark.py',\n",
        "                                    run_config = aci_run_config)\n",
        "run = exp.submit(script_run_config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "run"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "run.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Note** you can also create a new VM, or attach an existing VM, and use Docker-based execution to run the Spark job. Please see the `04.train-in-vm` for example on how to configure and run in Docker mode in a VM."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Attach an HDI cluster\n",
        "Now we can use a real Spark cluster, HDInsight for Spark, to run this job. To use HDI commpute target:\n",
        " 1. Create a Spark for HDI cluster in Azure. Here are some [quick instructions](https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-jupyter-spark-sql). Make sure you use the Ubuntu flavor, NOT CentOS.\n",
        " 2. Enter the IP address, username and password below"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.compute import HDInsightCompute\n",
        "from azureml.exceptions import ComputeTargetException\n",
        "\n",
        "try:\n",
        "    # if you want to connect using SSH key instead of username/password you can provide parameters private_key_file and private_key_passphrase\n",
        "    hdi_compute = HDInsightCompute.attach(workspace=ws, \n",
        "                                          name=\"myhdi\", \n",
        "                                          address=\"<myhdi-ssh>.azurehdinsight.net\", \n",
        "                                          ssh_port=22, \n",
        "                                          username='<ssh-username>', \n",
        "                                          password='<ssh-pwd>')\n",
        "\n",
        "except ComputeTargetException as e:\n",
        "    print(\"Caught = {}\".format(e.message))\n",
        "    \n",
        "        \n",
        "hdi_compute.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Configure HDI run"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.runconfig import RunConfiguration\n",
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "\n",
        "\n",
        "# use pyspark framework\n",
        "hdi_run_config = RunConfiguration(framework=\"pyspark\")\n",
        "\n",
        "# Set compute target to the HDI cluster\n",
        "hdi_run_config.target = hdi_compute.name\n",
        "\n",
        "# specify CondaDependencies object to ask system installing numpy\n",
        "cd = CondaDependencies()\n",
        "cd.add_conda_package('numpy')\n",
        "hdi_run_config.environment.python.conda_dependencies = cd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Submit the script to HDI"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import ScriptRunConfig\n",
        "\n",
        "script_run_config = ScriptRunConfig(source_directory = '.',\n",
        "                                    script= 'train-spark.py',\n",
        "                                    run_config = hdi_run_config)\n",
        "run = exp.submit(config=script_run_config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# get the URL of the run history web page\n",
        "run"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# get all metris logged in the run\n",
        "metrics = run.get_metrics()\n",
        "print(metrics)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "aashishb"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/01.getting-started/05.train-in-spark/iris.csv
+++ b/01.getting-started/05.train-in-spark/iris.csv
@@ -1,150 +0,0 @@
 5.1,3.5,1.4,0.2,Iris-setosa
 4.9,3.0,1.4,0.2,Iris-setosa
 4.7,3.2,1.3,0.2,Iris-setosa
 4.6,3.1,1.5,0.2,Iris-setosa
 5.0,3.6,1.4,0.2,Iris-setosa
 5.4,3.9,1.7,0.4,Iris-setosa
 4.6,3.4,1.4,0.3,Iris-setosa
 5.0,3.4,1.5,0.2,Iris-setosa
 4.4,2.9,1.4,0.2,Iris-setosa
 4.9,3.1,1.5,0.1,Iris-setosa
 5.4,3.7,1.5,0.2,Iris-setosa
 4.8,3.4,1.6,0.2,Iris-setosa
 4.8,3.0,1.4,0.1,Iris-setosa
 4.3,3.0,1.1,0.1,Iris-setosa
 5.8,4.0,1.2,0.2,Iris-setosa
 5.7,4.4,1.5,0.4,Iris-setosa
 5.4,3.9,1.3,0.4,Iris-setosa
 5.1,3.5,1.4,0.3,Iris-setosa
 5.7,3.8,1.7,0.3,Iris-setosa
 5.1,3.8,1.5,0.3,Iris-setosa
 5.4,3.4,1.7,0.2,Iris-setosa
 5.1,3.7,1.5,0.4,Iris-setosa
 4.6,3.6,1.0,0.2,Iris-setosa
 5.1,3.3,1.7,0.5,Iris-setosa
 4.8,3.4,1.9,0.2,Iris-setosa
 5.0,3.0,1.6,0.2,Iris-setosa
 5.0,3.4,1.6,0.4,Iris-setosa
 5.2,3.5,1.5,0.2,Iris-setosa
 5.2,3.4,1.4,0.2,Iris-setosa
 4.7,3.2,1.6,0.2,Iris-setosa
 4.8,3.1,1.6,0.2,Iris-setosa
 5.4,3.4,1.5,0.4,Iris-setosa
 5.2,4.1,1.5,0.1,Iris-setosa
 5.5,4.2,1.4,0.2,Iris-setosa
 4.9,3.1,1.5,0.1,Iris-setosa
 5.0,3.2,1.2,0.2,Iris-setosa
 5.5,3.5,1.3,0.2,Iris-setosa
 4.9,3.1,1.5,0.1,Iris-setosa
 4.4,3.0,1.3,0.2,Iris-setosa
 5.1,3.4,1.5,0.2,Iris-setosa
 5.0,3.5,1.3,0.3,Iris-setosa
 4.5,2.3,1.3,0.3,Iris-setosa
 4.4,3.2,1.3,0.2,Iris-setosa
 5.0,3.5,1.6,0.6,Iris-setosa
 5.1,3.8,1.9,0.4,Iris-setosa
 4.8,3.0,1.4,0.3,Iris-setosa
 5.1,3.8,1.6,0.2,Iris-setosa
 4.6,3.2,1.4,0.2,Iris-setosa
 5.3,3.7,1.5,0.2,Iris-setosa
 5.0,3.3,1.4,0.2,Iris-setosa
 7.0,3.2,4.7,1.4,Iris-versicolor
 6.4,3.2,4.5,1.5,Iris-versicolor
 6.9,3.1,4.9,1.5,Iris-versicolor
 5.5,2.3,4.0,1.3,Iris-versicolor
 6.5,2.8,4.6,1.5,Iris-versicolor
 5.7,2.8,4.5,1.3,Iris-versicolor
 6.3,3.3,4.7,1.6,Iris-versicolor
 4.9,2.4,3.3,1.0,Iris-versicolor
 6.6,2.9,4.6,1.3,Iris-versicolor
 5.2,2.7,3.9,1.4,Iris-versicolor
 5.0,2.0,3.5,1.0,Iris-versicolor
 5.9,3.0,4.2,1.5,Iris-versicolor
 6.0,2.2,4.0,1.0,Iris-versicolor
 6.1,2.9,4.7,1.4,Iris-versicolor
 5.6,2.9,3.6,1.3,Iris-versicolor
 6.7,3.1,4.4,1.4,Iris-versicolor
 5.6,3.0,4.5,1.5,Iris-versicolor
 5.8,2.7,4.1,1.0,Iris-versicolor
 6.2,2.2,4.5,1.5,Iris-versicolor
 5.6,2.5,3.9,1.1,Iris-versicolor
 5.9,3.2,4.8,1.8,Iris-versicolor
 6.1,2.8,4.0,1.3,Iris-versicolor
 6.3,2.5,4.9,1.5,Iris-versicolor
 6.1,2.8,4.7,1.2,Iris-versicolor
 6.4,2.9,4.3,1.3,Iris-versicolor
 6.6,3.0,4.4,1.4,Iris-versicolor
 6.8,2.8,4.8,1.4,Iris-versicolor
 6.7,3.0,5.0,1.7,Iris-versicolor
 6.0,2.9,4.5,1.5,Iris-versicolor
 5.7,2.6,3.5,1.0,Iris-versicolor
 5.5,2.4,3.8,1.1,Iris-versicolor
 5.5,2.4,3.7,1.0,Iris-versicolor
 5.8,2.7,3.9,1.2,Iris-versicolor
 6.0,2.7,5.1,1.6,Iris-versicolor
 5.4,3.0,4.5,1.5,Iris-versicolor
 6.0,3.4,4.5,1.6,Iris-versicolor
 6.7,3.1,4.7,1.5,Iris-versicolor
 6.3,2.3,4.4,1.3,Iris-versicolor
 5.6,3.0,4.1,1.3,Iris-versicolor
 5.5,2.5,4.0,1.3,Iris-versicolor
 5.5,2.6,4.4,1.2,Iris-versicolor
 6.1,3.0,4.6,1.4,Iris-versicolor
 5.8,2.6,4.0,1.2,Iris-versicolor
 5.0,2.3,3.3,1.0,Iris-versicolor
 5.6,2.7,4.2,1.3,Iris-versicolor
 5.7,3.0,4.2,1.2,Iris-versicolor
 5.7,2.9,4.2,1.3,Iris-versicolor
 6.2,2.9,4.3,1.3,Iris-versicolor
 5.1,2.5,3.0,1.1,Iris-versicolor
 5.7,2.8,4.1,1.3,Iris-versicolor
 6.3,3.3,6.0,2.5,Iris-virginica
 5.8,2.7,5.1,1.9,Iris-virginica
 7.1,3.0,5.9,2.1,Iris-virginica
 6.3,2.9,5.6,1.8,Iris-virginica
 6.5,3.0,5.8,2.2,Iris-virginica
 7.6,3.0,6.6,2.1,Iris-virginica
 4.9,2.5,4.5,1.7,Iris-virginica
 7.3,2.9,6.3,1.8,Iris-virginica
 6.7,2.5,5.8,1.8,Iris-virginica
 7.2,3.6,6.1,2.5,Iris-virginica
 6.5,3.2,5.1,2.0,Iris-virginica
 6.4,2.7,5.3,1.9,Iris-virginica
 6.8,3.0,5.5,2.1,Iris-virginica
 5.7,2.5,5.0,2.0,Iris-virginica
 5.8,2.8,5.1,2.4,Iris-virginica
 6.4,3.2,5.3,2.3,Iris-virginica
 6.5,3.0,5.5,1.8,Iris-virginica
 7.7,3.8,6.7,2.2,Iris-virginica
 7.7,2.6,6.9,2.3,Iris-virginica
 6.0,2.2,5.0,1.5,Iris-virginica
 6.9,3.2,5.7,2.3,Iris-virginica
 5.6,2.8,4.9,2.0,Iris-virginica
 7.7,2.8,6.7,2.0,Iris-virginica
 6.3,2.7,4.9,1.8,Iris-virginica
 6.7,3.3,5.7,2.1,Iris-virginica
 7.2,3.2,6.0,1.8,Iris-virginica
 6.2,2.8,4.8,1.8,Iris-virginica
 6.1,3.0,4.9,1.8,Iris-virginica
 6.4,2.8,5.6,2.1,Iris-virginica
 7.2,3.0,5.8,1.6,Iris-virginica
 7.4,2.8,6.1,1.9,Iris-virginica
 7.9,3.8,6.4,2.0,Iris-virginica
 6.4,2.8,5.6,2.2,Iris-virginica
 6.3,2.8,5.1,1.5,Iris-virginica
 6.1,2.6,5.6,1.4,Iris-virginica
 7.7,3.0,6.1,2.3,Iris-virginica
 6.3,3.4,5.6,2.4,Iris-virginica
 6.4,3.1,5.5,1.8,Iris-virginica
 6.0,3.0,4.8,1.8,Iris-virginica
 6.9,3.1,5.4,2.1,Iris-virginica
 6.7,3.1,5.6,2.4,Iris-virginica
 6.9,3.1,5.1,2.3,Iris-virginica
 5.8,2.7,5.1,1.9,Iris-virginica
 6.8,3.2,5.9,2.3,Iris-virginica
 6.7,3.3,5.7,2.5,Iris-virginica
 6.7,3.0,5.2,2.3,Iris-virginica
 6.3,2.5,5.0,1.9,Iris-virginica
 6.5,3.0,5.2,2.0,Iris-virginica
 6.2,3.4,5.4,2.3,Iris-virginica
 5.9,3.0,5.1,1.8,Iris-virginica
--- a/01.getting-started/05.train-in-spark/train-spark.py
+++ b/01.getting-started/05.train-in-spark/train-spark.py
@@ -1,94 +0,0 @@
 # Copyright (c) Microsoft. All rights reserved.
 # Licensed under the MIT license.
 import numpy as np
 import pyspark
 import os
 import urllib
 import sys
 from pyspark.sql.functions import *
 from pyspark.ml.classification import *
 from pyspark.ml.evaluation import *
 from pyspark.ml.feature import *
 from pyspark.sql.types import StructType, StructField
 from pyspark.sql.types import DoubleType, IntegerType, StringType
 from azureml.core.run import Run
 # initialize logger
 run = Run.get_context()
 # start Spark session
 spark = pyspark.sql.SparkSession.builder.appName('Iris').getOrCreate()
 # print runtime versions
 print('****************')
 print('Python version: {}'.format(sys.version))
 print('Spark version: {}'.format(spark.version))
 print('****************')
 # load iris.csv into Spark dataframe
 schema = StructType([
    StructField("sepal-length", DoubleType()),
    StructField("sepal-width", DoubleType()),
    StructField("petal-length", DoubleType()),
    StructField("petal-width", DoubleType()),
    StructField("class", StringType())
 ])
 data = spark.read.csv('iris.csv', header=False, schema=schema)
 print("First 10 rows of Iris dataset:")
 data.show(10)
 # vectorize all numerical columns into a single feature column
 feature_cols = data.columns[:-1]
 assembler = pyspark.ml.feature.VectorAssembler(
    inputCols=feature_cols, outputCol='features')
 data = assembler.transform(data)
 # convert text labels into indices
 data = data.select(['features', 'class'])
 label_indexer = pyspark.ml.feature.StringIndexer(
    inputCol='class', outputCol='label').fit(data)
 data = label_indexer.transform(data)
 # only select the features and label column
 data = data.select(['features', 'label'])
 print("Reading for machine learning")
 data.show(10)
 # change regularization rate and you will likely get a different accuracy.
 reg = 0.01
 # load regularization rate from argument if present
 if len(sys.argv) > 1:
    reg = float(sys.argv[1])
 # log regularization rate
 run.log("Regularization Rate", reg)
 # use Logistic Regression to train on the training set
 train, test = data.randomSplit([0.70, 0.30])
 lr = pyspark.ml.classification.LogisticRegression(regParam=reg)
 model = lr.fit(train)
 # predict on the test set
 prediction = model.transform(test)
 print("Prediction")
 prediction.show(10)
 # evaluate the accuracy of the model using the test set
 evaluator = pyspark.ml.evaluation.MulticlassClassificationEvaluator(
    metricName='accuracy')
 accuracy = evaluator.evaluate(prediction)
 print()
 print('#####################################')
 print('Regularization rate is {}'.format(reg))
 print("Accuracy is {}".format(accuracy))
 print('#####################################')
 print()
 # log accuracy
 run.log('Accuracy', accuracy)
--- a/01.getting-started/12.enable-data-collection-for-models-in-aks/12.enable-data-collection-for-models-in-aks.ipynb
+++ b/01.getting-started/12.enable-data-collection-for-models-in-aks/12.enable-data-collection-for-models-in-aks.ipynb
@@ -1,452 +0,0 @@
 {
 "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
 }
--- a/Dockerfiles/1.0.10/Dockerfile
+++ b/Dockerfiles/1.0.10/Dockerfile
@@ -0,0 +1,29 @@
 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"
--- a/Dockerfiles/1.0.2/Dockerfile
+++ b/Dockerfiles/1.0.2/Dockerfile
@@ -0,0 +1,29 @@
 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"
--- a/Dockerfiles/1.0.6/Dockerfile
+++ b/Dockerfiles/1.0.6/Dockerfile
@@ -0,0 +1,29 @@
 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"
--- a/Dockerfiles/1.0.8/Dockerfile
+++ b/Dockerfiles/1.0.8/Dockerfile
@@ -0,0 +1,29 @@
 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"
--- a/NBSETUP.md
+++ b/NBSETUP.md
@@ -0,0 +1,34 @@
 # Notebook setup
 ---
 To run the notebooks in this repository use one of these methods:
 ## Use Azure Notebooks - Jupyter based notebooks in the Azure cloud
 1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
 [Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks
 1. Follow the instructions in the [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
    ![set kernel to Python 3.6](images/python36.png)
 ## **Use your own notebook server**
 Video walkthrough:
 [![Get Started video](images/yt_cover.png)](https://youtu.be/VIsXeTuW3FU)
 1. Setup a Jupyter Notebook server and [install the Azure Machine Learning SDK](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python)
 1. Clone [this repository](https://aka.ms/aml-notebooks)
 1. You may need to install other packages for specific notebook
    - For example, to run the Azure Machine Learning Data Prep notebooks, install the extra dataprep SDK:
    ```bash
     pip install azureml-dataprep
    ```
 1. Start your notebook server
 1. Follow the instructions in the [Configuration](configuration.ipynb) notebook to create and connect to a workspace
 1. Open one of the sample notebooks
--- a/README.md
+++ b/README.md
@@ -1,47 +1,58 @@
-Get the full documentation for Azure Machine Learning service at:
+# Azure Machine Learning service example notebooks
-https://docs.microsoft.com/azure/machine-learning/service/
+This repository contains example notebooks demonstrating the [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning-service/) Python SDK
 which allows you to build, train, deploy and manage machine learning solutions using Azure.  The AML SDK
 allows you the choice of using local or cloud compute resources, while managing
 and maintaining the complete data science workflow from the cloud.
-<br>
+![Azure ML workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/service/media/overview-what-is-azure-ml/aml.png)
-# Sample notebooks for Azure Machine Learning service
+## Quick installation
 ```sh
 pip install azureml-sdk
 ```
 Read more detailed instructions on [how to set up your environment](./NBSETUP.md).
-To run the notebooks in this repository use one of these methods:
+## How to navigate and use the example notebooks?
 You should always run the [Configuration](./configuration.ipynb) notebook first when setting up a notebook library on a new machine or in a new environment. It configures your notebook library to connect to an Azure Machine Learning workspace, and sets up your workspace and compute to be used by many of the other examples. 
-## Use Azure Notebooks - Jupyter based notebooks in the Azure cloud
+If you want to...
-1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
+ * ...try out and explore Azure ML, start with image classification tutorials [part 1 training](./tutorials/img-classification-part1-training.ipynb) and [part 2 deployment](./tutorials/img-classification-part2-deploy.ipynb).
-[Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks.
+ * ...learn about experimentation and tracking run history, first [train within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then try [training on remote VM](./how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb) and [using logging APIs](./how-to-use-azureml/training/logging-api/logging-api.ipynb).
-1. Follow the instructions in the [00.configuration](00.configuration.ipynb) notebook to create and connect to a workspace.
+ * ...train deep learning models at scale, first learn about [Machine Learning Compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and then try [distributed hyperparameter tuning](./how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) and [distributed training](./how-to-use-azureml/training-with-deep-learning/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb).
-1. Open one of the sample notebooks.
+ * ...deploy model as realtime scoring service, first learn the basics by [training within Notebook and deploying to Azure Container Instance](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then learn how to [register and manage models, and create Docker images](./how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb), and [production deploy models on Azure Kubernetes Cluster](./how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb).
 * ...deploy models as batch scoring service, first [train a model within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), learn how to [register and manage models](./how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb), then [create Machine Learning Compute for scoring compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and [use Machine Learning Pipelines to deploy your model](./how-to-use-azureml/machine-learning-pipelines/pipeline-mpi-batch-prediction.ipynb).
 * ...monitor your deployed models, learn about using [App Insights](./how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb) and [model data collection](./how-to-use-azureml/deployment/enable-data-collection-for-models-in-aks/enable-data-collection-for-models-in-aks.ipynb).
-    **Make sure the Azure Notebook kernel is set to `Python 3.6`** when you open a notebook.  
+## Tutorials
-    ![set kernel to Python 3.6](images/python36.png)
+The [Tutorials](./tutorials) folder contains notebooks for the tutorials described in the [Azure Machine Learning documentation](https://aka.ms/aml-docs)
 ## How to use Azure ML
 The [How to use Azure ML](./how-to-use-azureml) folder contains specific examples demonstrating the features of the Azure Machine Learning SDK
 - [Training](./how-to-use-azureml/training) - Examples of how to build models using Azure ML's logging and execution capabilities on local and remote compute targets.
 - [Training with Deep Learning](./how-to-use-azureml/training-with-deep-learning) - Examples demonstrating how to build deep learning models using estimators and parameter sweeps
 - [Automated Machine Learning](./how-to-use-azureml/automated-machine-learning) - Examples using Automated Machine Learning to automatically generate optimal machine learning pipelines and models
 - [Machine Learning Pipelines](./how-to-use-azureml/machine-learning-pipelines) - Examples showing how to create and use reusable pipelines for training and batch scoring
 - [Deployment](./how-to-use-azureml/deployment) - Examples showing how to deploy and manage machine learning models and solutions
 - [Azure Databricks](./how-to-use-azureml/azure-databricks) - Examples showing how to use Azure ML with Azure Databricks
 ---
 ## Documentation
 * Quickstarts, end-to-end tutorials, and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/).
 * [Python SDK reference]( https://docs.microsoft.com/en-us/python/api/overview/azure/ml/intro?view=azure-ml-py)
-## **Use your own notebook server**
+---
-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).
+## Projects using Azure Machine Learning
 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.
-> Note: **Looking for automated machine learning samples?**
+Visit following repos to see projects contributed by Azure ML users:
 > 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.
-# Contributing
+ - [Fine tune natural language processing models using Azure Machine Learning service](https://github.com/Microsoft/AzureML-BERT)
-
+ - [Fashion MNIST with Azure ML SDK](https://github.com/amynic/azureml-sdk-fashion)
 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.
 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.
--- a/automl/00.configuration.ipynb
+++ b/automl/00.configuration.ipynb
@@ -1,224 +0,0 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# AutoML 00. Configuration\n",
        "\n",
        "In this example you will create an Azure Machine Learning `Workspace` object and initialize your notebook directory to easily reload this object from a configuration file.  Typically you will only need to run this once per notebook directory, and all other notebooks in this directory or any sub-directories will automatically use the settings you indicate here.\n",
        "\n",
        "\n",
        "## Prerequisites:\n",
        "\n",
        "Before running this notebook, run the `automl_setup` script described in README.md.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Register Machine Learning Services Resource Provider\n",
        "\n",
        "Microsoft.MachineLearningServices only needs to be registed once in the subscription.\n",
        "To register it:\n",
        "1. Start the Azure portal.\n",
        "2. Select your `All services` and then `Subscription`.\n",
        "3. Select the subscription that you want to use.\n",
        "4. Click on `Resource providers`\n",
        "3. Click the `Register` link next to Microsoft.MachineLearningServices"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Check the Azure ML Core SDK Version to Validate Your Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import azureml.core\n",
        "\n",
        "print(\"SDK Version:\", azureml.core.VERSION)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Initialize an Azure ML Workspace\n",
        "### What is an Azure ML Workspace and Why Do I Need One?\n",
        "\n",
        "An Azure ML workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an Azure ML workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, operationalization, and the monitoring of operationalized models.\n",
        "\n",
        "\n",
        "### What do I Need?\n",
        "\n",
        "To create or access an Azure ML workspace, you will need to import the Azure ML library and specify following information:\n",
        "* A name for your workspace. You can choose one.\n",
        "* Your subscription id. Use the `id` value from the `az account show` command output above.\n",
        "* The resource group name. The resource group organizes Azure resources and provides a default region for the resources in the group. The resource group will be created if it doesn't exist. Resource groups can be created and viewed in the [Azure portal](https://portal.azure.com)\n",
        "* Supported regions include `eastus2`, `eastus`,`westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "subscription_id = \"<subscription_id>\"\n",
        "resource_group = \"myrg\"\n",
        "workspace_name = \"myws\"\n",
        "workspace_region = \"eastus2\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Creating a Workspace\n",
        "If you already have access to an Azure ML workspace you want to use, you can skip this cell.  Otherwise, this cell will create an Azure ML workspace for you in the specified subscription, provided you have the correct permissions for the given `subscription_id`.\n",
        "\n",
        "This will fail when:\n",
        "1. The workspace already exists.\n",
        "2. You do not have permission to create a workspace in the resource group.\n",
        "3. You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription.\n",
        "\n",
        "If workspace creation fails for any reason other than already existing, please work with your IT administrator to provide you with the appropriate permissions or to provision the required resources.\n",
        "\n",
        "**Note:** Creation of a new workspace can take several minutes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Import the Workspace class and check the Azure ML SDK version.\n",
        "from azureml.core import Workspace\n",
        "\n",
        "ws = Workspace.create(name = workspace_name,\n",
        "                      subscription_id = subscription_id,\n",
        "                      resource_group = resource_group, \n",
        "                      location = workspace_region)\n",
        "ws.get_details()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Configuring Your Local Environment\n",
        "You can validate that you have access to the specified workspace and write a configuration file to the default configuration location, `./aml_config/config.json`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import Workspace\n",
        "\n",
        "ws = Workspace(workspace_name = workspace_name,\n",
        "               subscription_id = subscription_id,\n",
        "               resource_group = resource_group)\n",
        "\n",
        "# Persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
        "ws.write_config()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can then load the workspace from this config file from any notebook in the current directory."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Load workspace configuration from ./aml_config/config.json file.\n",
        "my_workspace = Workspace.from_config()\n",
        "my_workspace.get_details()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create a Folder to Host All Sample Projects\n",
        "Finally, create a folder where all the sample projects will be hosted."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "sample_projects_folder = './sample_projects'\n",
        "\n",
        "if not os.path.isdir(sample_projects_folder):\n",
        "    os.mkdir(sample_projects_folder)\n",
        "    \n",
        "print('Sample projects will be created in {}.'.format(sample_projects_folder))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Success!\n",
        "Great, you are ready to move on to the rest of the sample notebooks."
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "savitam"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/automl/10.auto-ml-multi-output-example.ipynb
+++ b/automl/10.auto-ml-multi-output-example.ipynb
@@ -1,294 +0,0 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# AutoML 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
 }
--- a/automl/12.auto-ml-retrieve-the-training-sdk-versions.ipynb
+++ b/automl/12.auto-ml-retrieve-the-training-sdk-versions.ipynb
@@ -1,251 +0,0 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# AutoML 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
 }
--- a/automl/README.md
+++ b/automl/README.md
@@ -1,232 +0,0 @@
 # 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.
--- a/automl/automl_env.yml
+++ b/automl/automl_env.yml
@@ -1,19 +0,0 @@
 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
--- a/automl/automl_setup.cmd
+++ b/automl/automl_setup.cmd
@@ -1,42 +0,0 @@
@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
--- a/automl/automl_setup_linux.sh
+++ b/automl/automl_setup_linux.sh
@@ -1,35 +0,0 @@
 #!/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
--- a/automl/automl_setup_mac.sh
+++ b/automl/automl_setup_mac.sh
@@ -1,36 +0,0 @@
 #!/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
--- a/configuration.ipynb
+++ b/configuration.ipynb
@@ -0,0 +1,376 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Configuration\n",
        "\n",
        "_**Setting up your Azure Machine Learning services workspace and configuring your notebook library**_\n",
        "\n",
        "---\n",
        "---\n",
        "\n",
        "## Table of Contents\n",
        "\n",
        "1. [Introduction](#Introduction)\n",
        "    1. What is an Azure Machine Learning workspace\n",
        "1. [Setup](#Setup)\n",
        "    1. Azure subscription\n",
        "    1. Azure ML SDK and other library installation\n",
        "    1. Azure Container Instance registration\n",
        "1. [Configure your Azure ML Workspace](#Configure%20your%20Azure%20ML%20workspace)\n",
        "    1. Workspace parameters\n",
        "    1. Access your workspace\n",
        "    1. Create a new workspace\n",
        "    1. Create compute resources\n",
        "1. [Next steps](#Next%20steps)\n",
        "\n",
        "---\n",
        "\n",
        "## Introduction\n",
        "\n",
        "This notebook configures your library of notebooks to connect to an Azure Machine Learning (ML) workspace.  In this case, a library contains all of the notebooks in the current folder and any nested folders.  You can configure this notebook library to use an existing workspace or create a new workspace.\n",
        "\n",
        "Typically you will need to run this notebook only once per notebook library as all other notebooks will use connection information that is written here.  If you want to redirect your notebook library to work with a different workspace, then you should re-run this notebook.\n",
        "\n",
        "In this notebook you will\n",
        "* Learn about getting an Azure subscription\n",
        "* Specify your workspace parameters\n",
        "* Access or create your workspace\n",
        "* Add a default compute cluster for your workspace\n",
        "\n",
        "### What is an Azure Machine Learning workspace\n",
        "\n",
        "An Azure ML Workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an Azure ML Workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, deployment, inferencing, and the monitoring of deployed models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\n",
        "\n",
        "This section describes activities required before you can access any Azure ML services functionality."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 1. Azure Subscription\n",
        "\n",
        "In order to create an Azure ML Workspace, first you need access to an Azure subscription.  An Azure subscription allows you to manage storage, compute, and other assets in the Azure cloud.  You can [create a new subscription](https://azure.microsoft.com/en-us/free/) or access existing subscription information from the [Azure portal](https://portal.azure.com).  Later in this notebook you will need information such as your subscription ID in order to create and access AML workspaces.\n",
        "\n",
        "### 2. Azure ML SDK and other library installation\n",
        "\n",
        "If you are running in your own environment, follow [SDK installation instructions](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment).  If you are running in Azure Notebooks or another Microsoft managed environment, the SDK is already installed.\n",
        "\n",
        "Also install following libraries to your environment. Many of the example notebooks depend on them\n",
        "\n",
        "```\n",
        "(myenv) $ conda install -y matplotlib tqdm scikit-learn\n",
        "```\n",
        "\n",
        "Once installation is complete, the following cell checks the Azure ML SDK version:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "install"
        ]
      },
      "outputs": [],
      "source": [
        "import azureml.core\n",
        "\n",
        "print(\"This notebook was created using version 1.0.10 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "If you are using an older version of the SDK then this notebook was created using, you should upgrade your SDK.\n",
        "\n",
        "### 3. Azure Container Instance registration\n",
        "Azure Machine Learning uses of [Azure Container Instance (ACI)](https://azure.microsoft.com/services/container-instances) to deploy dev/test web services. An Azure subscription needs to be registered to use ACI.  If you or the subscription owner have not yet registered ACI on your subscription, you will need to use the [Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest) and execute the following commands.  Note that if you ran through the AML [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) you have already registered ACI. \n",
        "\n",
        "```shell\n",
        "# check to see if ACI is already registered\n",
        "(myenv) $ az provider show -n Microsoft.ContainerInstance -o table\n",
        "\n",
        "# if ACI is not registered, run this command.\n",
        "# note you need to be the subscription owner in order to execute this command successfully.\n",
        "(myenv) $ az provider register -n Microsoft.ContainerInstance\n",
        "```\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Configure your Azure ML workspace\n",
        "\n",
        "### Workspace parameters\n",
        "\n",
        "To use an AML Workspace, you will need to import the Azure ML SDK and supply the following information:\n",
        "* Your subscription id\n",
        "* A resource group name\n",
        "* (optional) The region that will host your workspace\n",
        "* A name for your workspace\n",
        "\n",
        "You can get your subscription ID from the [Azure portal](https://portal.azure.com).\n",
        "\n",
        "You will also need access to a [_resource group_](https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-group-overview#resource-groups), which organizes Azure resources and provides a default region for the resources in a group.  You can see what resource groups to which you have access, or create a new one in the [Azure portal](https://portal.azure.com).  If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n",
        "\n",
        "The region to host your workspace will be used if you are creating a new workspace.  You do not need to specify this if you are using an existing workspace. You can find the list of supported regions [here](https://azure.microsoft.com/en-us/global-infrastructure/services/?products=machine-learning-service).  You should pick a region that is close to your location or that contains your data.\n",
        "\n",
        "The name for your workspace is unique within the subscription and should be descriptive enough to discern among other AML Workspaces.  The subscription may be used only by you, or it may be used by your department or your entire enterprise, so choose a name that makes sense for your situation.\n",
        "\n",
        "The following cell allows you to specify your workspace parameters.  This cell uses the python method `os.getenv` to read values from environment variables which is useful for automation.  If no environment variable exists, the parameters will be set to the specified default values.  \n",
        "\n",
        "If you ran the Azure Machine Learning [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) in Azure Notebooks, you already have a configured workspace!  You can go to your Azure Machine Learning Getting Started library, view *config.json* file, and copy-paste the values for subscription ID, resource group and workspace name below.\n",
        "\n",
        "Replace the default values in the cell below with your workspace parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "subscription_id = os.getenv(\"SUBSCRIPTION_ID\", default=\"<my-subscription-id>\")\n",
        "resource_group = os.getenv(\"RESOURCE_GROUP\", default=\"<my-resource-group>\")\n",
        "workspace_name = os.getenv(\"WORKSPACE_NAME\", default=\"<my-workspace-name>\")\n",
        "workspace_region = os.getenv(\"WORKSPACE_REGION\", default=\"eastus2\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Access your workspace\n",
        "\n",
        "The following cell uses the Azure ML SDK to attempt to load the workspace specified by your parameters.  If this cell succeeds, your notebook library will be configured to access the workspace from all notebooks using the `Workspace.from_config()` method.  The cell can fail if the specified workspace doesn't exist or you don't have permissions to access it. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import Workspace\n",
        "\n",
        "try:\n",
        "    ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name)\n",
        "    # write the details of the workspace to a configuration file to the notebook library\n",
        "    ws.write_config()\n",
        "    print(\"Workspace configuration succeeded. Skip the workspace creation steps below\")\n",
        "except:\n",
        "    print(\"Workspace not accessible. Change your parameters or create a new workspace below\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create a new workspace\n",
        "\n",
        "If you don't have an existing workspace and are the owner of the subscription or resource group, you can create a new workspace.  If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n",
        "\n",
        "**Note**: As with other Azure services, there are limits on certain resources (for example AmlCompute quota) associated with the Azure ML service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n",
        "\n",
        "This cell will create an Azure ML workspace for you in a subscription provided you have the correct permissions.\n",
        "\n",
        "This will fail if:\n",
        "* You do not have permission to create a workspace in the resource group\n",
        "* You do not have permission to create a resource group if it's non-existing.\n",
        "* You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription\n",
        "\n",
        "If workspace creation fails, please work with your IT admin to provide you with the appropriate permissions or to provision the required resources."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "create workspace"
        ]
      },
      "outputs": [],
      "source": [
        "from azureml.core import Workspace\n",
        "\n",
        "# Create the workspace using the specified parameters\n",
        "ws = Workspace.create(name = workspace_name,\n",
        "                      subscription_id = subscription_id,\n",
        "                      resource_group = resource_group, \n",
        "                      location = workspace_region,\n",
        "                      create_resource_group = True,\n",
        "                      exist_ok = True)\n",
        "ws.get_details()\n",
        "\n",
        "# write the details of the workspace to a configuration file to the notebook library\n",
        "ws.write_config()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create compute resources for your training experiments\n",
        "\n",
        "Many of the sample notebooks use Azure ML managed compute (AmlCompute) to train models using a dynamically scalable pool of compute. In this section you will create default compute clusters for use by the other notebooks and any other operations you choose.\n",
        "\n",
        "To create a cluster, you need to specify a compute configuration that specifies the type of machine to be used and the scalability behaviors.  Then you choose a name for the cluster that is unique within the workspace that can be used to address the cluster later.\n",
        "\n",
        "The cluster parameters are:\n",
        "* vm_size - this describes the virtual machine type and size used in the cluster.  All machines in the cluster are the same type.  You can get the list of vm sizes available in your region by using the CLI command\n",
        "\n",
        "```shell\n",
        "az vm list-skus -o tsv\n",
        "```\n",
        "* min_nodes - this sets the minimum size of the cluster.  If you set the minimum to 0 the cluster will shut down all nodes while note in use.  Setting this number to a value higher than 0 will allow for faster start-up times, but you will also be billed when the cluster is not in use.\n",
        "* max_nodes - this sets the maximum size of the cluster.  Setting this to a larger number allows for more concurrency and a greater distributed processing of scale-out jobs.\n",
        "\n",
        "\n",
        "To create a **CPU** cluster now, run the cell below. The autoscale settings mean that the cluster will scale down to 0 nodes when inactive and up to 4 nodes when busy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "# Choose a name for your CPU cluster\n",
        "cpu_cluster_name = \"cpucluster\"\n",
        "\n",
        "# Verify that cluster does not exist already\n",
        "try:\n",
        "    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
        "    print(\"Found existing cpucluster\")\n",
        "except ComputeTargetException:\n",
        "    print(\"Creating new cpucluster\")\n",
        "    \n",
        "    # Specify the configuration for the new cluster\n",
        "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_D2_V2\",\n",
        "                                                           min_nodes=0,\n",
        "                                                           max_nodes=4)\n",
        "\n",
        "    # Create the cluster with the specified name and configuration\n",
        "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
        "    \n",
        "    # Wait for the cluster to complete, show the output log\n",
        "    cpu_cluster.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To create a **GPU** cluster, run the cell below. Note that your subscription must have sufficient quota for GPU VMs or the command will fail. To increase quota, see [these instructions](https://docs.microsoft.com/en-us/azure/azure-supportability/resource-manager-core-quotas-request). "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "# Choose a name for your GPU cluster\n",
        "gpu_cluster_name = \"gpucluster\"\n",
        "\n",
        "# Verify that cluster does not exist already\n",
        "try:\n",
        "    gpu_cluster = ComputeTarget(workspace=ws, name=gpu_cluster_name)\n",
        "    print(\"Found existing gpu cluster\")\n",
        "except ComputeTargetException:\n",
        "    print(\"Creating new gpucluster\")\n",
        "    \n",
        "    # Specify the configuration for the new cluster\n",
        "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_NC6\",\n",
        "                                                           min_nodes=0,\n",
        "                                                           max_nodes=4)\n",
        "    # Create the cluster with the specified name and configuration\n",
        "    gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, compute_config)\n",
        "\n",
        "    # Wait for the cluster to complete, show the output log\n",
        "    gpu_cluster.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "---\n",
        "\n",
        "## Next steps\n",
        "\n",
        "In this notebook you configured this notebook library to connect easily to an Azure ML workspace.  You can copy this notebook to your own libraries to connect them to you workspace, or use it to bootstrap new workspaces completely.\n",
        "\n",
        "If you came here from another notebook, you can return there and complete that exercise, or you can try out the [Tutorials](./tutorials) or jump into \"how-to\" notebooks and start creating and deploying models.  A good place to start is the [train in notebook](./how-to-use-azureml/training/train-in-notebook) example that walks through a simplified but complete end to end machine learning process."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "roastala"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb
+++ b/contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb
@@ -0,0 +1,409 @@
 {
  "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": [
        "# 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 and 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 in 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.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",
        "# 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",
        "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. just use it. ' + 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_v2\", 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": "markdown",
      "metadata": {},
      "source": [
        "The _process&#95;data.py_ script used in the step below is a slightly modified implementation of [RAPIDS E2E example](https://github.com/rapidsai/notebooks/blob/master/mortgage/E2E.ipynb)."
      ]
    },
    {
      "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'))\n",
        "\n",
        "with open(os.path.join(scripts_folder, './process_data.py'), 'r') as process_data_script:\n",
        "    print(process_data_script.read())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Data required to run this sample"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This sample uses [Fannie Mae\u00e2\u20ac\u2122s Single-Family Loan Performance Data](http://www.fanniemae.com/portal/funding-the-market/data/loan-performance-data.html). Refer to the 'Available mortgage datasets' section in [instructions](https://rapidsai.github.io/demos/datasets/mortgage-data) to get sample data.\n",
        "\n",
        "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."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<font color='red'>Important</font>: The following step assumes the data is uploaded to the Workspace's default data store under a folder named 'mortgagedata2000_01'. Note that uploading data to the Workspace's default data store is not necessary and the data can be referenced from any datastore, e.g., from Azure Blob or File service, once it is added as a datastore to the workspace. The path_on_datastore parameter needs to be updated, depending on where the data is available.  The directory where the data is available should have the following folder structure, as the process_data.py script expects this directory structure:\n",
        "* _&lt;data directory>_/acq\n",
        "* _&lt;data directory>_/perf\n",
        "* _names.csv_\n",
        "\n",
        "The 'acq' and 'perf' refer to directories containing data files. The _&lt;data directory>_ is the path specified in _path&#95;on&#95;datastore_ parameter in the step below."
      ]
    },
    {
      "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",
        "# ds.upload(src_dir='<local directory that has data>', target_path='mortgagedata2000_01', 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='mortgagedata2000_01')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create AML run configuration to launch a machine learning job"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "AML allows the option of using existing Docker images with prebuilt conda environments. The following step use an existing image from [Docker Hub](https://hub.docker.com/r/rapidsai/rapidsai/)."
      ]
    },
    {
      "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",
        "# use conda environment named 'rapids' available in the Docker image\n",
        "# this conda environment does not include azureml-defaults package that is required for using AML functionality like metrics tracking, model management etc.\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",
        "# if registry is not mentioned the image is pulled from Docker Hub\n",
        "run_config.environment.docker.base_image = \"rapidsai/rapidsai:cuda9.2_ubuntu16.04_root\"\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):\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",
        "    gpu_count_data_partition_mapping = {1: 2, 2: 4, 3: 5, 4: 7}\n",
        "    part_count = gpu_count_data_partition_mapping[gpu_count]\n",
        "\n",
        "    end_year = 2000\n",
        "    if gpu_count > 2:\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()"
      ]
    },
    {
      "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",
        "# train using CPU, use GPU for both ETL and training\n",
        "run_rapids_experiment(cpu_predictor, num_gpu)"
      ]
    },
    {
      "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",
        "# train using CPU, use GPU for ETL\n",
        "run_rapids_experiment(cpu_predictor, num_gpu)"
      ]
    },
    {
      "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 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/contrib/RAPIDS/process_data.py
+++ b/contrib/RAPIDS/process_data.py
@@ -0,0 +1,500 @@
 # License Info: https://github.com/rapidsai/notebooks/blob/master/LICENSE
 import numpy as np
 import datetime
 import dask_xgboost as dxgb_gpu
 import dask
 import dask_cudf
 from dask.delayed import delayed
 from dask.distributed import Client, wait
 import xgboost as xgb
 import cudf
 from cudf.dataframe import DataFrame
 from collections import OrderedDict
 import gc
 from glob import glob
 import os
 import argparse
 parser = argparse.ArgumentParser("rapidssample")
 parser.add_argument("--data_dir", type=str, help="location of data")
 parser.add_argument("--num_gpu", type=int, help="Number of GPUs to use", default=1)
 parser.add_argument("--part_count", type=int, help="Number of data files to train against", default=2)
 parser.add_argument("--end_year", type=int, help="Year to end the data load", default=2000)
 parser.add_argument("--cpu_predictor", type=str, help="Flag to use CPU for prediction", default='False')
 parser.add_argument('-f', type=str, default='') # added for notebook execution scenarios
 args = parser.parse_args()
 data_dir = args.data_dir
 num_gpu = args.num_gpu
 part_count = args.part_count
 end_year = args.end_year
 cpu_predictor = args.cpu_predictor.lower() in ('yes', 'true', 't', 'y', '1')
 print('data_dir = {0}'.format(data_dir))
 print('num_gpu = {0}'.format(num_gpu))
 print('part_count = {0}'.format(part_count))
 part_count = part_count + 1 # adding one because the usage below is not inclusive
 print('end_year = {0}'.format(end_year))
 print('cpu_predictor = {0}'.format(cpu_predictor))
 import subprocess
 cmd = "hostname --all-ip-addresses"
 process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE)
 output, error = process.communicate()
 IPADDR = str(output.decode()).split()[0]
 print('IPADDR is {0}'.format(IPADDR))
 cmd = "/rapids/notebooks/utils/dask-setup.sh 0"
 process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE)
 output, error = process.communicate()
 cmd = "/rapids/notebooks/utils/dask-setup.sh rapids " + str(num_gpu) + " 8786 8787 8790 " + str(IPADDR) + " MASTER"
 process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE)
 output, error = process.communicate()
 print(output.decode())
 import dask
 from dask.delayed import delayed
 from dask.distributed import Client, wait
 _client = IPADDR + str(":8786")
 client = dask.distributed.Client(_client)
 def initialize_rmm_pool():
    from librmm_cffi import librmm_config as rmm_cfg
    rmm_cfg.use_pool_allocator = True
    #rmm_cfg.initial_pool_size = 2<<30 # set to 2GiB. Default is 1/2 total GPU memory
    import cudf
    return cudf._gdf.rmm_initialize()
 def initialize_rmm_no_pool():
    from librmm_cffi import librmm_config as rmm_cfg
    rmm_cfg.use_pool_allocator = False
    import cudf
    return cudf._gdf.rmm_initialize()
 def run_dask_task(func, **kwargs):
    task = func(**kwargs)
    return task
 def process_quarter_gpu(year=2000, quarter=1, perf_file=""):
    ml_arrays = run_dask_task(delayed(run_gpu_workflow),
                                          quarter=quarter,
                                          year=year,
                                          perf_file=perf_file)
    return client.compute(ml_arrays,
                          optimize_graph=False,
                          fifo_timeout="0ms"
                         )
 def null_workaround(df, **kwargs):
    for column, data_type in df.dtypes.items():
        if str(data_type) == "category":
            df[column] = df[column].astype('int32').fillna(-1)
        if str(data_type) in ['int8', 'int16', 'int32', 'int64', 'float32', 'float64']:
            df[column] = df[column].fillna(-1)
    return df
 def run_gpu_workflow(quarter=1, year=2000, perf_file="", **kwargs):
    names = gpu_load_names()
    acq_gdf = gpu_load_acquisition_csv(acquisition_path= acq_data_path + "/Acquisition_"
                                      + str(year) + "Q" + str(quarter) + ".txt")
    acq_gdf = acq_gdf.merge(names, how='left', on=['seller_name'])
    acq_gdf.drop_column('seller_name')
    acq_gdf['seller_name'] = acq_gdf['new']
    acq_gdf.drop_column('new')
    perf_df_tmp = gpu_load_performance_csv(perf_file)
    gdf = perf_df_tmp
    everdf = create_ever_features(gdf)
    delinq_merge = create_delinq_features(gdf)
    everdf = join_ever_delinq_features(everdf, delinq_merge)
    del(delinq_merge)
    joined_df = create_joined_df(gdf, everdf)
    testdf = create_12_mon_features(joined_df)
    joined_df = combine_joined_12_mon(joined_df, testdf)
    del(testdf)
    perf_df = final_performance_delinquency(gdf, joined_df)
    del(gdf, joined_df)
    final_gdf = join_perf_acq_gdfs(perf_df, acq_gdf)
    del(perf_df)
    del(acq_gdf)
    final_gdf = last_mile_cleaning(final_gdf)
    return final_gdf
 def gpu_load_performance_csv(performance_path, **kwargs):
    """ Loads performance data
    Returns
    -------
    GPU DataFrame
    """
    cols = [
        "loan_id", "monthly_reporting_period", "servicer", "interest_rate", "current_actual_upb",
        "loan_age", "remaining_months_to_legal_maturity", "adj_remaining_months_to_maturity",
        "maturity_date", "msa", "current_loan_delinquency_status", "mod_flag", "zero_balance_code",
        "zero_balance_effective_date", "last_paid_installment_date", "foreclosed_after",
        "disposition_date", "foreclosure_costs", "prop_preservation_and_repair_costs",
        "asset_recovery_costs", "misc_holding_expenses", "holding_taxes", "net_sale_proceeds",
        "credit_enhancement_proceeds", "repurchase_make_whole_proceeds", "other_foreclosure_proceeds",
        "non_interest_bearing_upb", "principal_forgiveness_upb", "repurchase_make_whole_proceeds_flag",
        "foreclosure_principal_write_off_amount", "servicing_activity_indicator"
    ]
    dtypes = OrderedDict([
        ("loan_id", "int64"),
        ("monthly_reporting_period", "date"),
        ("servicer", "category"),
        ("interest_rate", "float64"),
        ("current_actual_upb", "float64"),
        ("loan_age", "float64"),
        ("remaining_months_to_legal_maturity", "float64"),
        ("adj_remaining_months_to_maturity", "float64"),
        ("maturity_date", "date"),
        ("msa", "float64"),
        ("current_loan_delinquency_status", "int32"),
        ("mod_flag", "category"),
        ("zero_balance_code", "category"),
        ("zero_balance_effective_date", "date"),
        ("last_paid_installment_date", "date"),
        ("foreclosed_after", "date"),
        ("disposition_date", "date"),
        ("foreclosure_costs", "float64"),
        ("prop_preservation_and_repair_costs", "float64"),
        ("asset_recovery_costs", "float64"),
        ("misc_holding_expenses", "float64"),
        ("holding_taxes", "float64"),
        ("net_sale_proceeds", "float64"),
        ("credit_enhancement_proceeds", "float64"),
        ("repurchase_make_whole_proceeds", "float64"),
        ("other_foreclosure_proceeds", "float64"),
        ("non_interest_bearing_upb", "float64"),
        ("principal_forgiveness_upb", "float64"),
        ("repurchase_make_whole_proceeds_flag", "category"),
        ("foreclosure_principal_write_off_amount", "float64"),
        ("servicing_activity_indicator", "category")
    ])
    print(performance_path)
    return cudf.read_csv(performance_path, names=cols, delimiter='|', dtype=list(dtypes.values()), skiprows=1)
 def gpu_load_acquisition_csv(acquisition_path, **kwargs):
    """ Loads acquisition data
    Returns
    -------
    GPU DataFrame
    """
    cols = [
        'loan_id', 'orig_channel', 'seller_name', 'orig_interest_rate', 'orig_upb', 'orig_loan_term', 
        'orig_date', 'first_pay_date', 'orig_ltv', 'orig_cltv', 'num_borrowers', 'dti', 'borrower_credit_score', 
        'first_home_buyer', 'loan_purpose', 'property_type', 'num_units', 'occupancy_status', 'property_state',
        'zip', 'mortgage_insurance_percent', 'product_type', 'coborrow_credit_score', 'mortgage_insurance_type', 
        'relocation_mortgage_indicator'
    ]
    dtypes = OrderedDict([
        ("loan_id", "int64"),
        ("orig_channel", "category"),
        ("seller_name", "category"),
        ("orig_interest_rate", "float64"),
        ("orig_upb", "int64"),
        ("orig_loan_term", "int64"),
        ("orig_date", "date"),
        ("first_pay_date", "date"),
        ("orig_ltv", "float64"),
        ("orig_cltv", "float64"),
        ("num_borrowers", "float64"),
        ("dti", "float64"),
        ("borrower_credit_score", "float64"),
        ("first_home_buyer", "category"),
        ("loan_purpose", "category"),
        ("property_type", "category"),
        ("num_units", "int64"),
        ("occupancy_status", "category"),
        ("property_state", "category"),
        ("zip", "int64"),
        ("mortgage_insurance_percent", "float64"),
        ("product_type", "category"),
        ("coborrow_credit_score", "float64"),
        ("mortgage_insurance_type", "float64"),
        ("relocation_mortgage_indicator", "category")
    ])
    print(acquisition_path)
    return cudf.read_csv(acquisition_path, names=cols, delimiter='|', dtype=list(dtypes.values()), skiprows=1)
 def gpu_load_names(**kwargs):
    """ Loads names used for renaming the banks
    Returns
    -------
    GPU DataFrame
    """
    cols = [
        'seller_name', 'new'
    ]
    dtypes = OrderedDict([
        ("seller_name", "category"),
        ("new", "category"),
    ])
    return cudf.read_csv(col_names_path, names=cols, delimiter='|', dtype=list(dtypes.values()), skiprows=1)
 def create_ever_features(gdf, **kwargs):
    everdf = gdf[['loan_id', 'current_loan_delinquency_status']]
    everdf = everdf.groupby('loan_id', method='hash').max()
    del(gdf)
    everdf['ever_30'] = (everdf['max_current_loan_delinquency_status'] >= 1).astype('int8')
    everdf['ever_90'] = (everdf['max_current_loan_delinquency_status'] >= 3).astype('int8')
    everdf['ever_180'] = (everdf['max_current_loan_delinquency_status'] >= 6).astype('int8')
    everdf.drop_column('max_current_loan_delinquency_status')
    return everdf
 def create_delinq_features(gdf, **kwargs):
    delinq_gdf = gdf[['loan_id', 'monthly_reporting_period', 'current_loan_delinquency_status']]
    del(gdf)
    delinq_30 = delinq_gdf.query('current_loan_delinquency_status >= 1')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min()
    delinq_30['delinquency_30'] = delinq_30['min_monthly_reporting_period']
    delinq_30.drop_column('min_monthly_reporting_period')
    delinq_90 = delinq_gdf.query('current_loan_delinquency_status >= 3')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min()
    delinq_90['delinquency_90'] = delinq_90['min_monthly_reporting_period']
    delinq_90.drop_column('min_monthly_reporting_period')
    delinq_180 = delinq_gdf.query('current_loan_delinquency_status >= 6')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min()
    delinq_180['delinquency_180'] = delinq_180['min_monthly_reporting_period']
    delinq_180.drop_column('min_monthly_reporting_period')
    del(delinq_gdf)
    delinq_merge = delinq_30.merge(delinq_90, how='left', on=['loan_id'], type='hash')
    delinq_merge['delinquency_90'] = delinq_merge['delinquency_90'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]'))
    delinq_merge = delinq_merge.merge(delinq_180, how='left', on=['loan_id'], type='hash')
    delinq_merge['delinquency_180'] = delinq_merge['delinquency_180'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]'))
    del(delinq_30)
    del(delinq_90)
    del(delinq_180)
    return delinq_merge
 def join_ever_delinq_features(everdf_tmp, delinq_merge, **kwargs):
    everdf = everdf_tmp.merge(delinq_merge, on=['loan_id'], how='left', type='hash')
    del(everdf_tmp)
    del(delinq_merge)
    everdf['delinquency_30'] = everdf['delinquency_30'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]'))
    everdf['delinquency_90'] = everdf['delinquency_90'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]'))
    everdf['delinquency_180'] = everdf['delinquency_180'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]'))
    return everdf
 def create_joined_df(gdf, everdf, **kwargs):
    test = gdf[['loan_id', 'monthly_reporting_period', 'current_loan_delinquency_status', 'current_actual_upb']]
    del(gdf)
    test['timestamp'] = test['monthly_reporting_period']
    test.drop_column('monthly_reporting_period')
    test['timestamp_month'] = test['timestamp'].dt.month
    test['timestamp_year'] = test['timestamp'].dt.year
    test['delinquency_12'] = test['current_loan_delinquency_status']
    test.drop_column('current_loan_delinquency_status')
    test['upb_12'] = test['current_actual_upb']
    test.drop_column('current_actual_upb')
    test['upb_12'] = test['upb_12'].fillna(999999999)
    test['delinquency_12'] = test['delinquency_12'].fillna(-1)
    joined_df = test.merge(everdf, how='left', on=['loan_id'], type='hash')
    del(everdf)
    del(test)
    joined_df['ever_30'] = joined_df['ever_30'].fillna(-1)
    joined_df['ever_90'] = joined_df['ever_90'].fillna(-1)
    joined_df['ever_180'] = joined_df['ever_180'].fillna(-1)
    joined_df['delinquency_30'] = joined_df['delinquency_30'].fillna(-1)
    joined_df['delinquency_90'] = joined_df['delinquency_90'].fillna(-1)
    joined_df['delinquency_180'] = joined_df['delinquency_180'].fillna(-1)
    joined_df['timestamp_year'] = joined_df['timestamp_year'].astype('int32')
    joined_df['timestamp_month'] = joined_df['timestamp_month'].astype('int32')
    return joined_df
 def create_12_mon_features(joined_df, **kwargs):
    testdfs = []
    n_months = 12
    for y in range(1, n_months + 1):
        tmpdf = joined_df[['loan_id', 'timestamp_year', 'timestamp_month', 'delinquency_12', 'upb_12']]
        tmpdf['josh_months'] = tmpdf['timestamp_year'] * 12 + tmpdf['timestamp_month']
        tmpdf['josh_mody_n'] = ((tmpdf['josh_months'].astype('float64') - 24000 - y) / 12).floor()
        tmpdf = tmpdf.groupby(['loan_id', 'josh_mody_n'], method='hash').agg({'delinquency_12': 'max','upb_12': 'min'})
        tmpdf['delinquency_12'] = (tmpdf['max_delinquency_12']>3).astype('int32')
        tmpdf['delinquency_12'] +=(tmpdf['min_upb_12']==0).astype('int32')
        tmpdf.drop_column('max_delinquency_12')
        tmpdf['upb_12'] = tmpdf['min_upb_12']
        tmpdf.drop_column('min_upb_12')
        tmpdf['timestamp_year'] = (((tmpdf['josh_mody_n'] * n_months) + 24000 + (y - 1)) / 12).floor().astype('int16')
        tmpdf['timestamp_month'] = np.int8(y)
        tmpdf.drop_column('josh_mody_n')
        testdfs.append(tmpdf)
        del(tmpdf)
    del(joined_df)
    return cudf.concat(testdfs)
 def combine_joined_12_mon(joined_df, testdf, **kwargs):
    joined_df.drop_column('delinquency_12')
    joined_df.drop_column('upb_12')
    joined_df['timestamp_year'] = joined_df['timestamp_year'].astype('int16')
    joined_df['timestamp_month'] = joined_df['timestamp_month'].astype('int8')
    return joined_df.merge(testdf, how='left', on=['loan_id', 'timestamp_year', 'timestamp_month'], type='hash')
 def final_performance_delinquency(gdf, joined_df, **kwargs):
    merged = null_workaround(gdf)
    joined_df = null_workaround(joined_df)
    merged['timestamp_month'] = merged['monthly_reporting_period'].dt.month
    merged['timestamp_month'] = merged['timestamp_month'].astype('int8')
    merged['timestamp_year'] = merged['monthly_reporting_period'].dt.year
    merged['timestamp_year'] = merged['timestamp_year'].astype('int16')
    merged = merged.merge(joined_df, how='left', on=['loan_id', 'timestamp_year', 'timestamp_month'], type='hash')
    merged.drop_column('timestamp_year')
    merged.drop_column('timestamp_month')
    return merged
 def join_perf_acq_gdfs(perf, acq, **kwargs):
    perf = null_workaround(perf)
    acq = null_workaround(acq)
    return perf.merge(acq, how='left', on=['loan_id'], type='hash')
 def last_mile_cleaning(df, **kwargs):
    drop_list = [
        'loan_id', 'orig_date', 'first_pay_date', 'seller_name',
        'monthly_reporting_period', 'last_paid_installment_date', 'maturity_date', 'ever_30', 'ever_90', 'ever_180',
        'delinquency_30', 'delinquency_90', 'delinquency_180', 'upb_12',
        'zero_balance_effective_date','foreclosed_after', 'disposition_date','timestamp'
    ]
    for column in drop_list:
        df.drop_column(column)
    for col, dtype in df.dtypes.iteritems():
        if str(dtype)=='category':
            df[col] = df[col].cat.codes
        df[col] = df[col].astype('float32')
    df['delinquency_12'] = df['delinquency_12'] > 0
    df['delinquency_12'] = df['delinquency_12'].fillna(False).astype('int32')
    for column in df.columns:
        df[column] = df[column].fillna(-1)
    return df.to_arrow(index=False)
 # to download data for this notebook, visit https://rapidsai.github.io/demos/datasets/mortgage-data and update the following paths accordingly
 acq_data_path = "{0}/acq".format(data_dir) #"/rapids/data/mortgage/acq"
 perf_data_path = "{0}/perf".format(data_dir) #"/rapids/data/mortgage/perf"
 col_names_path = "{0}/names.csv".format(data_dir) # "/rapids/data/mortgage/names.csv"
 start_year = 2000
 #end_year = 2000 # end_year is inclusive -- converted to parameter
 #part_count = 2 # the number of data files to train against -- converted to parameter
 client.run(initialize_rmm_pool)
 # NOTE: The ETL calculates additional features which are then dropped before creating the XGBoost DMatrix.
 # This can be optimized to avoid calculating the dropped features.
 print("Reading ...")
 t1 = datetime.datetime.now()
 gpu_dfs = []
 gpu_time = 0
 quarter = 1
 year = start_year
 count = 0
 while year <= end_year:
    for file in glob(os.path.join(perf_data_path + "/Performance_" + str(year) + "Q" + str(quarter) + "*")):
        if count < part_count:
            gpu_dfs.append(process_quarter_gpu(year=year, quarter=quarter, perf_file=file))
            count += 1
            print('file: {0}'.format(file))
            print('count: {0}'.format(count))
    quarter += 1
    if quarter == 5:
        year += 1
        quarter = 1
 wait(gpu_dfs)
 t2 = datetime.datetime.now()
 print("Reading time ...")
 print(t2-t1)
 print('len(gpu_dfs) is {0}'.format(len(gpu_dfs)))
 client.run(cudf._gdf.rmm_finalize)
 client.run(initialize_rmm_no_pool)
 dxgb_gpu_params = {
    'nround':            100,
    'max_depth':         8,
    'max_leaves':        2**8,
    'alpha':             0.9,
    'eta':               0.1,
    'gamma':             0.1,
    'learning_rate':     0.1,
    'subsample':         1,
    'reg_lambda':        1,
    'scale_pos_weight':  2,
    'min_child_weight':  30,
    'tree_method':       'gpu_hist',
    'n_gpus':            1, 
    'distributed_dask':  True,
    'loss':              'ls',
    'objective':         'gpu:reg:linear',
    'max_features':      'auto',
    'criterion':         'friedman_mse',
    'grow_policy':       'lossguide',
    'verbose':           True
 }
 if cpu_predictor:
    print('Training using CPUs')
    dxgb_gpu_params['predictor'] = 'cpu_predictor'
    dxgb_gpu_params['tree_method'] = 'hist'
    dxgb_gpu_params['objective'] = 'reg:linear'
 else:
    print('Training using GPUs')
 print('Training parameters are {0}'.format(dxgb_gpu_params))
 gpu_dfs = [delayed(DataFrame.from_arrow)(gpu_df) for gpu_df in gpu_dfs[:part_count]]
 gpu_dfs = [gpu_df for gpu_df in gpu_dfs]
 wait(gpu_dfs)
 tmp_map = [(gpu_df, list(client.who_has(gpu_df).values())[0]) for gpu_df in gpu_dfs]
 new_map = {}
 for key, value in tmp_map:
    if value not in new_map:
        new_map[value] = [key]
    else:
        new_map[value].append(key)
 del(tmp_map)
 gpu_dfs = []
 for list_delayed in new_map.values():
    gpu_dfs.append(delayed(cudf.concat)(list_delayed))
 del(new_map)
 gpu_dfs = [(gpu_df[['delinquency_12']], gpu_df[delayed(list)(gpu_df.columns.difference(['delinquency_12']))]) for gpu_df in gpu_dfs]
 gpu_dfs = [(gpu_df[0].persist(), gpu_df[1].persist()) for gpu_df in gpu_dfs]
 gpu_dfs = [dask.delayed(xgb.DMatrix)(gpu_df[1], gpu_df[0]) for gpu_df in gpu_dfs]
 gpu_dfs = [gpu_df.persist() for gpu_df in gpu_dfs]
 gc.collect()
 labels = None
 print('str(gpu_dfs) is {0}'.format(str(gpu_dfs)))
 wait(gpu_dfs)
 t1 = datetime.datetime.now()
 bst = dxgb_gpu.train(client, dxgb_gpu_params, gpu_dfs, labels, num_boost_round=dxgb_gpu_params['nround'])
 t2 = datetime.datetime.now()
 print("Training time ...")
 print(t2-t1)
 print('str(bst) is {0}'.format(str(bst)))
 print('Exiting script')
--- a/databricks/01.Installation_and_Configuration.ipynb
+++ b/databricks/01.Installation_and_Configuration.ipynb
@@ -1 +0,0 @@
 {"cells":[{"cell_type":"markdown","source":["Azure ML & Azure Databricks notebooks by Parashar Shah.\n\nCopyright (c) Microsoft Corporation. All rights reserved.\n\nLicensed under the MIT License."],"metadata":{}},{"cell_type":"markdown","source":["Please ensure you have run this notebook before proceeding."],"metadata":{}},{"cell_type":"markdown","source":["Now we support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package (during private preview). You can select the option to attach the library to all clusters or just one cluster.\n\nProvide this full string to install the SDK:\n\nazureml-sdk[databricks]"],"metadata":{}},{"cell_type":"code","source":["import azureml.core\n\n# Check core SDK version number - based on build number of preview/master.\nprint(\"SDK version:\", azureml.core.VERSION)"],"metadata":{},"outputs":[],"execution_count":4},{"cell_type":"code","source":["subscription_id = \"<your-subscription-id>\"\nresource_group = \"<your-existing-resource-group>\"\nworkspace_name = \"<a-new-or-existing-workspace; it is unrelated to Databricks workspace>\"\nworkspace_region = \"<your-resource group-region>\""],"metadata":{},"outputs":[],"execution_count":5},{"cell_type":"code","source":["# import the Workspace class and check the azureml SDK version\n# exist_ok checks if workspace exists or not.\n\nfrom azureml.core import Workspace\n\nws = Workspace.create(name = workspace_name,\n                      subscription_id = subscription_id,\n                      resource_group = resource_group, \n                      location = workspace_region,\n                      exist_ok=True)\n\nws.get_details()"],"metadata":{},"outputs":[],"execution_count":6},{"cell_type":"code","source":["ws = Workspace(workspace_name = workspace_name,\n               subscription_id = subscription_id,\n               resource_group = resource_group)\n\n# persist the subscription id, resource group name, and workspace name in aml_config/config.json.\nws.write_config()"],"metadata":{},"outputs":[],"execution_count":7},{"cell_type":"code","source":["%sh\ncat /databricks/driver/aml_config/config.json"],"metadata":{},"outputs":[],"execution_count":8},{"cell_type":"code","source":["# import the Workspace class and check the azureml SDK version\nfrom azureml.core import Workspace\n\nws = Workspace.from_config()\nprint('Workspace name: ' + ws.name, \n      'Azure region: ' + ws.location, \n      'Subscription id: ' + ws.subscription_id, \n      'Resource group: ' + ws.resource_group, sep = '\\n')"],"metadata":{},"outputs":[],"execution_count":9},{"cell_type":"code","source":["dbutils.notebook.exit(\"success\")"],"metadata":{},"outputs":[],"execution_count":10},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":11}],"metadata":{"name":"01.Installation_and_Configuration","notebookId":3874566296719377},"nbformat":4,"nbformat_minor":0}
--- a/databricks/02.Ingest_data.ipynb
+++ b/databricks/02.Ingest_data.ipynb
@@ -1 +0,0 @@
 {"cells":[{"cell_type":"markdown","source":["Azure ML & Azure Databricks notebooks by Parashar Shah.\n\nCopyright (c) Microsoft Corporation. All rights reserved.\n\nLicensed under the MIT License."],"metadata":{}},{"cell_type":"markdown","source":["Please ensure you have run all previous notebooks in sequence before running this."],"metadata":{}},{"cell_type":"markdown","source":["#Data Ingestion"],"metadata":{}},{"cell_type":"code","source":["import os\nimport urllib"],"metadata":{},"outputs":[],"execution_count":4},{"cell_type":"code","source":["# Download AdultCensusIncome.csv from Azure CDN. This file has 32,561 rows.\nbasedataurl = \"https://amldockerdatasets.azureedge.net\"\ndatafile = \"AdultCensusIncome.csv\"\ndatafile_dbfs = os.path.join(\"/dbfs\", datafile)\n\nif os.path.isfile(datafile_dbfs):\n    print(\"found {} at {}\".format(datafile, datafile_dbfs))\nelse:\n    print(\"downloading {} to {}\".format(datafile, datafile_dbfs))\n    urllib.request.urlretrieve(os.path.join(basedataurl, datafile), datafile_dbfs)"],"metadata":{},"outputs":[],"execution_count":5},{"cell_type":"code","source":["# Create a Spark dataframe out of the csv file.\ndata_all = sqlContext.read.format('csv').options(header='true', inferSchema='true', ignoreLeadingWhiteSpace='true', ignoreTrailingWhiteSpace='true').load(datafile)\nprint(\"({}, {})\".format(data_all.count(), len(data_all.columns)))\ndata_all.printSchema()"],"metadata":{},"outputs":[],"execution_count":6},{"cell_type":"code","source":["#renaming columns\ncolumns_new = [col.replace(\"-\", \"_\") for col in data_all.columns]\ndata_all = data_all.toDF(*columns_new)\ndata_all.printSchema()"],"metadata":{},"outputs":[],"execution_count":7},{"cell_type":"code","source":["display(data_all.limit(5))"],"metadata":{},"outputs":[],"execution_count":8},{"cell_type":"markdown","source":["#Data Preparation"],"metadata":{}},{"cell_type":"code","source":["# Choose feature columns and the label column.\nlabel = \"income\"\nxvals_all = set(data_all.columns) - {label}\n\n#dbutils.widgets.remove(\"xvars_multiselect\")\ndbutils.widgets.removeAll()\n\ndbutils.widgets.multiselect('xvars_multiselect', 'hours_per_week', xvals_all)\nxvars_multiselect = dbutils.widgets.get(\"xvars_multiselect\")\nxvars = xvars_multiselect.split(\",\")\n\nprint(\"label = {}\".format(label))\nprint(\"features = {}\".format(xvars))\n\ndata = data_all.select([*xvars, label])\n\n# Split data into train and test.\ntrain, test = data.randomSplit([0.75, 0.25], seed=123)\n\nprint(\"train ({}, {})\".format(train.count(), len(train.columns)))\nprint(\"test ({}, {})\".format(test.count(), len(test.columns)))"],"metadata":{},"outputs":[],"execution_count":10},{"cell_type":"markdown","source":["#Data Persistence"],"metadata":{}},{"cell_type":"code","source":["# Write the train and test data sets to intermediate storage\ntrain_data_path = \"AdultCensusIncomeTrain\"\ntest_data_path = \"AdultCensusIncomeTest\"\n\ntrain_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTrain\")\ntest_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTest\")\n\ntrain.write.mode('overwrite').parquet(train_data_path)\ntest.write.mode('overwrite').parquet(test_data_path)\nprint(\"train and test datasets saved to {} and {}\".format(train_data_path_dbfs, test_data_path_dbfs))"],"metadata":{},"outputs":[],"execution_count":12},{"cell_type":"code","source":["dbutils.notebook.exit(\"success\")"],"metadata":{},"outputs":[],"execution_count":13},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":14}],"metadata":{"name":"02.Ingest_data","notebookId":3874566296719393},"nbformat":4,"nbformat_minor":0}
--- a/databricks/03a.Build_model.ipynb
+++ b/databricks/03a.Build_model.ipynb
--- a/databricks/03b.Build_model_runHistory.ipynb
+++ b/databricks/03b.Build_model_runHistory.ipynb
--- a/databricks/04.Deploy_to_ACI.ipynb
+++ b/databricks/04.Deploy_to_ACI.ipynb
--- a/databricks/05.Deploy_to_AKS_existingImage.ipynb
+++ b/databricks/05.Deploy_to_AKS_existingImage.ipynb
@@ -1 +0,0 @@
 {"cells":[{"cell_type":"markdown","source":["Azure ML & Azure Databricks notebooks by Parashar Shah.\n\nCopyright (c) Microsoft Corporation. All rights reserved.\n\nLicensed under the MIT License."],"metadata":{}},{"cell_type":"markdown","source":["Please ensure you have run all previous notebooks in sequence before running this. This notebook uses image from ACI notebook for deploying to AKS."],"metadata":{}},{"cell_type":"code","source":["from azureml.core import Workspace\nimport azureml.core\n\n# Check core SDK version number\nprint(\"SDK version:\", azureml.core.VERSION)\n\n#'''\nws = Workspace.from_config()\nprint('Workspace name: ' + ws.name, \n      'Azure region: ' + ws.location, \n      'Subscription id: ' + ws.subscription_id, \n      'Resource group: ' + ws.resource_group, sep = '\\n')\n#'''"],"metadata":{},"outputs":[],"execution_count":3},{"cell_type":"code","source":["# List images by ws\n\nfrom azureml.core.image import ContainerImage\nfor i in ContainerImage.list(workspace = ws):\n    print('{}(v.{} [{}]) stored at {} with build log {}'.format(i.name, i.version, i.creation_state, i.image_location, i.image_build_log_uri))"],"metadata":{},"outputs":[],"execution_count":4},{"cell_type":"code","source":["from azureml.core.image import Image\nmyimage = Image(workspace=ws, id=\"aciws:25\")"],"metadata":{},"outputs":[],"execution_count":5},{"cell_type":"code","source":["#create AKS compute\n#it may take 20-25 minutes to create a new cluster\n\nfrom azureml.core.compute import AksCompute, ComputeTarget\n\n# Use the default configuration (can also provide parameters to customize)\nprov_config = AksCompute.provisioning_configuration()\n\naks_name = 'ps-aks-clus2' \n\n# Create the cluster\naks_target = ComputeTarget.create(workspace = ws, \n                                  name = aks_name, \n                                  provisioning_configuration = prov_config)\n\naks_target.wait_for_completion(show_output = True)\n\nprint(aks_target.provisioning_state)\nprint(aks_target.provisioning_errors)"],"metadata":{},"outputs":[],"execution_count":6},{"cell_type":"code","source":["from azureml.core.webservice import Webservice\nhelp( Webservice.deploy_from_image)"],"metadata":{},"outputs":[],"execution_count":7},{"cell_type":"code","source":["from azureml.core.webservice import Webservice, AksWebservice\nfrom azureml.core.image import ContainerImage\n\n#Set the web service configuration (using default here)\naks_config = AksWebservice.deploy_configuration()\n\n#unique service name\nservice_name ='ps-aks-service'\n\n# Webservice creation using single command, there is a variant to use image directly as well.\naks_service = Webservice.deploy_from_image(\n  workspace=ws, \n  name=service_name,\n  deployment_config = aks_config,\n  image = myimage,\n  deployment_target = aks_target\n    )\n\naks_service.wait_for_deployment(show_output=True)"],"metadata":{},"outputs":[],"execution_count":8},{"cell_type":"code","source":["#for using the Web HTTP API \nprint(aks_service.scoring_uri)\nprint(aks_service.get_keys())"],"metadata":{},"outputs":[],"execution_count":9},{"cell_type":"code","source":["import json\n\n#get the some sample data\ntest_data_path = \"AdultCensusIncomeTest\"\ntest = spark.read.parquet(test_data_path).limit(5)\n\ntest_json = json.dumps(test.toJSON().collect())\n\nprint(test_json)"],"metadata":{},"outputs":[],"execution_count":10},{"cell_type":"code","source":["#using data defined above predict if income is >50K (1) or <=50K (0)\naks_service.run(input_data=test_json)"],"metadata":{},"outputs":[],"execution_count":11},{"cell_type":"code","source":["#comment to not delete the web service\naks_service.delete()\n#image.delete()\n#model.delete()\n#aks_target.delete()"],"metadata":{},"outputs":[],"execution_count":12},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":13}],"metadata":{"name":"04.DeploytoACI","notebookId":3874566296719318},"nbformat":4,"nbformat_minor":0}
--- a/databricks/Databricks_AMLSDK_github.dbc
+++ b/databricks/Databricks_AMLSDK_github.dbc
--- a/databricks/readme.md
+++ b/databricks/readme.md
@@ -1,29 +0,0 @@
 # Azure Databricks - Azure Machine Learning SDK Sample Notebooks
 **NOTE**: With the latest version of Azure Machine Learning SDK, there are some API changes due to which previous version of notebooks will not work.
 Please remove the previous SDK version and install the latest SDK by installing **azureml-sdk[databricks]** as a PyPi library in Azure Databricks workspace. 
 **NOTE**: Please create your Azure Databricks cluster as v4.x (high concurrency preferred) with **Python 3** (dropdown).
 **NOTE**: Some packages like psutil upgrade libs that can cause a conflict, please install such packages by freezing lib version. Eg. "pstuil **cryptography==1.5 pyopenssl==16.0.0 ipython=2.2.0**" to avoid install error. This issue is related to Databricks and not related to AML SDK.
 **NOTE**: You should at least have contributor access to your Azure subcription to run some of the notebooks.
 The iPython Notebooks have to be run sequentially after making changes based on your subscription. The corresponding DBC archive contains all the notebooks and can be imported into your Databricks workspace. You can the run notebooks after importing .dbc instead of downloading individually.  
 This set of notebooks are related to Income prediction experiment based on this [dataset](https://archive.ics.uci.edu/ml/datasets/adult) and demonstrate how to data prep, train and operationalize a Spark ML model with Azure ML Python SDK from within Azure Databricks. For details on SDK concepts, please refer to [notebooks](https://github.com/Azure/MachineLearningNotebooks)
 (Recommended) [Azure Databricks AML SDK notebooks](Databricks_AMLSDK_github.dbc) A single DBC package to import all notebooks in your Azure Databricks workspace.
 01. [Installation and Configuration](01.Installation_and_Configuration.ipynb): Install the Azure ML Python SDK and Initialize an Azure ML Workspace and save the Workspace configuration file.
 02. [Ingest data](02.Ingest_data.ipynb): Download the Adult Census Income dataset and split it into train and test sets.
 03. [Build model](03a.Build_model.ipynb): Train a binary classification model in Azure Databricks with a Spark ML Pipeline.
 04. [Build model with Run History](03b.Build_model_runHistory.ipynb): Train model and also capture run history (tracking) with Azure ML Python SDK.
 05. [Deploy to ACI](04.Deploy_to_ACI.ipynb): Deploy model to Azure Container Instance (ACI) with Azure ML Python SDK.
 06. [Deploy to AKS](04.Deploy_to_AKS_existingImage.ipynb): Deploy model to Azure Kubernetis Service (AKS) with Azure ML Python SDK from an existing Image with model, conda and score file.
 Copyright (c) Microsoft Corporation. All rights reserved.
 All notebooks in this folder are licensed under the MIT License. 
 Apache®, Apache Spark, and Spark® are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries.
--- a/how-to-use-azureml/README.md
+++ b/how-to-use-azureml/README.md
@@ -0,0 +1,16 @@
 ## Examples to get started with Azure Machine Learning service
 Learn how to use Azure Machine Learning services for experimentation and model management.
 As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) notebook first to set up your Azure ML Workspace. Then, run the notebooks in following recommended order.
 * [train-within-notebook](./training/train-within-notebook): Train a model hile tracking run history, and learn how to deploy the model as web service to Azure Container Instance.
 * [train-on-local](./training/train-on-local): Learn how to submit a run and use Azure ML managed run configuration.
 * [train-on-remote-vm](./training/train-on-remote-vm): Use Data Science Virtual Machine as a target for remote runs.
 * [logging-api](./training/logging-api): Learn about the details of logging metrics to run history.
 * [register-model-create-image-deploy-service](./deployment/register-model-create-image-deploy-service): Learn about the details of model management.
 * [production-deploy-to-aks](./deployment/production-deploy-to-aks) Deploy a model to production at scale on Azure Kubernetes Service.
 * [enable-data-collection-for-models-in-aks](./deployment/enable-data-collection-for-models-in-aks) Learn about data collection APIs for deployed model.
 * [enable-app-insights-in-production-service](./deployment/enable-app-insights-in-production-service) Learn how to use App Insights with production web service.
 Find quickstarts, end-to-end tutorials, and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/).
--- a/how-to-use-azureml/automated-machine-learning/README.md
+++ b/how-to-use-azureml/automated-machine-learning/README.md
@@ -0,0 +1,261 @@
 # Table of Contents
 1. [Automated ML Introduction](#introduction)
 1. [Running samples in Azure Notebooks](#jupyter)
 1. [Running samples in Azure Databricks](#databricks)
 1. [Running samples in a Local Conda environment](#localconda)
 1. [Automated ML SDK Sample Notebooks](#samples)
 1. [Documentation](#documentation)
 1. [Running using python command](#pythoncommand)
 1. [Troubleshooting](#troubleshooting)
 <a name="introduction"></a>
 # Automated ML introduction
 Automated machine learning (automated ML) builds high quality machine learning models for you by automating model and hyperparameter selection. Bring a labelled dataset that you want to build a model for, automated ML will give you a high quality machine learning model that you can use for predictions.
 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.
 Below are the three execution environments supported by AutoML.
 <a name="jupyter"></a>
 ## Running samples in Azure Notebooks - Jupyter based notebooks in the Azure cloud
 1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
 [Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks.
 1. Follow the instructions in the [configuration](configuration.ipynb) notebook to create and connect to a workspace.
 1. Open one of the sample notebooks.
 <a name="databricks"></a>
 ## Running samples in Azure Databricks
 **NOTE**: Please create your Azure Databricks cluster as v4.x (high concurrency preferred) with **Python 3** (dropdown).
 **NOTE**: You should at least have contributor access to your Azure subcription to run the notebook.
 - Please remove the previous SDK version if there is any and install the latest SDK by installing **azureml-sdk[automl_databricks]** as a PyPi library in Azure Databricks workspace.
 - You can find the detail Readme instructions at [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks).
 - Download the sample notebook automl-databricks-local-01.ipynb from [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks) and import into the Azure databricks workspace.
 - Attach the notebook to the cluster.
 <a name="localconda"></a>
 ## Running samples in a Local Conda environment
 To run these notebook on your own notebook server, use these installation instructions.
 The instructions below will install everything you need and then start a Jupyter notebook.  To start your Jupyter notebook manually, use:
 ```
 conda activate azure_automl
 jupyter notebook
 ```
 or on Mac:
 ```
 source activate azure_automl
 jupyter notebook
 ```
 ### 1. Install mini-conda from [here](https://conda.io/miniconda.html), choose 64-bit 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.  See the specific sections below for Windows, Mac and Linux.  It can take about 10 minutes to execute.
 ## Windows
 Start an **Anaconda Prompt** window, cd to the **how-to-use-azureml/automated-machine-learning** 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 **how-to-use-azureml/automated-machine-learning** folder where the sample notebooks were extracted and then run:
 ```
 bash automl_setup_mac.sh
 ```
 ## Linux
 cd to the **how-to-use-azureml/automated-machine-learning** folder where the sample notebooks were extracted and then run:
 ```
 bash automl_setup_linux.sh
 ```
 ### 4. Running configuration.ipynb
 - Before running any samples you next need to run the configuration notebook. Click on configuration.ipynb notebook
 - Execute the cells in the notebook to Register Machine Learning Services Resource Provider and create a workspace. (*instructions in notebook*)
 ### 5. Running Samples
 - 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
 <a name="samples"></a>
 # Automated ML SDK Sample Notebooks
 - [configuration.ipynb](configuration.ipynb)
    - Create new Azure ML Workspace
    - Save Workspace configuration file
 - [auto-ml-classification.ipynb](classification/auto-ml-classification.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
    - Simple example of using Auto ML for classification
    - Uses local compute for training
 - [auto-ml-regression.ipynb](regression/auto-ml-regression.ipynb)
    - Dataset: scikit learn's [diabetes dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html)
    - Simple example of using Auto ML for regression
    - Uses local compute for training
 - [auto-ml-remote-execution.ipynb](remote-execution/auto-ml-remote-execution.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
    - Example of using Auto ML for classification using a remote linux DSVM for training
    - Parallel execution of iterations
    - Async tracking of progress
    - Cancelling individual iterations or entire run
    - Retrieving models for any iteration or logged metric
    - Specify automl settings as kwargs
 - [auto-ml-remote-batchai.ipynb](remote-batchai/auto-ml-remote-batchai.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
    - Example of using automated ML for classification using remote AmlCompute 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
 - [auto-ml-remote-attach.ipynb](remote-attach/auto-ml-remote-attach.ipynb)
    - Dataset: Scikit learn's [20newsgroup](http://scikit-learn.org/stable/datasets/twenty_newsgroups.html)
    - handling text data with preprocess flag
    - Reading data from a blob store for remote executions
    - using pandas dataframes for reading data
 - [auto-ml-missing-data-blacklist-early-termination.ipynb](missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
    - Blacklist certain pipelines
    - Specify a target metrics to indicate stopping criteria
    - Handling Missing Data in the input
 - [auto-ml-sparse-data-train-test-split.ipynb](sparse-data-train-test-split/auto-ml-sparse-data-train-test-split.ipynb)
    - Dataset: Scikit learn's [20newsgroup](http://scikit-learn.org/stable/datasets/twenty_newsgroups.html)
    - Handle sparse datasets
    - Specify custom train and validation set
 - [auto-ml-exploring-previous-runs.ipynb](exploring-previous-runs/auto-ml-exploring-previous-runs.ipynb)
    - List all projects for the workspace
    - List all AutoML Runs for a given project
    - Get details for a AutoML Run. (Automl settings, run widget & all metrics)
    - Download fitted pipeline for any iteration
 - [auto-ml-remote-execution-with-datastore.ipynb](remote-execution-with-datastore/auto-ml-remote-execution-with-datastore.ipynb)
    - Dataset: Scikit learn's [20newsgroup](http://scikit-learn.org/stable/datasets/twenty_newsgroups.html)
    - Download the data and store it in DataStore.
 - [auto-ml-classification-with-deployment.ipynb](classification-with-deployment/auto-ml-classification-with-deployment.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
    - Simple example of using Auto ML for classification
    - Registering the model
    - Creating Image and creating aci service
    - Testing the aci service
 - [auto-ml-sample-weight.ipynb](sample-weight/auto-ml-sample-weight.ipynb)
    - How to specifying sample_weight
    - The difference that it makes to test results
 - [auto-ml-dataprep.ipynb](dataprep/auto-ml-dataprep.ipynb)
    - Using DataPrep for reading data
 - [auto-ml-dataprep-remote-execution.ipynb](dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb)
    - Using DataPrep for reading data with remote execution
 - [auto-ml-classification-with-whitelisting.ipynb](classification-with-whitelisting/auto-ml-classification-with-whitelisting.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 with whitelisting tensorflow models.
    - Uses local compute for training
 - [auto-ml-forecasting-energy-demand.ipynb](forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb)
    - Dataset: [NYC energy demand data](forecasting-a/nyc_energy.csv)
    - Example of using AutoML for training a forecasting model
 - [auto-ml-forecasting-orange-juice-sales.ipynb](forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb)
    - Dataset: [Dominick's grocery sales of orange juice](forecasting-b/dominicks_OJ.csv)
    - Example of training an AutoML forecasting model on multiple time-series
 <a name="documentation"></a>
 See [Configure automated machine learning experiments](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-auto-train) to learn how more about the the settings and features available for automated machine learning experiments.
 <a name="pythoncommand"></a>
 # Running using python command
 Jupyter notebook provides a File / Download as / Python (.py) option for saving the notebook as a Python file.
 You can then run this file using the python command.
 However, on Windows the file needs to be modified before it can be run.
 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.
 <a name="troubleshooting"></a>
 # Troubleshooting
 ## automl_setup fails
 1. On windows, make sure that you are running automl_setup from an Anconda Prompt window rather than a regular cmd window.  You can launch the "Anaconda Prompt" window by hitting the Start button and typing "Anaconda Prompt".  If you don't see the application "Anaconda Prompt", you might not have conda or mini conda installed.  In that case, you can install it [here](https://conda.io/miniconda.html)
 2. Check that you have conda 64-bit installed rather than 32-bit.  You can check this with the command `conda info`.  The `platform` should be `win-64` for Windows or `osx-64` for Mac.
 3. Check that you have conda 4.4.10 or later.  You can check the version with the command `conda -V`.  If you have a previous version installed, you can update it using the command: `conda update conda`.
 4. Pass a new name as the first parameter to automl_setup so that it creates a new conda environment. You can view existing conda environments using `conda env list` and remove them with `conda env remove -n <environmentname>`. 
 ## configuration.ipynb fails
 1) For local conda, make sure that you have susccessfully run automl_setup first.
 2) Check that the subscription_id is correct.  You can find the subscription_id in the Azure Portal by selecting All Service and then Subscriptions. The characters "<" and ">" should not be included in the subscription_id value.  For example, `subscription_id = "12345678-90ab-1234-5678-1234567890abcd"` has the valid format.
 3) Check that you have Contributor or Owner access to the Subscription.
 4) Check that the region is one of the supported regions: `eastus2`, `eastus`, `westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`
 5) Check that you have access to the region using the Azure Portal.
 ## workspace.from_config fails
 If the call `ws = Workspace.from_config()` fails:
 1) Make sure that you have run the `configuration.ipynb` notebook successfully.
 2) If you are running a notebook from a folder that is not under the folder where you ran `configuration.ipynb`, copy the folder aml_config and the file config.json that it contains to the new folder.  Workspace.from_config reads the config.json for the notebook folder or it parent folder.
 3) If you are switching to a new subscription, resource group, workspace or region, make sure that you run the `configuration.ipynb` notebook again.  Changing config.json directly will only work if the workspace already exists in the specified resource group under the specified subscription.
 4) If you want to change the region, please change the workspace, resource group or subscription.  `Workspace.create` will not create or update a workspace if it already exists, even if the region specified is different.
 ## Sample notebook fails
 If a sample notebook fails with an error that property, method or library does not exist:
 1) Check that you have selected correct kernel in jupyter notebook.  The kernel is displayed in the top right of the notebook page.  It can be changed using the `Kernel | Change Kernel` menu option.  For Azure Notebooks, it should be `Python 3.6`.  For local conda environments, it should be the conda envioronment name that you specified in automl_setup.  The default is azure_automl.  Note that the kernel is saved as part of the notebook.  So, if you switch to a new conda environment, you will have to select the new kernel in the notebook.
 2) Check that the notebook is for the SDK version that you are using.  You can check the SDK version by executing `azureml.core.VERSION` in a jupyter notebook cell.  You can download previous version of the sample notebooks from GitHub by clicking the `Branch` button, selecting the `Tags` tab and then selecting the version.
 ## Remote run: DsvmCompute.create fails 
 There are several reasons why the DsvmCompute.create can fail.  The reason is usually in the error message but you have to look at the end of the error message for the detailed reason.  Some common reasons are:
 1) `Compute name is invalid, it should start with a letter, be between 2 and 16 character, and only include letters (a-zA-Z), numbers (0-9) and \'-\'.`  Note that underscore is not allowed in the name.
 2) `The requested VM size xxxxx is not available in the current region.`  You can select a different region or vm_size. 
 ## Remote run: Unable to establish SSH connection
 AutoML uses the SSH protocol to communicate with remote DSVMs.  This defaults to port 22.  Possible causes for this error are:
 1) The DSVM is not ready for SSH connections.  When DSVM creation completes, the DSVM might still not be ready to acceept SSH connections.  The sample notebooks have a one minute delay to allow for this.
 2) Your Azure Subscription may restrict the IP address ranges that can access the DSVM on port 22.  You can check this in the Azure Portal by selecting the Virtual Machine and then clicking Networking.  The Virtual Machine name is the name that you provided in the notebook plus 10 alpha numeric characters to make the name unique.  The Inbound Port Rules define what can access the VM on specific ports.  Note that there is a priority priority order.  So, a Deny entry with a low priority number will override a Allow entry with a higher priority number.
 ## Remote run: setup iteration fails
 This is often an issue with the `get_data` method.
 1) Check that the `get_data` method is valid by running it locally.
 2) Make sure that `get_data` isn't referring to any local files.  `get_data` is executed on the remote DSVM.  So, it doesn't have direct access to local data files.  Instead you can store the data files with DataStore.  See [auto-ml-remote-execution-with-datastore.ipynb](remote-execution-with-datastore/auto-ml-remote-execution-with-datastore.ipynb)
 3) You can get to the error log for the setup iteration by clicking the `Click here to see the run in Azure portal` link, click `Back to Experiment`, click on the highest run number and then click on Logs.
 ## Remote run: disk full
 AutoML creates files under /tmp/azureml_runs for each iteration that it runs.  It creates a folder with the iteration id.  For example: AutoML_9a038a18-77cc-48f1-80fb-65abdbc33abe_93.  Under this, there is a azureml-logs folder, which contains logs.  If you run too many iterations on the same DSVM, these files can fill the disk.
 You can delete the files under /tmp/azureml_runs or just delete the VM and create a new one.
 If your get_data downloads files, make sure the delete them or they can use disk space as well.
 When using DataStore, it is good to specify an absolute path for the files so that they are downloaded just once.  If you specify a relative path, it will download a file for each iteration.
 ## Remote run: 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 max_concurrent_iterations setting specifies the maximum concurrent iterations.  For example, if the training data size is 8Gb and max_concurrent_iterations is set to 10, the minimum memory required is at least 80Gb.
 To resolve this issue, allocate a DSVM with more memory or reduce the value specified for max_concurrent_iterations.
 ## Remote run: Iterations show as "Not Responding" in the RunDetails widget.
 This can be caused by too many concurrent iterations for a remote DSVM.  Each concurrent iteration usually takes 100% of a core when it is running.  Some iterations can use multiple cores.  So, the max_concurrent_iterations setting should always be less than the number of cores of the DSVM.
 To resolve this issue, try reducing the value specified for the max_concurrent_iterations setting.
--- a/how-to-use-azureml/automated-machine-learning/automl_env.yml
+++ b/how-to-use-azureml/automated-machine-learning/automl_env.yml
@@ -0,0 +1,32 @@
 name: azure_automl
 dependencies:
  # The python interpreter version.
  # Currently Azure ML only supports 3.5.2 and later.
 - python=3.6
 - nb_conda
 - matplotlib==2.1.0
 - numpy>=1.11.0,<1.15.0
 - cython
 - urllib3<1.24
 - scipy>=1.0.0,<=1.1.0
 - scikit-learn>=0.18.0,<=0.19.1
 - pandas>=0.22.0,<0.23.0
 - tensorflow>=1.12.0
 # Required for azuremlftk
 - dill
 - pyodbc
 - statsmodels 
 - numexpr  
 - keras
 - distributed>=1.21.5,<1.24
 - pip:
  # Required for azuremlftk
  - https://azuremlpackages.blob.core.windows.net/forecasting/azuremlftk-0.1.18323.5a1-py3-none-any.whl
  # Required packages for AzureML execution, history, and data preparation.
  - azureml-sdk[automl,notebooks,explain]
  - pandas_ml
--- a/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
+++ b/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
@@ -0,0 +1,33 @@
 name: azure_automl
 dependencies:
  # The python interpreter version.
  # Currently Azure ML only supports 3.5.2 and later.
 - python=3.6
 - nb_conda
 - matplotlib==2.1.0
 - numpy>=1.15.3
 - cython
 - urllib3<1.24
 - scipy>=1.0.0,<=1.1.0
 - scikit-learn>=0.18.0,<=0.19.1
 - pandas>=0.22.0,<0.23.0
 - tensorflow>=1.12.0
 # Required for azuremlftk
 - dill
 - pyodbc
 - statsmodels 
 - numexpr  
 - keras
 - distributed>=1.21.5,<1.24
 - pip:
  # Required for azuremlftk
  - https://azuremlpackages.blob.core.windows.net/forecasting/azuremlftk-0.1.18323.5a1-py3-none-any.whl
  # Required packages for AzureML execution, history, and data preparation.
  - azureml-sdk[automl,notebooks,explain]
  - pandas_ml
--- a/how-to-use-azureml/automated-machine-learning/automl_setup.cmd
+++ b/how-to-use-azureml/automated-machine-learning/automl_setup.cmd
@@ -0,0 +1,51 @@
@echo off
 set conda_env_name=%1
 set automl_env_file=%2
 set options=%3
 set PIP_NO_WARN_SCRIPT_LOCATION=0
 IF "%conda_env_name%"=="" SET conda_env_name="azure_automl"
 IF "%automl_env_file%"=="" SET automl_env_file="automl_env.yml"
 IF NOT EXIST %automl_env_file% GOTO YmlMissing
 call conda activate %conda_env_name% 2>nul:
 if not errorlevel 1 (
  echo Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment %conda_env_name%
  call pip install --upgrade azureml-sdk[automl,notebooks,explain]
  if errorlevel 1 goto ErrorExit
 ) else (
  call conda env create -f %automl_env_file% -n %conda_env_name%
 )
 call conda activate %conda_env_name% 2>nul:
 if errorlevel 1 goto ErrorExit
 call python -m ipykernel install --user --name %conda_env_name% --display-name "Python (%conda_env_name%)"
 REM azureml.widgets is now installed as part of the pip install under the conda env.
 REM Removing the old user install so that the notebooks will use the latest widget.
 call jupyter nbextension uninstall --user --py azureml.widgets
 echo.
 echo.
 echo ***************************************
 echo * AutoML setup completed successfully *
 echo ***************************************
 IF NOT "%options%"=="nolaunch" (
  echo.
  echo Starting jupyter notebook - please run the configuration notebook 
  echo.
  jupyter notebook --log-level=50 --notebook-dir='..\..'
 )
 goto End
 :YmlMissing
 echo File %automl_env_file% not found.
 :ErrorExit
 echo Install failed
 :End
--- a/how-to-use-azureml/automated-machine-learning/automl_setup_linux.sh
+++ b/how-to-use-azureml/automated-machine-learning/automl_setup_linux.sh
@@ -0,0 +1,52 @@
 #!/bin/bash
 CONDA_ENV_NAME=$1
 AUTOML_ENV_FILE=$2
 OPTIONS=$3
 PIP_NO_WARN_SCRIPT_LOCATION=0
 if [ "$CONDA_ENV_NAME" == "" ]
 then
  CONDA_ENV_NAME="azure_automl"
 fi
 if [ "$AUTOML_ENV_FILE" == "" ]
 then
  AUTOML_ENV_FILE="automl_env.yml"
 fi
 if [ ! -f $AUTOML_ENV_FILE ]; then
    echo "File $AUTOML_ENV_FILE not found"
    exit 1
 fi
 if source activate $CONDA_ENV_NAME 2> /dev/null
 then
   echo "Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment" $CONDA_ENV_NAME
   pip install --upgrade azureml-sdk[automl,notebooks,explain] &&
   jupyter nbextension uninstall --user --py azureml.widgets
 else
   conda env create -f $AUTOML_ENV_FILE -n $CONDA_ENV_NAME &&
   source activate $CONDA_ENV_NAME &&
   python -m ipykernel install --user --name $CONDA_ENV_NAME --display-name "Python ($CONDA_ENV_NAME)" &&
   jupyter nbextension uninstall --user --py azureml.widgets &&
   echo "" &&
   echo "" &&
   echo "***************************************" &&
   echo "* AutoML setup completed successfully *" &&
   echo "***************************************" &&
   if [ "$OPTIONS" != "nolaunch" ]
   then
      echo "" &&
      echo "Starting jupyter notebook - please run the configuration notebook" &&
      echo "" &&
      jupyter notebook --log-level=50 --notebook-dir '../..'
   fi
 fi
 if [ $? -gt 0 ]
 then
   echo "Installation failed"
 fi
--- a/how-to-use-azureml/automated-machine-learning/automl_setup_mac.sh
+++ b/how-to-use-azureml/automated-machine-learning/automl_setup_mac.sh
@@ -0,0 +1,55 @@
 #!/bin/bash
 CONDA_ENV_NAME=$1
 AUTOML_ENV_FILE=$2
 OPTIONS=$3
 PIP_NO_WARN_SCRIPT_LOCATION=0
 if [ "$CONDA_ENV_NAME" == "" ]
 then
  CONDA_ENV_NAME="azure_automl"
 fi
 if [ "$AUTOML_ENV_FILE" == "" ]
 then
  AUTOML_ENV_FILE="automl_env_mac.yml"
 fi
 if [ ! -f $AUTOML_ENV_FILE ]; then
    echo "File $AUTOML_ENV_FILE not found"
    exit 1
 fi
 if source activate $CONDA_ENV_NAME 2> /dev/null
 then
   echo "Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment" $CONDA_ENV_NAME
   pip install --upgrade azureml-sdk[automl,notebooks,explain] &&
   jupyter nbextension uninstall --user --py azureml.widgets
 else
   conda env create -f $AUTOML_ENV_FILE -n $CONDA_ENV_NAME &&
   source activate $CONDA_ENV_NAME &&
   conda install lightgbm -c conda-forge -y &&
   python -m ipykernel install --user --name $CONDA_ENV_NAME --display-name "Python ($CONDA_ENV_NAME)" &&
   jupyter nbextension uninstall --user --py azureml.widgets &&
   pip install numpy==1.15.3 &&
   echo "" &&
   echo "" &&
   echo "***************************************" &&
   echo "* AutoML setup completed successfully *" &&
   echo "***************************************" &&
   if [ "$OPTIONS" != "nolaunch" ]
   then
      echo "" &&
      echo "Starting jupyter notebook - please run the configuration notebook" &&
      echo "" &&
      jupyter notebook --log-level=50 --notebook-dir '../..'
   fi
 fi
 if [ $? -gt 0 ]
 then
   echo "Installation failed"
 fi
--- a/how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb
@@ -13,11 +13,26 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "# AutoML 09: Classification with Deployment\n",
+        "# Automated Machine Learning\n",
        "_**Classification with Deployment**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Train](#Train)\n",
        "1. [Deploy](#Deploy)\n",
        "1. [Test](#Test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\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",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. Create an experiment using an existing workspace.\n",
@@ -27,14 +42,14 @@
        "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"
+        "8. Test the ACI service."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Create an Experiment\n",
+        "## Setup\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."
      ]
@@ -47,11 +62,8 @@
      "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",
@@ -87,15 +99,14 @@
        "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"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Diagnostics\n",
        "\n",
        "Opt-in diagnostics for better experience, quality, and security of future releases."
      ]
    },
@@ -113,15 +124,15 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Configure AutoML\n",
+        "## Train\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",
+        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
-        "|**max_time_sec**|Time limit in seconds for each iteration.|\n",
+        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
        "|**n_cross_validations**|Number of cross validation splits.|\n",
        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
@@ -143,7 +154,7 @@
        "                             name = experiment_name,\n",
        "                             debug_log = 'automl_errors.log',\n",
        "                             primary_metric = 'AUC_weighted',\n",
-        "                             max_time_sec = 1200,\n",
+        "                             iteration_timeout_minutes = 20,\n",
        "                             iterations = 10,\n",
        "                             n_cross_validations = 2,\n",
        "                             verbosity = logging.INFO,\n",
@@ -156,8 +167,6 @@
      "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."
      ]
@@ -171,10 +180,21 @@
        "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": [
        "## Deploy\n",
        "\n",
        "### 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*."
@@ -206,7 +226,8 @@
        "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."
+        "\n",
        "print(local_run.model_id) # This will be written to the script file later in the notebook."
      ]
    },
    {
@@ -226,6 +247,7 @@
        "import pickle\n",
        "import json\n",
        "import numpy\n",
        "import azureml.train.automl\n",
        "from sklearn.externals import joblib\n",
        "from azureml.core.model import Model\n",
        "\n",
@@ -298,15 +320,12 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "%%writefile myenv.yml\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "name: myenv\n",
+        "\n",
-        "channels:\n",
+        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-sdk[automl]'])\n",
-        "  - defaults\n",
+        "\n",
-        "dependencies:\n",
+        "conda_env_file_name = 'myenv.yml'\n",
-        "  - pip:\n",
+        "myenv.save_to_file('.', conda_env_file_name)"
        "    - numpy==1.14.2\n",
        "    - scikit-learn==0.19.2\n",
        "    - azureml-sdk[notebooks,automl]==<<azureml-version>>"
      ]
    },
    {
@@ -316,14 +335,14 @@
      "outputs": [],
      "source": [
        "# Substitute the actual version number in the environment file.\n",
-        "\n",
+        "# This is not strictly needed in this notebook because the model should have been generated using the current SDK version.\n",
-        "conda_env_file_name = 'myenv.yml'\n",
+        "# However, we include this in case this code is used on an experiment from a previous SDK version.\n",
        "\n",
        "with open(conda_env_file_name, 'r') as cefr:\n",
        "    content = cefr.read()\n",
        "\n",
        "with open(conda_env_file_name, 'w') as cefw:\n",
-        "    cefw.write(content.replace('<<azureml-version>>', dependencies['azureml-sdk']))\n",
+        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
        "\n",
        "# Substitute the actual model id in the script file.\n",
        "\n",
@@ -363,7 +382,10 @@
        "                     image_config = image_config, \n",
        "                     workspace = ws)\n",
        "\n",
-        "image.wait_for_creation(show_output = True)"
+        "image.wait_for_creation(show_output = True)\n",
        "\n",
        "if image.creation_state == 'Failed':\n",
        "    print(\"Image build log at: \" + image.image_build_log_uri)"
      ]
    },
    {
@@ -441,7 +463,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Test a Web Service"
+        "## Test"
      ]
    },
    {
--- a/how-to-use-azureml/automated-machine-learning/classification-with-whitelisting/auto-ml-classification-with-whitelisting.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-with-whitelisting/auto-ml-classification-with-whitelisting.ipynb
@@ -0,0 +1,398 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Automated Machine Learning\n",
        "_**Classification using whitelist models**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Data](#Data)\n",
        "1. [Train](#Train)\n",
        "1. [Results](#Results)\n",
        "1. [Test](#Test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "\n",
        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
        "\n",
        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "This notebooks shows how can automl can be trained on a a selected list of models,see the readme.md for the models.\n",
        "This trains the model exclusively on tensorflow based models.\n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. Create an `Experiment` in an existing `Workspace`.\n",
        "2. Configure AutoML using `AutoMLConfig`.\n",
        "3. Train the model on a whilelisted models using local compute. \n",
        "4. Explore the results.\n",
        "5. Test the best fitted model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\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",
        "\n",
        "from matplotlib import pyplot as plt\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"
      ]
    },
    {
      "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-whitelist'\n",
        "project_folder = './sample_projects/automl-local-whitelist'\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",
        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "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": [
        "## 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": [
        "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": [
        "## Train\n",
        "\n",
        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
        "|**task**|classification or regression|\n",
        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
        "|**n_cross_validations**|Number of cross validation splits.|\n",
        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
        "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
        "|**whitelist_models**|List of models that AutoML should use.  The possible values are listed [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#configure-your-experiment-settings).|"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             debug_log = 'automl_errors.log',\n",
        "                             primary_metric = 'AUC_weighted',\n",
        "                             iteration_timeout_minutes = 60,\n",
        "                             iterations = 10,\n",
        "                             n_cross_validations = 3,\n",
        "                             verbosity = logging.INFO,\n",
        "                             X = X_train, \n",
        "                             y = y_train,\n",
        "                             enable_tf=True,\n",
        "                             whitelist_models=[\"TensorFlowLinearClassifier\", \"TensorFlowDNN\"],\n",
        "                             path = project_folder)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "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": [
        "## Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Widget for Monitoring Runs\n",
        "\n",
        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
        "\n",
        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(local_run).show() "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n",
        "#### Retrieve All Child Runs\n",
        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "children = list(local_run.get_children())\n",
        "metricslist = {}\n",
        "for run in children:\n",
        "    properties = run.get_properties()\n",
        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
        "    metricslist[int(properties['iteration'])] = metrics\n",
        "\n",
        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
        "rundata"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Retrieve the Best Model\n",
        "\n",
        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run, fitted_model = local_run.get_output()\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Best Model Based on Any Other Metric\n",
        "Show the run and the model that has the smallest `log_loss` value:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "lookup_metric = \"log_loss\"\n",
        "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Model from a Specific Iteration\n",
        "Show the run and the model from the third iteration:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "iteration = 3\n",
        "third_run, third_model = local_run.get_output(iteration = iteration)\n",
        "print(third_run)\n",
        "print(third_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Test\n",
        "\n",
        "#### Load Test Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "digits = datasets.load_digits()\n",
        "X_test = digits.data[:10, :]\n",
        "y_test = digits.target[:10]\n",
        "images = digits.images[:10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Testing Our Best Fitted Model\n",
        "We will try to predict 2 digits and see how our model works."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Randomly select digits and test.\n",
        "for index in np.random.choice(len(y_test), 2, replace = False):\n",
        "    print(index)\n",
        "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
        "    label = y_test[index]\n",
        "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
        "    fig = plt.figure(1, figsize = (3,3))\n",
        "    ax1 = fig.add_axes((0,0,.8,.8))\n",
        "    ax1.set_title(title)\n",
        "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
        "    plt.show()"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "savitam"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.ipynb
@@ -13,25 +13,42 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "# AutoML 01: Classification with Local Compute\n",
+        "# Automated Machine Learning\n",
        "_**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",
+        "## Contents\n",
-        "\n",
+        "1. [Introduction](#Introduction)\n",
-        "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+        "1. [Setup](#Setup)\n",
-        "\n",
+        "1. [Data](#Data)\n",
-        "In this notebook you will learn how to:\n",
+        "1. [Train](#Train)\n",
-        "1. Create an `Experiment` in an existing `Workspace`.\n",
+        "1. [Results](#Results)\n",
-        "2. Configure AutoML using `AutoMLConfig`.\n",
+        "1. [Test](#Test)\n",
-        "3. Train the model using local compute.\n",
+        "\n"
        "4. Explore the results.\n",
        "5. Test the best fitted model.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Create an Experiment\n",
+        "## Introduction\n",
        "\n",
        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
        "\n",
        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you 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."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\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."
      ]
@@ -43,11 +60,8 @@
      "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",
@@ -55,8 +69,7 @@
        "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 import AutoMLConfig"
        "from azureml.train.automl.run import AutoMLRun"
      ]
    },
    {
@@ -82,15 +95,14 @@
        "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"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Diagnostics\n",
        "\n",
        "Opt-in diagnostics for better experience, quality, and security of future releases."
      ]
    },
@@ -108,7 +120,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Load Training Data\n",
+        "## Data\n",
        "\n",
        "This uses scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
      ]
@@ -119,8 +131,6 @@
      "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",
@@ -132,15 +142,15 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Configure AutoML\n",
+        "## Train\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",
+        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
-        "|**max_time_sec**|Time limit in seconds for each iteration.|\n",
+        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
        "|**n_cross_validations**|Number of cross validation splits.|\n",
        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
@@ -157,8 +167,8 @@
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             debug_log = 'automl_errors.log',\n",
        "                             primary_metric = 'AUC_weighted',\n",
-        "                             max_time_sec = 3600,\n",
+        "                             iteration_timeout_minutes = 60,\n",
-        "                             iterations = 50,\n",
+        "                             iterations = 25,\n",
        "                             n_cross_validations = 3,\n",
        "                             verbosity = logging.INFO,\n",
        "                             X = X_train, \n",
@@ -170,8 +180,6 @@
      "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."
      ]
@@ -213,20 +221,11 @@
        "                                          iterations = 5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Explore the Results"
+        "## Results"
      ]
    },
    {
@@ -246,7 +245,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "from azureml.train.widgets import RunDetails\n",
+        "from azureml.widgets import RunDetails\n",
        "RunDetails(local_run).show() "
      ]
    },
@@ -340,7 +339,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Test the Best Fitted Model\n",
+        "## Test \n",
        "\n",
        "#### Load Test Data"
      ]
--- a/how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb
@@ -13,10 +13,26 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "# AutoML 13: Prepare Data using `azureml.dataprep`\n",
+        "# Automated Machine Learning\n",
        "_**Prepare Data using `azureml.dataprep` for Remote Execution (DSVM)**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Data](#Data)\n",
        "1. [Train](#Train)\n",
        "1. [Results](#Results)\n",
        "1. [Test](#Test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\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",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. Define data loading and preparation steps in a `Dataflow` using `azureml.dataprep`.\n",
@@ -28,24 +44,15 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Install `azureml.dataprep` SDK"
+        "## Setup\n",
-      ]
+        "\n",
-    },
+        "Currently, Data Prep only supports __Ubuntu 16__ and __Red Hat Enterprise Linux 7__. We are working on supporting more linux distros."
    {
      "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."
      ]
    },
@@ -63,8 +70,6 @@
      "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."
      ]
    },
@@ -75,15 +80,13 @@
      "outputs": [],
      "source": [
        "import logging\n",
-        "import os\n",
+        "import time\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.compute import DsvmCompute\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.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"
@@ -98,9 +101,9 @@
        "ws = Workspace.from_config()\n",
        " \n",
        "# choose a name for experiment\n",
-        "experiment_name = 'automl-dataprep-classification'\n",
+        "experiment_name = 'automl-dataprep-remote-dsvm'\n",
        "# project folder\n",
-        "project_folder = './sample_projects/automl-dataprep-classification'\n",
+        "project_folder = './sample_projects/automl-dataprep-remote-dsvm'\n",
        " \n",
        "experiment = Experiment(ws, experiment_name)\n",
        " \n",
@@ -113,14 +116,15 @@
        "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"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Loading Data using DataPrep"
+        "## Data"
      ]
    },
    {
@@ -129,10 +133,10 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "# You can use `smart_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
+        "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
        "# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
        "simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
-        "X = dprep.smart_read_file(simple_example_data_root + 'X.csv').skip(1)  # Remove the header row.\n",
+        "X = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1)  # Remove the header row.\n",
        "\n",
        "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
        "# and convert column types manually.\n",
@@ -144,8 +148,6 @@
      "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."
      ]
    },
@@ -162,7 +164,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Configure AutoML\n",
+        "## Train\n",
        "\n",
        "This creates a general AutoML settings object applicable for both local and remote runs."
      ]
@@ -174,7 +176,7 @@
      "outputs": [],
      "source": [
        "automl_settings = {\n",
-        "    \"max_time_sec\" : 600,\n",
+        "    \"iteration_timeout_minutes\" : 10,\n",
        "    \"iterations\" : 2,\n",
        "    \"primary_metric\" : 'AUC_weighted',\n",
        "    \"preprocess\" : False,\n",
@@ -183,51 +185,6 @@
        "}"
      ]
    },
    {
      "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": {},
@@ -241,52 +198,38 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "dsvm_name = 'mydsvm'\n",
+        "dsvm_name = 'mydsvmc'\n",
        "\n",
        "try:\n",
        "    while ws.compute_targets[dsvm_name].provisioning_state == 'Creating':\n",
        "        time.sleep(1)\n",
        "        \n",
        "    dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
        "    print('Found existing DVSM.')\n",
        "except:\n",
        "    print('Creating a new DSVM.')\n",
        "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
        "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
-        "    dsvm_compute.wait_for_completion(show_output = True)"
+        "    dsvm_compute.wait_for_completion(show_output = True)\n",
        "    print(\"Waiting one minute for ssh to be accessible\")\n",
        "    time.sleep(60) # Wait for ssh to be accessible"
      ]
    },
    {
-      "cell_type": "markdown",
+      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
-        "### Update Conda Dependency file to have AutoML and DataPrep SDK\n",
+        "from azureml.core.runconfig import RunConfiguration\n",
        "from azureml.core.conda_dependencies import CondaDependencies\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."
+        "conda_run_config = RunConfiguration(framework=\"python\")\n",
-      ]
+        "\n",
-    },
+        "conda_run_config.target = dsvm_compute\n",
-    {
+        "\n",
-      "cell_type": "code",
+        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
-      "execution_count": null,
+        "conda_run_config.environment.python.conda_dependencies = cd"
      "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"
      ]
    },
    {
@@ -307,18 +250,35 @@
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             debug_log = 'automl_errors.log',\n",
        "                             path = project_folder,\n",
-        "                             run_configuration = run_config,\n",
+        "                             run_configuration=conda_run_config,\n",
        "                             X = X,\n",
        "                             y = y,\n",
-        "                             **automl_settings)\n",
+        "                             **automl_settings)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "remote_run = experiment.submit(automl_config, show_output = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "remote_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Explore the Results"
+        "## Results"
      ]
    },
    {
@@ -338,8 +298,8 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "from azureml.train.widgets import RunDetails\n",
+        "from azureml.widgets import RunDetails\n",
-        "RunDetails(local_run).show()"
+        "RunDetails(remote_run).show()"
      ]
    },
    {
@@ -356,14 +316,13 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "children = list(local_run.get_children())\n",
+        "children = list(remote_run.get_children())\n",
        "metricslist = {}\n",
        "for run in children:\n",
        "    properties = run.get_properties()\n",
        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
        "    metricslist[int(properties['iteration'])] = metrics\n",
        "    \n",
        "import pandas as pd\n",
        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
        "rundata"
      ]
@@ -383,7 +342,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "best_run, fitted_model = local_run.get_output()\n",
+        "best_run, fitted_model = remote_run.get_output()\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
@@ -403,7 +362,7 @@
      "outputs": [],
      "source": [
        "lookup_metric = \"log_loss\"\n",
-        "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
+        "best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
@@ -423,7 +382,7 @@
      "outputs": [],
      "source": [
        "iteration = 0\n",
-        "best_run, fitted_model = local_run.get_output(iteration = iteration)\n",
+        "best_run, fitted_model = remote_run.get_output(iteration = iteration)\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
@@ -432,7 +391,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Test the Best Fitted Model\n",
+        "## Test\n",
        "\n",
        "#### Load Test Data"
      ]
@@ -467,8 +426,6 @@
      "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",
@@ -506,7 +463,7 @@
      "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')"
+        "digits_complete = dprep.auto_read_file('https://dprepdata.blob.core.windows.net/automl-notebook-data/digits-complete.csv')"
      ]
    },
    {
@@ -522,7 +479,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "digits_complete.to_pandas_dataframe().shape\n",
+        "print(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])"
--- a/how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.ipynb
@@ -0,0 +1,466 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Automated Machine Learning\n",
        "_**Prepare Data using `azureml.dataprep` for Local Execution**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Data](#Data)\n",
        "1. [Train](#Train)\n",
        "1. [Results](#Results)\n",
        "1. [Test](#Test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "In this example we showcase how you can use the `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone; full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
        "\n",
        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. Define data loading and preparation steps in a `Dataflow` using `azureml.dataprep`.\n",
        "2. Pass the `Dataflow` to AutoML for a local run.\n",
        "3. Pass the `Dataflow` to AutoML for a remote run."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\n",
        "\n",
        "Currently, Data Prep only supports __Ubuntu 16__ and __Red Hat Enterprise Linux 7__. We are working on supporting more linux distros."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "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": [
        "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",
        "\n",
        "import pandas as pd\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "import azureml.dataprep as dprep\n",
        "from azureml.train.automl import AutoMLConfig"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        " \n",
        "# choose a name for experiment\n",
        "experiment_name = 'automl-dataprep-local'\n",
        "# project folder\n",
        "project_folder = './sample_projects/automl-dataprep-local'\n",
        " \n",
        "experiment = Experiment(ws, experiment_name)\n",
        " \n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace Name'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Project Directory'] = project_folder\n",
        "output['Experiment Name'] = experiment.name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
        "# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
        "simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
        "X = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1)  # Remove the header row.\n",
        "\n",
        "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
        "# and convert column types manually.\n",
        "# Here we read a comma delimited file and convert all columns to integers.\n",
        "y = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Review the Data Preparation Result\n",
        "\n",
        "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "X.skip(1).head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train\n",
        "\n",
        "This creates a general AutoML settings object applicable for both local and remote runs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_settings = {\n",
        "    \"iteration_timeout_minutes\" : 10,\n",
        "    \"iterations\" : 2,\n",
        "    \"primary_metric\" : 'AUC_weighted',\n",
        "    \"preprocess\" : False,\n",
        "    \"verbosity\" : logging.INFO,\n",
        "    \"n_cross_validations\": 3\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 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": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Widget for Monitoring Runs\n",
        "\n",
        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
        "\n",
        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(local_run).show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Retrieve All Child Runs\n",
        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "children = list(local_run.get_children())\n",
        "metricslist = {}\n",
        "for run in children:\n",
        "    properties = run.get_properties()\n",
        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
        "    metricslist[int(properties['iteration'])] = metrics\n",
        "    \n",
        "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\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",
        "import numpy as np\n",
        "\n",
        "for index in np.random.choice(len(y_test), 2, replace = False):\n",
        "    print(index)\n",
        "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
        "    label = y_test[index]\n",
        "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
        "    fig = plt.figure(1, figsize=(3,3))\n",
        "    ax1 = fig.add_axes((0,0,.8,.8))\n",
        "    ax1.set_title(title)\n",
        "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Appendix"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Capture the `Dataflow` Objects for Later Use in AutoML\n",
        "\n",
        "`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object can be branched at any point for further usage."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# sklearn.digits.data + target\n",
        "digits_complete = dprep.auto_read_file('https://dprepdata.blob.core.windows.net/automl-notebook-data/digits-complete.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`digits_complete` (sourced from `sklearn.datasets.load_digits()`) is forked into `dflow_X` to capture all the feature columns and `dflow_y` to capture the label column."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print(digits_complete.to_pandas_dataframe().shape)\n",
        "labels_column = 'Column64'\n",
        "dflow_X = digits_complete.drop_columns(columns = [labels_column])\n",
        "dflow_y = digits_complete.keep_columns(columns = [labels_column])"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "savitam"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/exploring-previous-runs/auto-ml-exploring-previous-runs.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/exploring-previous-runs/auto-ml-exploring-previous-runs.ipynb
@@ -13,24 +13,38 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "# AutoML 07: Exploring Previous Runs\n",
+        "# Automated Machine Learning\n",
        "_**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",
+        "## Contents\n",
-        "\n",
+        "1. [Introduction](#Introduction)\n",
-        "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+        "1. [Setup](#Setup)\n",
-        "\n",
+        "1. [Explore](#Explore)\n",
-        "In this notebook you will learn how to:\n",
+        "1. [Download](#Download)\n",
-        "1. List all experiments in a workspace.\n",
+        "1. [Register](#Register)"
        "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"
+        "## Introduction\n",
        "In this example we present some examples on navigating previously executed runs. We also show how you can download a fitted model for any previous run.\n",
        "\n",
        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. List all experiments in a workspace.\n",
        "2. List all AutoML runs in an experiment.\n",
        "3. Get details for an AutoML run, including settings, run widget, and all metrics.\n",
        "4. Download a fitted pipeline for any iteration."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup"
      ]
    },
    {
@@ -39,22 +53,11 @@
      "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",
+        "import json\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"
      ]
    },
@@ -64,29 +67,13 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "ws = Workspace.from_config()\n",
+        "ws = Workspace.from_config()"
        "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."
      ]
    },
@@ -104,7 +91,38 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "# List AutoML runs for an experiment\n",
+        "## Explore"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### List Experiments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "experiment_list = Experiment.list(workspace=ws)\n",
        "\n",
        "summary_df = pd.DataFrame(index = ['No of Runs'])\n",
        "for experiment in experiment_list:\n",
        "    automl_runs = list(experiment.get_runs(type='automl'))\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": [
        "### List 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."
      ]
    },
@@ -116,20 +134,21 @@
      "source": [
        "experiment_name = 'automl-local-classification' # Replace this with any project name from previous cell.\n",
        "\n",
-        "proj = ws.experiments()[experiment_name]\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",
+        "automl_runs = list(proj.get_runs(type='automl'))\n",
-        "all_runs = list(proj.get_runs(properties={'azureml.runsource': 'automl'}))\n",
+        "automl_runs_project = []\n",
-        "for run in all_runs:\n",
+        "for run in automl_runs:\n",
        "    if(pattern.match(run.id)):\n",
        "    properties = run.get_properties()\n",
        "    tags = run.get_tags()\n",
-        "        amlsettings = eval(properties['RawAMLSettingsString'])\n",
+        "    amlsettings = json.loads(properties['AMLSettingsJsonString'])\n",
        "    if 'iterations' in tags:\n",
        "        iterations = tags['iterations']\n",
        "    else:\n",
        "        iterations = properties['num_iterations']\n",
        "    summary_df[run.id] = [amlsettings['task_type'], run.get_details()['status'], properties['primary_metric'], iterations, properties['target'], amlsettings['name']]\n",
        "    if run.get_details()['status'] == 'Completed':\n",
        "        automl_runs_project.append(run.id)\n",
        "    \n",
        "from IPython.display import HTML\n",
        "projname_html = HTML(\"<h3>{}</h3>\".format(proj.name))\n",
@@ -143,7 +162,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "# Get details for an AutoML run\n",
+        "### Get details for a run\n",
        "\n",
        "Copy the project name and run id from the previous cell output to find more details on a particular run."
      ]
@@ -154,10 +173,10 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "run_id = '' # Filling your own run_id from above run ids\n",
+        "run_id = automl_runs_project[0]  # Replace with your own run_id from above run ids\n",
        "assert (run_id in summary_df.keys()), \"Run id not found! Please set run id to a value from above run ids\"\n",
        "\n",
-        "from azureml.train.widgets import RunDetails\n",
+        "from azureml.widgets import RunDetails\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "ml_run = AutoMLRun(experiment = experiment, run_id = run_id)\n",
@@ -166,7 +185,7 @@
        "properties = ml_run.get_properties()\n",
        "tags = ml_run.get_tags()\n",
        "status = ml_run.get_details()\n",
-        "amlsettings = eval(properties['RawAMLSettingsString'])\n",
+        "amlsettings = json.loads(properties['AMLSettingsJsonString'])\n",
        "if 'iterations' in tags:\n",
        "    iterations = tags['iterations']\n",
        "else:\n",
@@ -204,14 +223,14 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "# Download fitted models"
+        "## Download"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Download the Best Model for Any Given Metric"
+        "### Download the Best Model for Any Given Metric"
      ]
    },
    {
@@ -229,7 +248,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Download the Model for Any Given Iteration"
+        "### Download the Model for Any Given Iteration"
      ]
    },
    {
@@ -238,7 +257,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "iteration = 4 # Replace with an iteration number.\n",
+        "iteration = 1 # Replace with an iteration number.\n",
        "best_run, fitted_model = ml_run.get_output(iteration = iteration)\n",
        "fitted_model"
      ]
@@ -247,7 +266,14 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "# Register fitted model for deployment\n",
+        "## Register"
      ]
    },
    {
      "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."
      ]
    },
@@ -260,14 +286,14 @@
        "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."
+        "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 Best Model for Any Given Metric"
+        "### Register the Best Model for Any Given Metric"
      ]
    },
    {
@@ -287,7 +313,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Register the Model for Any Given Iteration"
+        "### Register the Model for Any Given Iteration"
      ]
    },
    {
@@ -296,7 +322,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "iteration = 4 # Replace with an iteration number.\n",
+        "iteration = 1 # Replace with an iteration number.\n",
        "description = 'AutoML Model'\n",
        "tags = None\n",
        "ml_run.register_model(description = description, tags = tags, iteration = iteration)\n",
--- a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb
@@ -0,0 +1,405 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Automated Machine Learning\n",
        "_**Energy Demand Forecasting**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Data](#Data)\n",
        "1. [Train](#Train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "In this example, we show how AutoML can be used for energy demand forecasting.\n",
        "\n",
        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you would see\n",
        "1. Creating an Experiment in an existing Workspace\n",
        "2. Instantiating AutoMLConfig with new task type \"forecasting\" for timeseries data training, and other timeseries related settings: for this dataset we use the basic one: \"time_column_name\" \n",
        "3. Training the Model using local compute\n",
        "4. Exploring the results\n",
        "5. Testing the fitted model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To use the *forecasting* task in AutoML, you need to have the **azuremlftk** package installed in your environment. The following cell tests whether this package is installed locally and, if not, gives you instructions for installing it.  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "try:\n",
        "    import ftk\n",
        "    print('Using FTK version ' + ftk.__version__)\n",
        "except ImportError:\n",
        "    print(\"Unable to import forecasting package. This notebook does not work without this package.\\n\"\n",
        "          + \"Please open a command prompt and run `pip install azuremlftk` to install the package. \\n\"\n",
        "          + \"Make sure you install the package into AutoML's Python environment.\\n\\n\"\n",
        "          + \"For instance, if AutoML is installed in a conda environment called `python36`, run:\\n\"\n",
        "          + \"> activate python36\\n> pip install azuremlftk\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import azureml.core\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import logging\n",
        "import warnings\n",
        "# Squash warning messages for cleaner output in the notebook\n",
        "warnings.showwarning = lambda *args, **kwargs: None\n",
        "\n",
        "\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.train.automl import AutoMLConfig\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "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": [
        "ws = Workspace.from_config()\n",
        "\n",
        "# choose a name for the run history container in the workspace\n",
        "experiment_name = 'automl-energydemandforecasting'\n",
        "# project folder\n",
        "project_folder = './sample_projects/automl-local-energydemandforecasting'\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Project Directory'] = project_folder\n",
        "output['Run History Name'] = experiment_name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data\n",
        "Read energy demanding data from file, and preview data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "data = pd.read_csv(\"nyc_energy.csv\", parse_dates=['timeStamp'])\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Split the data to train and test\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "train = data[data['timeStamp'] < '2017-02-01']\n",
        "test = data[data['timeStamp'] >= '2017-02-01']\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Prepare the test data, we will feed X_test to the fitted model and get prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "y_test = test.pop('demand').values\n",
        "X_test = test"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Split the train data to train and valid\n",
        "\n",
        "Use one month's data as valid data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "X_train = train[train['timeStamp'] < '2017-01-01']\n",
        "X_valid = train[train['timeStamp'] >= '2017-01-01']\n",
        "y_train = X_train.pop('demand').values\n",
        "y_valid = X_valid.pop('demand').values\n",
        "print(X_train.shape)\n",
        "print(y_train.shape)\n",
        "print(X_valid.shape)\n",
        "print(y_valid.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train\n",
        "\n",
        "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
        "|**task**|forecasting|\n",
        "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
        "|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
        "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
        "|**X_valid**|Data used to evaluate a model in a iteration. (sparse) array-like, shape = [n_samples, n_features]|\n",
        "|**y_valid**|Data used to evaluate a model in a iteration. (sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
        "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "time_column_name = 'timeStamp'\n",
        "automl_settings = {\n",
        "    \"time_column_name\": time_column_name,\n",
        "}\n",
        "\n",
        "\n",
        "automl_config = AutoMLConfig(task = 'forecasting',\n",
        "                             debug_log = 'automl_nyc_energy_errors.log',\n",
        "                             primary_metric='normalized_root_mean_squared_error',\n",
        "                             iterations = 10,\n",
        "                             iteration_timeout_minutes = 5,\n",
        "                             X = X_train,\n",
        "                             y = y_train,\n",
        "                             X_valid = X_valid,\n",
        "                             y_valid = y_valid,\n",
        "                             path=project_folder,\n",
        "                             verbosity = logging.INFO,\n",
        "                            **automl_settings)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
        "You will see the currently running iterations printing to the console."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run = experiment.submit(automl_config, show_output=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Retrieve the Best Model\n",
        "Below we select the best pipeline from our iterations. The get_output method on automl_classifier returns the best run and the fitted model for the last fit invocation. There are overloads on get_output that allow you to retrieve the best run and fitted model for any logged metric or a particular iteration."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run, fitted_model = local_run.get_output()\n",
        "fitted_model.steps"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Test the Best Fitted Model\n",
        "\n",
        "Predict on training and test set, and calculate residual values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "y_pred = fitted_model.predict(X_test)\n",
        "y_pred"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Use the Check Data Function to remove the nan values from y_test to avoid error when calculate metrics "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "if len(y_test) != len(y_pred):\n",
        "    raise ValueError(\n",
        "        'the true values and prediction values do not have equal length.')\n",
        "elif len(y_test) == 0:\n",
        "    raise ValueError(\n",
        "        'y_true and y_pred are empty.')\n",
        "\n",
        "# if there is any non-numeric element in the y_true or y_pred,\n",
        "# the ValueError exception will be thrown.\n",
        "y_test_f = np.array(y_test).astype(float)\n",
        "y_pred_f = np.array(y_pred).astype(float)\n",
        "\n",
        "# remove entries both in y_true and y_pred where at least\n",
        "# one element in y_true or y_pred is missing\n",
        "y_test = y_test_f[~(np.isnan(y_test_f) | np.isnan(y_pred_f))]\n",
        "y_pred = y_pred_f[~(np.isnan(y_test_f) | np.isnan(y_pred_f))]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Calculate metrics for the prediction\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
        "# Explained variance score: 1 is perfect prediction\n",
        "print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
        "print('R2 score: %.2f' % r2_score(y_test, y_pred))\n",
        "\n",
        "\n",
        "\n",
        "# Plot outputs\n",
        "%matplotlib notebook\n",
        "test_pred = plt.scatter(y_test, y_pred, color='b')\n",
        "test_test = plt.scatter(y_test, y_test, color='g')\n",
        "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "xiaga"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.8"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/nyc_energy.csv
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/nyc_energy.csv
--- a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
@@ -0,0 +1,441 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Automated Machine Learning\n",
        "_**Orange Juice Sales Forecasting**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Data](#Data)\n",
        "1. [Train](#Train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "In this example, we use AutoML to find and tune a time-series forecasting model.\n",
        "\n",
        "Make sure you have executed the [configuration notebook](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook, you will:\n",
        "1. Create an Experiment in an existing Workspace\n",
        "2. Instantiate an AutoMLConfig \n",
        "3. Find and train a forecasting model using local compute\n",
        "4. Evaluate the performance of the model\n",
        "\n",
        "The examples in the follow code samples use the [University of Chicago's Dominick's Finer Foods dataset](https://research.chicagobooth.edu/kilts/marketing-databases/dominicks) to forecast orange juice sales. Dominick's was a grocery chain in the Chicago metropolitan area."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To use the *forecasting* task in AutoML, you need to have the **azuremlftk** package installed in your environment. The following cell tests whether this package is installed locally and, if not, gives you instructions for installing it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "try:\n",
        "    import ftk\n",
        "    print('Using FTK version ' + ftk.__version__)\n",
        "except ImportError:\n",
        "    print(\"Unable to import forecasting package. This notebook does not work without this package.\\n\"\n",
        "          + \"Please open a command prompt and run `pip install azuremlftk` to install the package. \\n\"\n",
        "          + \"Make sure you install the package into AutoML's Python environment.\\n\\n\"\n",
        "          + \"For instance, if AutoML is installed in a conda environment called `python36`, run:\\n\"\n",
        "          + \"> activate python36\\n> pip install azuremlftk\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import azureml.core\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import logging\n",
        "import warnings\n",
        "# Squash warning messages for cleaner output in the notebook\n",
        "warnings.showwarning = lambda *args, **kwargs: None\n",
        "\n",
        "\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.train.automl import AutoMLConfig\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "As part of the setup you have already created a <b>Workspace</b>. To run AutoML, you also need to create an <b>Experiment</b>. An Experiment is a named object in a Workspace which represents a predictive task, the output of which is a trained model and a set of evaluation metrics for the model. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "\n",
        "# choose a name for the run history container in the workspace\n",
        "experiment_name = 'automl-ojsalesforecasting'\n",
        "# project folder\n",
        "project_folder = './sample_projects/automl-local-ojsalesforecasting'\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Project Directory'] = project_folder\n",
        "output['Run History Name'] = experiment_name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data\n",
        "You are now ready to load the historical orange juice sales data. We will load the CSV file into a plain pandas DataFrame; the time column in the CSV is called _WeekStarting_, so it will be specially parsed into the datetime type."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "time_column_name = 'WeekStarting'\n",
        "data = pd.read_csv(\"dominicks_OJ.csv\", parse_dates=[time_column_name])\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Each row in the DataFrame holds a quantity of weekly sales for an OJ brand at a single store. The data also includes the sales price, a flag indicating if the OJ brand was advertised in the store that week, and some customer demographic information based on the store location. For historical reasons, the data also include the logarithm of the sales quantity. The Dominick's grocery data is commonly used to illustrate econometric modeling techniques where logarithms of quantities are generally preferred.    \n",
        "\n",
        "The task is now to build a time-series model for the _Quantity_ column. It is important to note that this dataset is comprised of many individual time-series - one for each unique combination of _Store_ and _Brand_. To distinguish the individual time-series, we thus define the **grain** - the columns whose values determine the boundaries between time-series: "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "grain_column_names = ['Store', 'Brand']\n",
        "nseries = data.groupby(grain_column_names).ngroups\n",
        "print('Data contains {0} individual time-series.'.format(nseries))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Data Splitting\n",
        "For the purposes of demonstration and later forecast evaluation, we now split the data into a training and a testing set. The test set will contain the final 20 weeks of observed sales for each time-series."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ntest_periods = 20\n",
        "\n",
        "def split_last_n_by_grain(df, n):\n",
        "    \"\"\"\n",
        "    Group df by grain and split on last n rows for each group\n",
        "    \"\"\"\n",
        "    df_grouped = (df.sort_values(time_column_name) # Sort by ascending time\n",
        "                  .groupby(grain_column_names, group_keys=False))\n",
        "    df_head = df_grouped.apply(lambda dfg: dfg.iloc[:-n])\n",
        "    df_tail = df_grouped.apply(lambda dfg: dfg.iloc[-n:])\n",
        "    return df_head, df_tail\n",
        "\n",
        "X_train, X_test = split_last_n_by_grain(data, ntest_periods)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Modeling\n",
        "\n",
        "For forecasting tasks, AutoML uses pre-processing and estimation steps that are specific to time-series. AutoML will undertake the following pre-processing steps:\n",
        "* Detect time-series sample frequency (e.g. hourly, daily, weekly) and create new records for absent time points to make the series regular. A regular time series has a well-defined frequency and has a value at every sample point in a contiguous time span \n",
        "* Impute missing values in the target (via forward-fill) and feature columns (using median column values) \n",
        "* Create grain-based features to enable fixed effects across different series\n",
        "* Create time-based features to assist in learning seasonal patterns\n",
        "* Encode categorical variables to numeric quantities\n",
        "\n",
        "AutoML will currently train a single, regression-type model across **all** time-series in a given training set. This allows the model to generalize across related series.\n",
        "\n",
        "You are almost ready to start an AutoML training job. We will first need to create a validation set from the existing training set (i.e. for hyper-parameter tuning): "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "nvalidation_periods = 20\n",
        "X_train, X_validate = split_last_n_by_grain(X_train, nvalidation_periods)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We also need to separate the target column from the rest of the DataFrame: "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "target_column_name = 'Quantity'\n",
        "y_train = X_train.pop(target_column_name).values\n",
        "y_validate = X_validate.pop(target_column_name).values "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train\n",
        "\n",
        "The AutoMLConfig object defines the settings and data for an AutoML training job. Here, we set necessary inputs like the task type, the number of AutoML iterations to try, and the training and validation data. \n",
        "\n",
        "For forecasting tasks, there are some additional parameters that can be set: the name of the column holding the date/time and the grain column names. A time column is required for forecasting, while the grain is optional. If a grain is not given, the forecaster assumes that the whole dataset is a single time-series. We also pass a list of columns to drop prior to modeling. The _logQuantity_ column is completely correlated with the target quantity, so it must be removed to prevent a target leak. \n",
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
        "|**task**|forecasting|\n",
        "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
        "|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
        "|**X**|Training matrix of features, shape = [n_training_samples, n_features]|\n",
        "|**y**|Target values, shape = [n_training_samples, ]|\n",
        "|**X_valid**|Validation matrix of features, shape = [n_validation_samples, n_features]|\n",
        "|**y_valid**|Target values for validation, shape = [n_validation_samples, ]\n",
        "|**enable_ensembling**|Allow AutoML to create ensembles of the best performing models\n",
        "|**debug_log**|Log file path for writing debugging information\n",
        "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_settings = {\n",
        "    'time_column_name': time_column_name,\n",
        "    'grain_column_names': grain_column_names,\n",
        "    'drop_column_names': ['logQuantity']\n",
        "}\n",
        "\n",
        "automl_config = AutoMLConfig(task='forecasting',\n",
        "                             debug_log='automl_oj_sales_errors.log',\n",
        "                             primary_metric='normalized_root_mean_squared_error',\n",
        "                             iterations=10,\n",
        "                             X=X_train,\n",
        "                             y=y_train,\n",
        "                             X_valid=X_validate,\n",
        "                             y_valid=y_validate,\n",
        "                             enable_ensembling=False,\n",
        "                             path=project_folder,\n",
        "                             verbosity=logging.INFO,\n",
        "                            **automl_settings)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can now submit a new training run. For local runs, the execution is synchronous. Depending on the data and number of iterations this operation may take several minutes.\n",
        "Information from each iteration will be printed to the console."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run = experiment.submit(automl_config, show_output=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Retrieve the Best Model\n",
        "Each run within an Experiment stores serialized (i.e. pickled) pipelines from the AutoML iterations. We can now retrieve the pipeline with the best performance on the validation dataset:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run, fitted_pipeline = local_run.get_output()\n",
        "fitted_pipeline.steps"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Make Predictions from the Best Fitted Model\n",
        "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. First, we remove the target values from the test set:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "y_test = X_test.pop(target_column_name).values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "X_test.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To produce predictions on the test set, we need to know the feature values at all dates in the test set. This requirement is somewhat reasonable for the OJ sales data since the features mainly consist of price, which is usually set in advance, and customer demographics which are approximately constant for each store over the 20 week forecast horizon in the testing data. \n",
        "\n",
        "The target predictions can be retrieved by calling the `predict` method on the best model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "y_pred = fitted_pipeline.predict(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Calculate evaluation metrics for the prediction\n",
        "To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def MAPE(actual, pred):\n",
        "    \"\"\"\n",
        "    Calculate mean absolute percentage error.\n",
        "    Remove NA and values where actual is close to zero\n",
        "    \"\"\"\n",
        "    not_na = ~(np.isnan(actual) | np.isnan(pred))\n",
        "    not_zero = ~np.isclose(actual, 0.0)\n",
        "    actual_safe = actual[not_na & not_zero]\n",
        "    pred_safe = pred[not_na & not_zero]\n",
        "    APE = 100*np.abs((actual_safe - pred_safe)/actual_safe)\n",
        "    return np.mean(APE)\n",
        "\n",
        "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
        "print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
        "print('MAPE: %.2f' % MAPE(y_test, y_pred))"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "erwright"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.8"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/dominicks_OJ.csv
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/dominicks_OJ.csv
--- a/how-to-use-azureml/automated-machine-learning/missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.ipynb
@@ -13,11 +13,26 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "# AutoML 05: Blacklisting Models, Early Termination, and Handling Missing Data\n",
+        "# Automated Machine Learning\n",
        "_**Blacklisting Models, Early Termination, and Handling Missing Data**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Data](#Data)\n",
        "1. [Train](#Train)\n",
        "1. [Results](#Results)\n",
        "1. [Test](#Test)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\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",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. Create an `Experiment` in an existing `Workspace`.\n",
@@ -29,14 +44,14 @@
        "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"
+        "- Handling **missing data** in the input"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Create an Experiment\n",
+        "## Setup\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."
      ]
@@ -48,11 +63,8 @@
      "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",
@@ -60,8 +72,7 @@
        "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 import AutoMLConfig"
        "from azureml.train.automl.run import AutoMLRun"
      ]
    },
    {
@@ -87,15 +98,14 @@
        "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"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Diagnostics\n",
        "\n",
        "Opt-in diagnostics for better experience, quality, and security of future releases."
      ]
    },
@@ -113,7 +123,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Creating missing data"
+        "## Data"
      ]
    },
    {
@@ -122,8 +132,6 @@
      "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",
@@ -153,20 +161,20 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Configure AutoML\n",
+        "## Train\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",
+        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.  This includes setting `experiment_exit_score`, which should cause the run to complete before the `iterations` count is reached.\n",
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
        "|**task**|classification or regression|\n",
-        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
+        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
-        "|**max_time_sec**|Time limit in seconds for each iteration.|\n",
+        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
        "|**n_cross_validations**|Number of cross validation splits.|\n",
        "|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.|\n",
-        "|**exit_score**|*double* value indicating the target for *primary_metric*. <br>Once the target is surpassed the run terminates.|\n",
+        "|**experiment_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",
+        "|**blacklist_models**|*List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run.<br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGD</i><br><i>MultinomialNaiveBayes</i><br><i>BernoulliNaiveBayes</i><br><i>SVM</i><br><i>LinearSVM</i><br><i>KNN</i><br><i>DecisionTree</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>GradientBoosting</i><br><i>TensorFlowDNN</i><br><i>TensorFlowLinearClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i>|\n",
        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
        "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers.|\n",
        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
@@ -181,12 +189,12 @@
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             debug_log = 'automl_errors.log',\n",
        "                             primary_metric = 'AUC_weighted',\n",
-        "                             max_time_sec = 3600,\n",
+        "                             iteration_timeout_minutes = 60,\n",
        "                             iterations = 20,\n",
        "                             n_cross_validations = 5,\n",
        "                             preprocess = True,\n",
-        "                             exit_score = 0.9984,\n",
+        "                             experiment_exit_score = 0.9984,\n",
-        "                             blacklist_algos = ['KNeighborsClassifier','LinearSVMWrapper'],\n",
+        "                             blacklist_models = ['KNN','LinearSVM'],\n",
        "                             verbosity = logging.INFO,\n",
        "                             X = X_train, \n",
        "                             y = y_train,\n",
@@ -197,8 +205,6 @@
      "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."
      ]
@@ -212,11 +218,20 @@
        "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"
+        "## Results"
      ]
    },
    {
@@ -236,7 +251,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "from azureml.train.widgets import RunDetails\n",
+        "from azureml.widgets import RunDetails\n",
        "RunDetails(local_run).show() "
      ]
    },
@@ -324,7 +339,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Testing the best Fitted Model"
+        "## Test"
      ]
    },
    {
--- a/how-to-use-azureml/automated-machine-learning/model-explanation/auto-ml-model-explanation.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/model-explanation/auto-ml-model-explanation.ipynb
@@ -0,0 +1,365 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Automated Machine Learning\n",
        "_**Explain classification model and visualize the explanation**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Data](#Data)\n",
        "1. [Train](#Train)\n",
        "1. [Results](#Results)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "In this example we use the sklearn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) to showcase how you can use the AutoML Classifier for a simple classification problem.\n",
        "\n",
        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you would see\n",
        "1. Creating an Experiment in an existing Workspace\n",
        "2. Instantiating AutoMLConfig\n",
        "3. Training the Model using local compute and explain the model\n",
        "4. Visualization model's feature importance in widget\n",
        "5. Explore best model's explanation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\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",
        "\n",
        "import pandas as pd\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.train.automl import AutoMLConfig"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "\n",
        "# choose a name for experiment\n",
        "experiment_name = 'automl-local-classification'\n",
        "# project folder\n",
        "project_folder = './sample_projects/automl-local-classification-model-explanation'\n",
        "\n",
        "experiment=Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace Name'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Project Directory'] = project_folder\n",
        "output['Experiment Name'] = experiment.name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "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": [
        "## Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn import datasets\n",
        "\n",
        "iris = datasets.load_iris()\n",
        "y = iris.target\n",
        "X = iris.data\n",
        "\n",
        "features = iris.feature_names\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,\n",
        "                                                    y,\n",
        "                                                    test_size=0.1,\n",
        "                                                    random_state=100,\n",
        "                                                    stratify=y)\n",
        "\n",
        "X_train = pd.DataFrame(X_train, columns=features)\n",
        "X_test = pd.DataFrame(X_test, columns=features)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train\n",
        "\n",
        "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
        "|**task**|classification or regression|\n",
        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
        "|**max_time_sec**|Time limit in minutes for each iterations|\n",
        "|**iterations**|Number of iterations. In each iteration Auto ML trains the data with a specific pipeline|\n",
        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
        "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
        "|**X_valid**|(sparse) array-like, shape = [n_samples, n_features]|\n",
        "|**y_valid**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]|\n",
        "|**model_explainability**|Indicate to explain each trained pipeline or not |\n",
        "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. |"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             debug_log = 'automl_errors.log',\n",
        "                             primary_metric = 'AUC_weighted',\n",
        "                             iteration_timeout_minutes = 200,\n",
        "                             iterations = 10,\n",
        "                             verbosity = logging.INFO,\n",
        "                             X = X_train, \n",
        "                             y = y_train,\n",
        "                             X_valid = X_test,\n",
        "                             y_valid = y_test,\n",
        "                             model_explainability=True,\n",
        "                             path=project_folder)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
        "You will see the currently running iterations printing to the console."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run = experiment.submit(automl_config, show_output=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Widget for monitoring runs\n",
        "\n",
        "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
        "\n",
        "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(local_run).show() "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Retrieve the Best Model\n",
        "\n",
        "Below we select the best pipeline from our iterations. The *get_output* method on automl_classifier returns the best run and the fitted model for the last *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run, fitted_model = local_run.get_output()\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Best Model 's explanation\n",
        "\n",
        "Retrieve the explanation from the best_run. And explanation information includes:\n",
        "\n",
        "1.\tshap_values: The explanation information generated by shap lib\n",
        "2.\texpected_values: The expected value of the model applied to set of X_train data.\n",
        "3.\toverall_summary: The model level feature importance values sorted in descending order\n",
        "4.\toverall_imp: The feature names sorted in the same order as in overall_summary\n",
        "5.\tper_class_summary: The class level feature importance values sorted in descending order. Only available for the classification case\n",
        "6.\tper_class_imp: The feature names sorted in the same order as in per_class_summary. Only available for the classification case"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.train.automl.automlexplainer import retrieve_model_explanation\n",
        "\n",
        "shap_values, expected_values, overall_summary, overall_imp, per_class_summary, per_class_imp = \\\n",
        "    retrieve_model_explanation(best_run)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print(overall_summary)\n",
        "print(overall_imp)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print(per_class_summary)\n",
        "print(per_class_imp)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Beside retrieve the existed model explanation information, explain the model with different train/test data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.train.automl.automlexplainer import explain_model\n",
        "\n",
        "shap_values, expected_values, overall_summary, overall_imp, per_class_summary, per_class_imp = \\\n",
        "    explain_model(fitted_model, X_train, X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print(overall_summary)\n",
        "print(overall_imp)"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "xif"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb
@@ -13,25 +13,40 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "# AutoML 02: Regression with Local Compute\n",
+        "# Automated Machine Learning\n",
        "_**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",
+        "## Contents\n",
-        "\n",
+        "1. [Introduction](#Introduction)\n",
-        "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+        "1. [Setup](#Setup)\n",
-        "\n",
+        "1. [Data](#Data)\n",
-        "In this notebook you will learn how to:\n",
+        "1. [Train](#Train)\n",
-        "1. Create an `Experiment` in an existing `Workspace`.\n",
+        "1. [Results](#Results)\n",
-        "2. Configure AutoML using `AutoMLConfig`.\n",
+        "1. [Test](#Test)\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",
+        "## Introduction\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 [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. Create an `Experiment` in an existing `Workspace`.\n",
        "2. Configure AutoML using `AutoMLConfig`.\n",
        "3. Train the model using local compute.\n",
        "4. Explore the results.\n",
        "5. Test the best fitted model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\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."
      ]
@@ -43,20 +58,15 @@
      "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 import AutoMLConfig"
        "from azureml.train.automl.run import AutoMLRun"
      ]
    },
    {
@@ -82,15 +92,14 @@
        "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"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Diagnostics\n",
        "\n",
        "Opt-in diagnostics for better experience, quality, and security of future releases."
      ]
    },
@@ -108,7 +117,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Load Training Data\n",
+        "## Data\n",
        "This uses scikit-learn's [load_diabetes](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html) method."
      ]
    },
@@ -120,8 +129,6 @@
      "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",
@@ -135,7 +142,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Configure AutoML\n",
+        "## Train\n",
        "\n",
        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
        "\n",
@@ -143,7 +150,7 @@
        "|-|-|\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",
+        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
        "|**n_cross_validations**|Number of cross validation splits.|\n",
        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
@@ -158,7 +165,7 @@
      "outputs": [],
      "source": [
        "automl_config = AutoMLConfig(task = 'regression',\n",
-        "                             max_time_sec = 600,\n",
+        "                             iteration_timeout_minutes = 10,\n",
        "                             iterations = 10,\n",
        "                             primary_metric = 'spearman_correlation',\n",
        "                             n_cross_validations = 5,\n",
@@ -173,8 +180,6 @@
      "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."
      ]
@@ -201,7 +206,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Explore the Results"
+        "## Results"
      ]
    },
    {
@@ -221,7 +226,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "from azureml.train.widgets import RunDetails\n",
+        "from azureml.widgets import RunDetails\n",
        "RunDetails(local_run).show() "
      ]
    },
@@ -315,7 +320,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Test the Best Fitted Model"
+        "## Test"
      ]
    },
    {
@@ -345,9 +350,6 @@
      "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",
@@ -366,8 +368,8 @@
        "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', bins = 10, histtype = 'step')\n",
-        "a0.hist(y_residual_train, orientation = 'horizontal', color = 'b', alpha = 0.2, bins = 10);\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",
--- a/how-to-use-azureml/automated-machine-learning/remote-attach/auto-ml-remote-attach.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/remote-attach/auto-ml-remote-attach.ipynb
@@ -13,11 +13,26 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "# Auto ML 04: Remote Execution with Text Data from Azure Blob Storage\n",
+        "# Automated Machine Learning\n",
        "_**Remote Execution using attach**_\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",
+        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Data](#Data)\n",
        "1. [Train](#Train)\n",
        "1. [Results](#Results)\n",
        "1. [Test](#Test)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "In this example we use the scikit-learn's [20newsgroup](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_20newsgroups.html) to showcase how you can use AutoML to handle text data with remote attach.\n",
        "\n",
-        "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. Create an `Experiment` in an existing `Workspace`.\n",
@@ -33,14 +48,14 @@
        "- **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"
+        "- Handling **text** data using the `preprocess` flag"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Create an Experiment\n",
+        "## Setup\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."
      ]
@@ -51,21 +66,15 @@
      "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 import AutoMLConfig"
        "from azureml.train.automl.run import AutoMLRun"
      ]
    },
    {
@@ -77,8 +86,8 @@
        "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",
+        "experiment_name = 'automl-remote-attach'\n",
-        "project_folder = './sample_projects/automl-remote-dsvm-blobstore'\n",
+        "project_folder = './sample_projects/automl-remote-attach'\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
@@ -91,15 +100,14 @@
        "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"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Diagnostics\n",
        "\n",
        "Opt-in diagnostics for better experience, quality, and security of future releases."
      ]
    },
@@ -117,7 +125,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Attach a Remote Linux DSVM\n",
+        "### 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",
@@ -131,40 +139,60 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "from azureml.core.compute import RemoteCompute\n",
+        "from azureml.core.compute import ComputeTarget, RemoteCompute\n",
        "import time\n",
        "\n",
        "# Add your VM information below\n",
        "# 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",
+        "compute_name  = 'mydsvmb'\n",
        "dsvm_ip_addr  = '<<ip_addr>>'\n",
        "dsvm_ssh_port = 22\n",
        "dsvm_username = '<<username>>'\n",
        "dsvm_password = '<<password>>'\n",
        "\n",
-        "if compute_name in ws.compute_targets():\n",
+        "if compute_name in ws.compute_targets:\n",
        "    print('Using existing compute.')\n",
-        "    dsvm_compute = ws.compute_targets()[compute_name]\n",
+        "    dsvm_compute = ws.compute_targets[compute_name]\n",
        "else:\n",
-        "    RemoteCompute.attach(workspace=ws, name=compute_name, address=dsvm_ip_addr, username=dsvm_username, password=dsvm_password, ssh_port=dsvm_ssh_port)\n",
+        "    attach_config = RemoteCompute.attach_configuration(address=dsvm_ip_addr, username=dsvm_username, password=dsvm_password, ssh_port=dsvm_ssh_port)\n",
        "    ComputeTarget.attach(workspace=ws, name=compute_name, attach_configuration=attach_config)\n",
        "\n",
-        "    while ws.compute_targets()[compute_name].provisioning_state == 'Creating':\n",
+        "    while ws.compute_targets[compute_name].provisioning_state == 'Creating':\n",
        "        time.sleep(1)\n",
        "\n",
-        "    dsvm_compute = ws.compute_targets()[compute_name]\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()"
+        "        dsvm_compute.detach()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.runconfig import RunConfiguration\n",
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "\n",
        "# create a new RunConfig object\n",
        "conda_run_config = RunConfiguration(framework=\"python\")\n",
        "\n",
        "# Set compute target to the Linux DSVM\n",
        "conda_run_config.target = dsvm_compute\n",
        "\n",
        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
        "conda_run_config.environment.python.conda_dependencies = cd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Create Get Data File\n",
+        "## Data\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)."
      ]
@@ -187,21 +215,23 @@
      "source": [
        "%%writefile $project_folder/get_data.py\n",
        "\n",
-        "import pandas as pd\n",
+        "import numpy as np\n",
-        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.datasets import fetch_20newsgroups\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "def get_data():\n",
-        "    # Load Burning Man 2016 data.\n",
+        "    remove = ('headers', 'footers', 'quotes')\n",
-        "    df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
+        "    categories = [\n",
-        "                     delimiter=\"\\t\", quotechar='\"')\n",
+        "        'alt.atheism',\n",
-        "    # Get integer labels.\n",
+        "        'talk.religion.misc',\n",
-        "    le = LabelEncoder()\n",
+        "        'comp.graphics',\n",
-        "    le.fit(df[\"Label\"].values)\n",
+        "        'sci.space',\n",
-        "    y = le.transform(df[\"Label\"].values)\n",
+        "    ]\n",
-        "    X = df.drop([\"Label\"], axis=1)\n",
+        "    data_train = fetch_20newsgroups(subset = 'train', categories = categories,\n",
        "                                    shuffle = True, random_state = 42,\n",
        "                                    remove = remove)\n",
        "    \n",
-        "    X_train, _, y_train, _ = train_test_split(X, y, test_size = 0.1, random_state = 42)\n",
+        "    X_train = np.array(data_train.data).reshape((len(data_train.data),1))\n",
        "    y_train = np.array(data_train.target)\n",
        "    \n",
        "    return { \"X\" : X_train, \"y\" : y_train }"
      ]
@@ -210,31 +240,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### View data\n",
+        "## Train\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",
@@ -242,12 +248,13 @@
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
-        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
+        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
-        "|**max_time_sec**|Time limit in seconds for each iteration.|\n",
+        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
        "|**n_cross_validations**|Number of cross validation splits.|\n",
-        "|**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",
+        "|**max_concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of cores on the DSVM.|\n",
        "|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.|\n",
        "|**enable_cache**|Setting this to *True* enables preprocess done once and reuse the same preprocessed data for all the iterations. Default value is True.\n",
        "|**max_cores_per_iteration**|Indicates how many cores on the compute target would be used to train a single pipeline.<br>Default is *1*; you can set it to *-1* to use all cores.|"
      ]
    },
@@ -258,8 +265,8 @@
      "outputs": [],
      "source": [
        "automl_settings = {\n",
-        "    \"max_time_sec\": 3600,\n",
+        "    \"iteration_timeout_minutes\": 60,\n",
-        "    \"iterations\": 10,\n",
+        "    \"iterations\": 4,\n",
        "    \"n_cross_validations\": 5,\n",
        "    \"primary_metric\": 'AUC_weighted',\n",
        "    \"preprocess\": True,\n",
@@ -268,7 +275,7 @@
        "\n",
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             path = project_folder,\n",
-        "                             compute_target = dsvm_compute,\n",
+        "                             run_configuration=conda_run_config,\n",
        "                             data_script = project_folder + \"/get_data.py\",\n",
        "                             **automl_settings\n",
        "                            )\n"
@@ -278,8 +285,6 @@
      "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."
      ]
    },
@@ -292,11 +297,20 @@
        "remote_run = experiment.submit(automl_config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "remote_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
+        "## Results\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",
@@ -312,10 +326,37 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "from azureml.train.widgets import RunDetails\n",
+        "from azureml.widgets import RunDetails\n",
        "RunDetails(remote_run).show() "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Wait until the run finishes.\n",
        "remote_run.wait_for_completion(show_output = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Pre-process cache cleanup\n",
        "The preprocess data gets cache at user default file store. When the run is completed the cache can be cleaned by running below cell"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "remote_run.clean_preprocessor_cache()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -346,7 +387,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Cancelling Runs\n",
+        "### Cancelling Runs\n",
        "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
      ]
    },
@@ -357,7 +398,7 @@
      "outputs": [],
      "source": [
        "# Cancel the ongoing experiment and stop scheduling new iterations.\n",
-        "remote_run.cancel()\n",
+        "# remote_run.cancel()\n",
        "\n",
        "# Cancel iteration 1 and move onto iteration 2.\n",
        "# remote_run.cancel_iteration(1)"
@@ -422,7 +463,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
+        "## Test"
      ]
    },
    {
@@ -431,33 +472,33 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "import sklearn\n",
+        "# Load test data.\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from pandas_ml import ConfusionMatrix\n",
        "from sklearn.datasets import fetch_20newsgroups\n",
        "\n",
-        "df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
+        "remove = ('headers', 'footers', 'quotes')\n",
-        "                     delimiter=\"\\t\", quotechar='\"')\n",
+        "categories = [\n",
        "        'alt.atheism',\n",
        "        'talk.religion.misc',\n",
        "        'comp.graphics',\n",
        "        'sci.space',\n",
        "    ]\n",
        "\n",
-        "# get integer labels\n",
+        "data_test = fetch_20newsgroups(subset = 'test', categories = categories,\n",
-        "le = LabelEncoder()\n",
+        "                               shuffle = True, random_state = 42,\n",
-        "le.fit(df[\"Label\"].values)\n",
+        "                               remove = remove)\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",
+        "X_test = np.array(data_test.data).reshape((len(data_test.data),1))\n",
        "y_test = data_test.target\n",
        "\n",
        "# Test our best pipeline.\n",
        "\n",
-        "ypred = fitted_model.predict(X_test.values)\n",
+        "y_pred = fitted_model.predict(X_test)\n",
-        "\n",
+        "y_pred_strings = [data_test.target_names[i] for i in y_pred]\n",
-        "\n",
+        "y_test_strings = [data_test.target_names[i] for i in y_test]\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",
        "cm = ConfusionMatrix(y_test_strings, y_pred_strings)\n",
        "print(cm)\n",
        "\n",
        "cm.plot()"
      ]
    }
--- a/how-to-use-azureml/automated-machine-learning/remote-batchai/auto-ml-remote-batchai.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/remote-batchai/auto-ml-remote-batchai.ipynb
@@ -13,17 +13,32 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "# AutoML 03: Remote Execution using Batch AI\n",
+        "# Automated Machine Learning\n",
        "_**Remote Execution using AmlCompute**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Data](#Data)\n",
        "1. [Train](#Train)\n",
        "1. [Results](#Results)\n",
        "1. [Test](#Test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
        "\n",
-        "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you would see\n",
        "1. Create an `Experiment` in an existing `Workspace`.\n",
-        "2. Attach an existing Batch AI compute to a workspace.\n",
+        "2. Create or Attach existing AmlCompute to a workspace.\n",
        "3. Configure AutoML using `AutoMLConfig`.\n",
-        "4. Train the model using Batch AI.\n",
+        "4. Train the model using AmlCompute\n",
        "5. Explore the results.\n",
        "6. Test the best fitted model.\n",
        "\n",
@@ -32,14 +47,14 @@
        "- **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"
+        "- Specifying AutoML settings as `**kwargs`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Create an Experiment\n",
+        "## Setup\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."
      ]
@@ -52,10 +67,8 @@
      "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",
@@ -63,8 +76,7 @@
        "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 import AutoMLConfig"
        "from azureml.train.automl.run import AutoMLRun"
      ]
    },
    {
@@ -76,8 +88,8 @@
        "ws = Workspace.from_config()\n",
        "\n",
        "# Choose a name for the run history container in the workspace.\n",
-        "experiment_name = 'automl-remote-batchai'\n",
+        "experiment_name = 'automl-remote-amlcompute'\n",
-        "project_folder = './sample_projects/automl-remote-batchai'\n",
+        "project_folder = './sample_projects/automl-remote-amlcompute'\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
@@ -90,15 +102,14 @@
        "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"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Diagnostics\n",
        "\n",
        "Opt-in diagnostics for better experience, quality, and security of future releases."
      ]
    },
@@ -116,12 +127,12 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Create Batch AI Cluster\n",
+        "### Create or Attach existing AmlCompute\n",
-        "The cluster is created as Machine Learning Compute and will appear under your workspace.\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
        "\n",
-        "**Note:** The creation of the Batch AI cluster can take over 10 minutes, please be patient.\n",
+        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\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."
+        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) 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."
      ]
    },
    {
@@ -130,45 +141,62 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "from azureml.core.compute import BatchAiCompute\n",
+        "from azureml.core.compute import AmlCompute\n",
        "from azureml.core.compute import ComputeTarget\n",
        "\n",
        "# Choose a name for your cluster.\n",
-        "batchai_cluster_name = \"mybatchai\"\n",
+        "amlcompute_cluster_name = \"automlcl\"\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",
+        "cts = ws.compute_targets\n",
-        "    print(ct.name, ct.type)\n",
+        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\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",
+        "    compute_target = cts[amlcompute_cluster_name]\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",
+        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                autoscale_enabled = True,\n",
+        "                                                                max_nodes = 6)\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",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_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."
+        "     # For a more detailed view of current AmlCompute status, use get_status()."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.runconfig import RunConfiguration\n",
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "\n",
        "# create a new RunConfig object\n",
        "conda_run_config = RunConfiguration(framework=\"python\")\n",
        "\n",
        "# Set compute target to AmlCompute\n",
        "conda_run_config.target = compute_target\n",
        "conda_run_config.environment.docker.enabled = True\n",
        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
        "\n",
        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
        "conda_run_config.environment.python.conda_dependencies = cd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Create Get Data File\n",
+        "## Data\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."
      ]
@@ -208,19 +236,19 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
+        "## Train\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",
+        "**Note:** When using AmlCompute, 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",
+        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
-        "|**max_time_sec**|Time limit in seconds for each iteration.|\n",
+        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
        "|**n_cross_validations**|Number of cross validation splits.|\n",
-        "|**concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of cores on the DSVM.|"
+        "|**max_concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of cores on the DSVM.|"
      ]
    },
    {
@@ -230,19 +258,19 @@
      "outputs": [],
      "source": [
        "automl_settings = {\n",
-        "    \"max_time_sec\": 120,\n",
+        "    \"iteration_timeout_minutes\": 2,\n",
        "    \"iterations\": 20,\n",
        "    \"n_cross_validations\": 5,\n",
        "    \"primary_metric\": 'AUC_weighted',\n",
        "    \"preprocess\": False,\n",
-        "    \"concurrent_iterations\": 5,\n",
+        "    \"max_concurrent_iterations\": 5,\n",
        "    \"verbosity\": logging.INFO\n",
        "}\n",
        "\n",
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             debug_log = 'automl_errors.log',\n",
        "                             path = project_folder,\n",
-        "                             compute_target = compute_target,\n",
+        "                             run_configuration=conda_run_config,\n",
        "                             data_script = project_folder + \"/get_data.py\",\n",
        "                             **automl_settings\n",
        "                            )\n"
@@ -252,8 +280,6 @@
      "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."
      ]
@@ -267,11 +293,20 @@
        "remote_run = experiment.submit(automl_config, show_output = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "remote_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Explore the Results\n",
+        "## 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."
@@ -312,7 +347,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "from azureml.train.widgets import RunDetails\n",
+        "from azureml.widgets import RunDetails\n",
        "RunDetails(remote_run).show() "
      ]
    },
@@ -356,7 +391,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Cancelling Runs\n",
+        "### Cancelling Runs\n",
        "\n",
        "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
      ]
@@ -438,7 +473,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n",
+        "## Test\n",
        "\n",
        "#### Load Test Data"
      ]
--- a/how-to-use-azureml/automated-machine-learning/remote-execution-with-datastore/auto-ml-remote-execution-with-datastore.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/remote-execution-with-datastore/auto-ml-remote-execution-with-datastore.ipynb
@@ -13,26 +13,40 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "# AutoML 08: Remote Execution with DataStore\n",
+        "# Automated Machine Learning\n",
        "_**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",
+        "## Contents\n",
-        "1. DataStore secures the access details.\n",
+        "1. [Introduction](#Introduction)\n",
-        "2. DataStore supports read, write to blob and file store\n",
+        "1. [Setup](#Setup)\n",
-        "3. AutoML natively supports copying data from DataStore to DSVM\n",
+        "1. [Data](#Data)\n",
-        "\n",
+        "1. [Train](#Train)\n",
-        "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+        "1. [Results](#Results)\n",
-        "\n",
+        "1. [Test](#Test)"
        "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",
+        "## Introduction\n",
        "This sample accesses a data file on a remote DSVM through DataStore. Advantages of using data store are:\n",
        "1. DataStore secures the access details.\n",
        "2. DataStore supports read, write to blob and file store\n",
        "3. AutoML natively supports copying data from DataStore to DSVM\n",
        "\n",
        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you would see\n",
        "1. Storing data in DataStore.\n",
        "2. get_data returning data from DataStore."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\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."
      ]
@@ -45,19 +59,16 @@
      "source": [
        "import logging\n",
        "import os\n",
-        "import random\n",
+        "import time\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "from matplotlib.pyplot import imshow\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import datasets\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.compute import DsvmCompute\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl import AutoMLConfig\n",
+        "from azureml.train.automl import AutoMLConfig"
        "from azureml.train.automl.run import AutoMLRun"
      ]
    },
    {
@@ -71,7 +82,7 @@
        "# 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",
+        "project_folder = './sample_projects/automl-remote-datastore-file'\n",
        "\n",
        "experiment=Experiment(ws, experiment_name)\n",
        "\n",
@@ -84,15 +95,14 @@
        "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"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Diagnostics\n",
        "\n",
        "Opt-in diagnostics for better experience, quality, and security of future releases"
      ]
    },
@@ -110,7 +120,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Create a Remote Linux DSVM\n",
+        "### 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."
@@ -122,25 +132,29 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "from azureml.core.compute import DsvmCompute\n",
+        "compute_target_name = 'mydsvmc'\n",
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "compute_target_name = 'mydsvm'\n",
        "\n",
        "try:\n",
        "    while ws.compute_targets[compute_target_name].provisioning_state == 'Creating':\n",
        "        time.sleep(1)\n",
        "        \n",
        "    dsvm_compute = DsvmCompute(workspace=ws, name=compute_target_name)\n",
        "    print('found existing:', dsvm_compute.name)\n",
-        "except ComputeTargetException:\n",
+        "except:\n",
        "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size=\"Standard_D2_v2\")\n",
        "    dsvm_compute = DsvmCompute.create(ws, name=compute_target_name, provisioning_configuration=dsvm_config)\n",
-        "    dsvm_compute.wait_for_completion(show_output=True)"
+        "    dsvm_compute.wait_for_completion(show_output=True)\n",
        "    print(\"Waiting one minute for ssh to be accessible\")\n",
        "    time.sleep(60) # Wait for ssh to be accessible"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Copy data file to local\n",
+        "## Data\n",
        "\n",
        "### Copy data file to local\n",
        "\n",
        "Download the data file.\n"
      ]
@@ -151,7 +165,8 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "mkdir data"
+        "if not os.path.isdir('data'):\n",
        "    os.mkdir('data')    "
      ]
    },
    {
@@ -160,16 +175,29 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
+        "from sklearn.datasets import fetch_20newsgroups\n",
-        "                     delimiter=\"\\t\", quotechar='\"')\n",
+        "import csv\n",
-        "df.to_csv(\"data/data.tsv\", sep=\"\\t\", quotechar='\"', index=False)"
+        "\n",
        "remove = ('headers', 'footers', 'quotes')\n",
        "categories = [\n",
        "        'alt.atheism',\n",
        "        'talk.religion.misc',\n",
        "        'comp.graphics',\n",
        "        'sci.space',\n",
        "    ]\n",
        "data_train = fetch_20newsgroups(subset = 'train', categories = categories,\n",
        "                                    shuffle = True, random_state = 42,\n",
        "                                    remove = remove)\n",
        "    \n",
        "pd.DataFrame(data_train.data).to_csv(\"data/X_train.tsv\", index=False, header=False, quoting=csv.QUOTE_ALL, sep=\"\\t\")\n",
        "pd.DataFrame(data_train.target).to_csv(\"data/y_train.tsv\", index=False, header=False, sep=\"\\t\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Upload data to the cloud"
+        "### Upload data to the cloud"
      ]
    },
    {
@@ -187,7 +215,6 @@
      "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)"
@@ -207,9 +234,10 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Configure & Run\n",
+        "### Configure & Run\n",
        "\n",
-        "First let's create a DataReferenceConfigruation object to inform the system what data folder to download to the copmute target."
+        "First let's create a DataReferenceConfigruation object to inform the system what data folder to download to the compute target.\n",
        "The path_on_compute should be an absolute path to ensure that the data files are downloaded only once.   The get_data method should use this same path to access the data files."
      ]
    },
    {
@@ -221,8 +249,9 @@
        "from azureml.core.runconfig import DataReferenceConfiguration\n",
        "dr = DataReferenceConfiguration(datastore_name=ds.name, \n",
        "                   path_on_datastore='data', \n",
        "                   path_on_compute='/tmp/azureml_runs',\n",
        "                   mode='download', # download files from datastore to compute target\n",
-        "                   overwrite=True)"
+        "                   overwrite=False)"
      ]
    },
    {
@@ -232,24 +261,30 @@
      "outputs": [],
      "source": [
        "from azureml.core.runconfig import RunConfiguration\n",
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "\n",
        "# create a new RunConfig object\n",
        "conda_run_config = RunConfiguration(framework=\"python\")\n",
        "\n",
        "# Set compute target to the Linux DSVM\n",
-        "conda_run_config.target = dsvm_compute.name\n",
+        "conda_run_config.target = dsvm_compute\n",
        "# set the data reference of the run coonfiguration\n",
-        "conda_run_config.data_references = {ds.name: dr}"
+        "conda_run_config.data_references = {ds.name: dr}\n",
        "\n",
        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
        "conda_run_config.environment.python.conda_dependencies = cd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Create Get Data File\n",
+        "### 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)."
+        "The *get_data()* function returns a [dictionary](README.md#getdata).\n",
        "\n",
        "The read_csv uses the path_on_compute value specified in the DataReferenceConfiguration call plus the path_on_datastore folder and then the actual file name."
      ]
    },
    {
@@ -271,32 +306,19 @@
        "%%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",
+        "    X_train = pd.read_csv(\"/tmp/azureml_runs/data/X_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
-        "    df = pd.read_csv(join(dirname(os.path.realpath(__file__)),\n",
+        "    y_train = pd.read_csv(\"/tmp/azureml_runs/data/y_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\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",
+        "    return { \"X\" : X_train.values, \"y\" : y_train[0].values }"
        "\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",
+        "## Train\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",
@@ -304,12 +326,13 @@
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
-        "|**primary_metric**|This is the metric that you want to optimize.<br> Classification supports the following primary metrics <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
+        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
-        "|**max_time_sec**|Time limit in seconds for each iteration|\n",
+        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration|\n",
        "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data|\n",
        "|**n_cross_validations**|Number of cross validation splits|\n",
-        "|**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",
+        "|**max_concurrent_iterations**|Max number of iterations that would be executed in parallel.  This should be less than the number of cores on the DSVM\n",
        "|**preprocess**| *True/False* <br>Setting this to *True* enables Auto ML to perform preprocessing <br>on the input to handle *missing data*, and perform some common *feature extraction*|\n",
        "|**enable_cache**|Setting this to *True* enables preprocess done once and reuse the same preprocessed data for all the iterations. Default value is True.|\n",
        "|**max_cores_per_iteration**| Indicates how many cores on the compute target would be used to train a single pipeline.<br> Default is *1*, you can set it to *-1* to use all cores|"
      ]
    },
@@ -320,12 +343,12 @@
      "outputs": [],
      "source": [
        "automl_settings = {\n",
-        "    \"max_time_sec\": 3600,\n",
+        "    \"iteration_timeout_minutes\": 60,\n",
-        "    \"iterations\": 10,\n",
+        "    \"iterations\": 4,\n",
        "    \"n_cross_validations\": 5,\n",
        "    \"primary_metric\": 'AUC_weighted',\n",
        "    \"preprocess\": True,\n",
-        "    \"max_cores_per_iteration\": 2,\n",
+        "    \"max_cores_per_iteration\": 1,\n",
        "    \"verbosity\": logging.INFO\n",
        "}\n",
        "automl_config = AutoMLConfig(task = 'classification',\n",
@@ -342,8 +365,6 @@
      "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."
      ]
    },
@@ -356,11 +377,20 @@
        "remote_run = experiment.submit(automl_config, show_output=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "remote_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
+        "## Results\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",
@@ -376,10 +406,20 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "from azureml.train.widgets import RunDetails\n",
+        "from azureml.widgets import RunDetails\n",
        "RunDetails(remote_run).show() "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Wait until the run finishes.\n",
        "remote_run.wait_for_completion(show_output = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -410,7 +450,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Canceling Runs\n",
+        "### Canceling Runs\n",
        "You can cancel ongoing remote runs using the *cancel()* and *cancel_iteration()* functions"
      ]
    },
@@ -421,12 +461,29 @@
      "outputs": [],
      "source": [
        "# Cancel the ongoing experiment and stop scheduling new iterations\n",
-        "remote_run.cancel()\n",
+        "# remote_run.cancel()\n",
        "\n",
        "# Cancel iteration 1 and move onto iteration 2\n",
        "# remote_run.cancel_iteration(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Pre-process cache cleanup\n",
        "The preprocess data gets cache at user default file store. When the run is completed the cache can be cleaned by running below cell"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "remote_run.clean_preprocessor_cache()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -483,7 +540,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Testing the Best Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
+        "## Test\n"
      ]
    },
    {
@@ -492,31 +549,24 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "import sklearn\n",
+        "# Load test data.\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",
+        "data_test = fetch_20newsgroups(subset = 'test', categories = categories,\n",
-        "                     delimiter=\"\\t\", quotechar='\"')\n",
+        "                               shuffle = True, random_state = 42,\n",
        "                               remove = remove)\n",
        "\n",
-        "# get integer labels\n",
+        "X_test = np.array(data_test.data).reshape((len(data_test.data),1))\n",
-        "le = LabelEncoder()\n",
+        "y_test = data_test.target\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",
+        "# Test our best pipeline.\n",
        "\n",
-        "ypred = fitted_model.predict(X_test.values)\n",
+        "y_pred = fitted_model.predict(X_test)\n",
-        "\n",
+        "y_pred_strings = [data_test.target_names[i] for i in y_pred]\n",
-        "ypred_strings = le.inverse_transform(ypred)\n",
+        "y_test_strings = [data_test.target_names[i] for i in y_test]\n",
        "ytest_strings = le.inverse_transform(y_test)\n",
        "\n",
        "cm = ConfusionMatrix(ytest_strings, ypred_strings)\n",
        "\n",
        "cm = ConfusionMatrix(y_test_strings, y_pred_strings)\n",
        "print(cm)\n",
        "\n",
        "cm.plot()"
      ]
    }
--- a/how-to-use-azureml/automated-machine-learning/remote-execution/auto-ml-remote-execution.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/remote-execution/auto-ml-remote-execution.ipynb
@@ -13,11 +13,26 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "# AutoML 03: Remote Execution using DSVM (Ubuntu)\n",
+        "# Automated Machine Learning\n",
        "_**Remote Execution using DSVM (Ubuntu)**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Data](#Data)\n",
        "1. [Train](#Train)\n",
        "1. [Results](#Results)\n",
        "1. [Test](#Test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
        "\n",
-        "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you wiil learn how to:\n",
        "1. Create an `Experiment` in an existing `Workspace`.\n",
@@ -32,14 +47,14 @@
        "- **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"
+        "- Specifying AutoML settings as `**kwargs`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Create an Experiment\n",
+        "## Setup\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."
      ]
@@ -52,10 +67,9 @@
      "source": [
        "import logging\n",
        "import os\n",
-        "import random\n",
+        "import time\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "from matplotlib.pyplot import imshow\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import datasets\n",
@@ -63,8 +77,7 @@
        "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 import AutoMLConfig"
        "from azureml.train.automl.run import AutoMLRun"
      ]
    },
    {
@@ -76,8 +89,8 @@
        "ws = Workspace.from_config()\n",
        "\n",
        "# Choose a name for the run history container in the workspace.\n",
-        "experiment_name = 'automl-remote-dsvm4'\n",
+        "experiment_name = 'automl-remote-dsvm'\n",
-        "project_folder = './sample_projects/automl-remote-dsvm4'\n",
+        "project_folder = './sample_projects/automl-remote-dsvm'\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
@@ -90,15 +103,14 @@
        "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"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Diagnostics\n",
        "\n",
        "Opt-in diagnostics for better experience, quality, and security of future releases."
      ]
    },
@@ -116,7 +128,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Create a Remote Linux DSVM\n",
+        "### 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"
      ]
    },
@@ -128,22 +140,43 @@
      "source": [
        "from azureml.core.compute import DsvmCompute\n",
        "\n",
-        "dsvm_name = 'mydsvm'\n",
+        "dsvm_name = 'mydsvma'\n",
        "try:\n",
        "    dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
        "    print('Found an existing DSVM.')\n",
        "except:\n",
        "    print('Creating a new DSVM.')\n",
-        "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
+        "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2s_v3\")\n",
        "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
-        "    dsvm_compute.wait_for_completion(show_output = True)"
+        "    dsvm_compute.wait_for_completion(show_output = True)\n",
        "    print(\"Waiting one minute for ssh to be accessible\")\n",
        "    time.sleep(90) # Wait for ssh to be accessible"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.runconfig import RunConfiguration\n",
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "\n",
        "# create a new RunConfig object\n",
        "conda_run_config = RunConfiguration(framework=\"python\")\n",
        "\n",
        "# Set compute target to the Linux DSVM\n",
        "conda_run_config.target = dsvm_compute\n",
        "\n",
        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
        "conda_run_config.environment.python.conda_dependencies = cd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Create Get Data File\n",
+        "## Data\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."
      ]
@@ -183,7 +216,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Configure AutoML <a class=\"anchor\" id=\"Instantiate-AutoML-Remote-DSVM\"></a>\n",
+        "## Train\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",
@@ -191,11 +224,11 @@
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
-        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
+        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
-        "|**max_time_sec**|Time limit in seconds for each iteration.|\n",
+        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
        "|**n_cross_validations**|Number of cross validation splits.|\n",
-        "|**concurrent_iterations**|Maximum number of iterations to execute in parallel. This should be less than the number of cores on the DSVM.|"
+        "|**max_concurrent_iterations**|Maximum number of iterations to execute in parallel. This should be less than the number of cores on the DSVM.|"
      ]
    },
    {
@@ -205,19 +238,19 @@
      "outputs": [],
      "source": [
        "automl_settings = {\n",
-        "    \"max_time_sec\": 600,\n",
+        "    \"iteration_timeout_minutes\": 10,\n",
        "    \"iterations\": 20,\n",
        "    \"n_cross_validations\": 5,\n",
        "    \"primary_metric\": 'AUC_weighted',\n",
        "    \"preprocess\": False,\n",
-        "    \"concurrent_iterations\": 2,\n",
+        "    \"max_concurrent_iterations\": 2,\n",
        "    \"verbosity\": logging.INFO\n",
        "}\n",
        "\n",
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             debug_log = 'automl_errors.log',\n",
        "                             path = project_folder, \n",
-        "                             compute_target = dsvm_compute,\n",
+        "                             run_configuration=conda_run_config,\n",
        "                             data_script = project_folder + \"/get_data.py\",\n",
        "                             **automl_settings\n",
        "                            )\n"
@@ -234,8 +267,6 @@
      "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."
@@ -250,11 +281,20 @@
        "remote_run = experiment.submit(automl_config, show_output = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "remote_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Explore the Results\n",
+        "## 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."
@@ -286,7 +326,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "from azureml.train.widgets import RunDetails\n",
+        "from azureml.widgets import RunDetails\n",
        "RunDetails(remote_run).show() "
      ]
    },
@@ -330,7 +370,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Cancelling Runs\n",
+        "### Cancelling Runs\n",
        "\n",
        "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
      ]
@@ -412,7 +452,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Test the Best Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n",
+        "## Test\n",
        "\n",
        "#### Load Test Data"
      ]
--- a/how-to-use-azureml/automated-machine-learning/sample-weight/auto-ml-sample-weight.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/sample-weight/auto-ml-sample-weight.ipynb
@@ -13,20 +13,33 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "# AutoML 11: Sample Weight\n",
+        "# Automated Machine Learning\n",
        "_**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",
+        "## Contents\n",
-        "\n",
+        "1. [Introduction](#Introduction)\n",
-        "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+        "1. [Setup](#Setup)\n",
-        "\n",
+        "1. [Train](#Train)\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"
+        "1. [Test](#Test)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Create an Experiment\n",
+        "## Introduction\n",
        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use sample weight with AutoML. Sample weight is used where some sample values are more important than others.\n",
        "\n",
        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you will learn how to configure AutoML to use `sample_weight` and you will see the difference sample weight makes to the test results."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\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."
      ]
@@ -38,11 +51,8 @@
      "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",
@@ -50,8 +60,7 @@
        "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 import AutoMLConfig"
        "from azureml.train.automl.run import AutoMLRun"
      ]
    },
    {
@@ -80,15 +89,14 @@
        "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"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Diagnostics\n",
        "\n",
        "Opt-in diagnostics for better experience, quality, and security of future releases."
      ]
    },
@@ -106,7 +114,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Configure AutoML\n",
+        "## Train\n",
        "\n",
        "Instantiate two `AutoMLConfig` objects. One will be used with `sample_weight` and one without."
      ]
@@ -128,7 +136,7 @@
        "automl_classifier = AutoMLConfig(task = 'classification',\n",
        "                                 debug_log = 'automl_errors.log',\n",
        "                                 primary_metric = 'AUC_weighted',\n",
-        "                                 max_time_sec = 3600,\n",
+        "                                 iteration_timeout_minutes = 60,\n",
        "                                 iterations = 10,\n",
        "                                 n_cross_validations = 2,\n",
        "                                 verbosity = logging.INFO,\n",
@@ -139,7 +147,7 @@
        "automl_sample_weight = AutoMLConfig(task = 'classification',\n",
        "                                    debug_log = 'automl_errors.log',\n",
        "                                    primary_metric = 'AUC_weighted',\n",
-        "                                    max_time_sec = 3600,\n",
+        "                                    iteration_timeout_minutes = 60,\n",
        "                                    iterations = 10,\n",
        "                                    n_cross_validations = 2,\n",
        "                                    verbosity = logging.INFO,\n",
@@ -153,8 +161,6 @@
      "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."
      ]
@@ -176,7 +182,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Test the Best Fitted Model\n",
+        "## Test\n",
        "\n",
        "#### Load Test Data"
      ]
--- a/how-to-use-azureml/automated-machine-learning/sparse-data-train-test-split/auto-ml-sparse-data-train-test-split.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/sparse-data-train-test-split/auto-ml-sparse-data-train-test-split.ipynb
@@ -13,11 +13,25 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "# AutoML 06: Custom CV Splits and Handling Sparse Data\n",
+        "# Automated Machine Learning\n",
        "_**Train Test Split and Handling Sparse Data**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Data](#Data)\n",
        "1. [Train](#Train)\n",
        "1. [Results](#Results)\n",
        "1. [Test](#Test)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "In this example we use the scikit-learn's [20newsgroup](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_20newsgroups.html) to showcase how you can use AutoML for handling sparse data and how to specify custom cross validations splits.\n",
-        "\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "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",
@@ -27,7 +41,7 @@
        "6. Test the best fitted model.\n",
        "\n",
        "In addition this notebook showcases the following features\n",
-        "- **Custom CV** splits \n",
+        "- Explicit train test splits \n",
        "- Handling **sparse data** in the input"
      ]
    },
@@ -35,7 +49,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Create an Experiment\n",
+        "## Setup\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."
      ]
@@ -47,20 +61,13 @@
      "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 import AutoMLConfig"
        "from azureml.train.automl.run import AutoMLRun"
      ]
    },
    {
@@ -87,15 +94,14 @@
        "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"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Diagnostics\n",
        "\n",
        "Opt-in diagnostics for better experience, quality, and security of future releases."
      ]
    },
@@ -113,7 +119,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Creating Sparse Data"
+        "## Data"
      ]
    },
    {
@@ -155,15 +161,15 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Configure AutoML\n",
+        "## Train\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",
+        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
-        "|**max_time_sec**|Time limit in seconds for each iteration.|\n",
+        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
        "|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.<br>**Note:** If input data is sparse, you cannot use *True*.|\n",
        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
@@ -182,7 +188,7 @@
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             debug_log = 'automl_errors.log',\n",
        "                             primary_metric = 'AUC_weighted',\n",
-        "                             max_time_sec = 3600,\n",
+        "                             iteration_timeout_minutes = 60,\n",
        "                             iterations = 5,\n",
        "                             preprocess = False,\n",
        "                             verbosity = logging.INFO,\n",
@@ -197,8 +203,6 @@
      "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."
      ]
@@ -212,11 +216,20 @@
        "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"
+        "## Results"
      ]
    },
    {
@@ -236,7 +249,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "from azureml.train.widgets import RunDetails\n",
+        "from azureml.widgets import RunDetails\n",
        "RunDetails(local_run).show() "
      ]
    },
@@ -324,7 +337,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Testing the Best Fitted Model"
+        "## Test"
      ]
    },
    {
--- a/how-to-use-azureml/azure-databricks/Databricks_AMLSDK_1-4_6.dbc
+++ b/how-to-use-azureml/azure-databricks/Databricks_AMLSDK_1-4_6.dbc
--- a/how-to-use-azureml/azure-databricks/README.md
+++ b/how-to-use-azureml/azure-databricks/README.md
@@ -0,0 +1,26 @@
 Azure Databricks is a managed Spark offering on Azure and customers already use it for advanced analytics. It provides a collaborative Notebook based environment with CPU or GPU based compute cluster. 
 In this section, you will find sample notebooks on how to use Azure Machine Learning SDK with Azure Databricks. You can train a model using Spark MLlib and then deploy the model to ACI/AKS from within Azure Databricks. You can also use Automated ML capability (**public preview**) of Azure ML SDK with Azure Databricks. 
 - Customers who use Azure Databricks for advanced analytics can now use the same cluster to run experiments with or without automated machine learning. 
 - You can keep the data within the same cluster. 
 - You can leverage the local worker nodes with autoscale and auto termination capabilities. 
 - You can use multiple cores of your Azure Databricks cluster to perform simultenous training. 
 - You can further tune the model generated by automated machine learning if you chose to. 
 - Every run (including the best run) is available as a pipeline, which you can tune further if needed. 
 - The model trained using Azure Databricks can be registered in Azure ML SDK workspace and then deployed to Azure managed compute (ACI or AKS) using the Azure Machine learning SDK.
 Please follow our [Azure doc](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#azure-databricks) to install the sdk in your Azure Databricks cluster before trying any of the sample notebooks.
 **Single file** - 
 The following archive contains all the sample notebooks. You can the run notebooks after importing [DBC](Databricks_AMLSDK_1-4_6.dbc) in your Databricks workspace instead of downloading individually.
 Notebooks 1-4 have to be run sequentially & are related to Income prediction experiment based on this [dataset](https://archive.ics.uci.edu/ml/datasets/adult) and demonstrate how to data prep, train and operationalize a Spark ML model with Azure ML Python SDK from within Azure Databricks. 
 Notebook 6 is an Automated ML sample notebook for Classification.
 Learn more about [how to use Azure Databricks as a development environment](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment#azure-databricks) for Azure Machine Learning service.
 For more on SDK concepts, please refer to [notebooks](https://github.com/Azure/MachineLearningNotebooks).
 **Please let us know your feedback.**
--- a/how-to-use-azureml/azure-databricks/amlsdk/build-model-run-history-03.ipynb
+++ b/how-to-use-azureml/azure-databricks/amlsdk/build-model-run-history-03.ipynb
@@ -0,0 +1,380 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
        "\n",
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![04ACI](files/tables/image2.JPG)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#Model Building"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "import pprint\n",
        "import numpy as np\n",
        "\n",
        "from pyspark.ml import Pipeline, PipelineModel\n",
        "from pyspark.ml.feature import OneHotEncoder, StringIndexer, VectorAssembler\n",
        "from pyspark.ml.classification import LogisticRegression\n",
        "from pyspark.ml.evaluation import BinaryClassificationEvaluator\n",
        "from pyspark.ml.tuning import CrossValidator, ParamGridBuilder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import azureml.core\n",
        "\n",
        "# Check core SDK version number\n",
        "print(\"SDK version:\", azureml.core.VERSION)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# import the Workspace class and check the azureml SDK version\n",
        "from azureml.core import Workspace\n",
        "\n",
        "ws = Workspace.from_config(auth = auth)\n",
        "print('Workspace name: ' + ws.name, \n",
        "      'Azure region: ' + ws.location, \n",
        "      'Subscription id: ' + ws.subscription_id, \n",
        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# import the Workspace class and check the azureml SDK version\n",
        "from azureml.core import Workspace\n",
        "\n",
        "ws = Workspace.from_config()\n",
        "print('Workspace name: ' + ws.name, \n",
        "      'Azure region: ' + ws.location, \n",
        "      'Subscription id: ' + ws.subscription_id, \n",
        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#get the train and test datasets\n",
        "train_data_path = \"AdultCensusIncomeTrain\"\n",
        "test_data_path = \"AdultCensusIncomeTest\"\n",
        "\n",
        "train = spark.read.parquet(train_data_path)\n",
        "test = spark.read.parquet(test_data_path)\n",
        "\n",
        "print(\"train: ({}, {})\".format(train.count(), len(train.columns)))\n",
        "print(\"test: ({}, {})\".format(test.count(), len(test.columns)))\n",
        "\n",
        "train.printSchema()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#Define Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "label = \"income\"\n",
        "dtypes = dict(train.dtypes)\n",
        "dtypes.pop(label)\n",
        "\n",
        "si_xvars = []\n",
        "ohe_xvars = []\n",
        "featureCols = []\n",
        "for idx,key in enumerate(dtypes):\n",
        "    if dtypes[key] == \"string\":\n",
        "        featureCol = \"-\".join([key, \"encoded\"])\n",
        "        featureCols.append(featureCol)\n",
        "        \n",
        "        tmpCol = \"-\".join([key, \"tmp\"])\n",
        "        # string-index and one-hot encode the string column\n",
        "        #https://spark.apache.org/docs/2.3.0/api/java/org/apache/spark/ml/feature/StringIndexer.html\n",
        "        #handleInvalid: Param for how to handle invalid data (unseen labels or NULL values). \n",
        "        #Options are 'skip' (filter out rows with invalid data), 'error' (throw an error), \n",
        "        #or 'keep' (put invalid data in a special additional bucket, at index numLabels). Default: \"error\"\n",
        "        si_xvars.append(StringIndexer(inputCol=key, outputCol=tmpCol, handleInvalid=\"skip\"))\n",
        "        ohe_xvars.append(OneHotEncoder(inputCol=tmpCol, outputCol=featureCol))\n",
        "    else:\n",
        "        featureCols.append(key)\n",
        "\n",
        "# string-index the label column into a column named \"label\"\n",
        "si_label = StringIndexer(inputCol=label, outputCol='label')\n",
        "\n",
        "# assemble the encoded feature columns in to a column named \"features\"\n",
        "assembler = VectorAssembler(inputCols=featureCols, outputCol=\"features\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.run import Run\n",
        "from azureml.core.experiment import Experiment\n",
        "import numpy as np\n",
        "import os\n",
        "import shutil\n",
        "\n",
        "model_name = \"AdultCensus_runHistory.mml\"\n",
        "model_dbfs = os.path.join(\"/dbfs\", model_name)\n",
        "run_history_name = 'spark-ml-notebook'\n",
        "\n",
        "# start a training run by defining an experiment\n",
        "myexperiment = Experiment(ws, \"Ignite_AI_Talk\")\n",
        "root_run = myexperiment.start_logging()\n",
        "\n",
        "# Regularization Rates - \n",
        "regs = [0.0001, 0.001, 0.01, 0.1]\n",
        " \n",
        "# try a bunch of regularization rate in a Logistic Regression model\n",
        "for reg in regs:\n",
        "    print(\"Regularization rate: {}\".format(reg))\n",
        "    # create a bunch of child runs\n",
        "    with root_run.child_run(\"reg-\" + str(reg)) as run:\n",
        "        # create a new Logistic Regression model.\n",
        "        lr = LogisticRegression(regParam=reg)\n",
        "        \n",
        "        # put together the pipeline\n",
        "        pipe = Pipeline(stages=[*si_xvars, *ohe_xvars, si_label, assembler, lr])\n",
        "\n",
        "        # train the model\n",
        "        model_p = pipe.fit(train)\n",
        "        \n",
        "        # make prediction\n",
        "        pred = model_p.transform(test)\n",
        "        \n",
        "        # evaluate. note only 2 metrics are supported out of the box by Spark ML.\n",
        "        bce = BinaryClassificationEvaluator(rawPredictionCol='rawPrediction')\n",
        "        au_roc = bce.setMetricName('areaUnderROC').evaluate(pred)\n",
        "        au_prc = bce.setMetricName('areaUnderPR').evaluate(pred)\n",
        "\n",
        "        print(\"Area under ROC: {}\".format(au_roc))\n",
        "        print(\"Area Under PR: {}\".format(au_prc))\n",
        "      \n",
        "        # log reg, au_roc, au_prc and feature names in run history\n",
        "        run.log(\"reg\", reg)\n",
        "        run.log(\"au_roc\", au_roc)\n",
        "        run.log(\"au_prc\", au_prc)\n",
        "        run.log_list(\"columns\", train.columns)\n",
        "\n",
        "        # save model\n",
        "        model_p.write().overwrite().save(model_name)\n",
        "        \n",
        "        # upload the serialized model into run history record\n",
        "        mdl, ext = model_name.split(\".\")\n",
        "        model_zip = mdl + \".zip\"\n",
        "        shutil.make_archive(mdl, 'zip', model_dbfs)\n",
        "        run.upload_file(\"outputs/\" + model_name, model_zip)        \n",
        "        #run.upload_file(\"outputs/\" + model_name, path_or_stream = model_dbfs) #cannot deal with folders\n",
        "\n",
        "        # now delete the serialized model from local folder since it is already uploaded to run history \n",
        "        shutil.rmtree(model_dbfs)\n",
        "        os.remove(model_zip)\n",
        "        \n",
        "# Declare run completed\n",
        "root_run.complete()\n",
        "root_run_id = root_run.id\n",
        "print (\"run id:\", root_run.id)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "metrics = root_run.get_metrics(recursive=True)\n",
        "best_run_id = max(metrics, key = lambda k: metrics[k]['au_roc'])\n",
        "print(best_run_id, metrics[best_run_id]['au_roc'], metrics[best_run_id]['reg'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#Get the best run\n",
        "child_runs = {}\n",
        "\n",
        "for r in root_run.get_children():\n",
        "    child_runs[r.id] = r\n",
        "   \n",
        "best_run = child_runs[best_run_id]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#Download the model from the best run to a local folder\n",
        "best_model_file_name = \"best_model.zip\"\n",
        "best_run.download_file(name = 'outputs/' + model_name, output_file_path = best_model_file_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "##unzip the model to dbfs (as load() seems to require that) and load it.\n",
        "if os.path.isfile(model_dbfs) or os.path.isdir(model_dbfs):\n",
        "    shutil.rmtree(model_dbfs)\n",
        "shutil.unpack_archive(best_model_file_name, model_dbfs)\n",
        "\n",
        "model_p_best = PipelineModel.load(model_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# make prediction\n",
        "pred = model_p_best.transform(test)\n",
        "output = pred[['hours_per_week','age','workclass','marital_status','income','prediction']]\n",
        "display(output.limit(5))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# evaluate. note only 2 metrics are supported out of the box by Spark ML.\n",
        "bce = BinaryClassificationEvaluator(rawPredictionCol='rawPrediction')\n",
        "au_roc = bce.setMetricName('areaUnderROC').evaluate(pred)\n",
        "au_prc = bce.setMetricName('areaUnderPR').evaluate(pred)\n",
        "\n",
        "print(\"Area under ROC: {}\".format(au_roc))\n",
        "print(\"Area Under PR: {}\".format(au_prc))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#Model Persistence"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "##NOTE: by default the model is saved to and loaded from /dbfs/ instead of cwd!\n",
        "model_p_best.write().overwrite().save(model_name)\n",
        "print(\"saved model to {}\".format(model_dbfs))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%sh\n",
        "\n",
        "ls -la /dbfs/AdultCensus_runHistory.mml/*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "dbutils.notebook.exit(\"success\")"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "pasha"
      },
      {
        "name": "wamartin"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "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": "3.7.0"
    },
    "name": "03.Build_model_runHistory",
    "notebookId": 3836944406456339
  },
  "nbformat": 4,
  "nbformat_minor": 1
 }
--- a/how-to-use-azureml/azure-databricks/amlsdk/deploy-to-aci-04.ipynb
+++ b/how-to-use-azureml/azure-databricks/amlsdk/deploy-to-aci-04.ipynb
@@ -0,0 +1,338 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
        "\n",
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Please ensure you have run all previous notebooks in sequence before running this.\n",
        "\n",
        "Please Register Azure Container Instance(ACI) using Azure Portal: https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-manager-supported-services#portal in your subscription before using the SDK to deploy your ML model to ACI."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![04ACI](files/tables/image3.JPG)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import azureml.core\n",
        "\n",
        "# Check core SDK version number\n",
        "print(\"SDK version:\", azureml.core.VERSION)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import Workspace\n",
        "\n",
        "#'''\n",
        "ws = Workspace.from_config(auth = auth)\n",
        "print('Workspace name: ' + ws.name, \n",
        "      'Azure region: ' + ws.location, \n",
        "      'Subscription id: ' + ws.subscription_id, \n",
        "      'Resource group: ' + ws.resource_group, sep = '\\n')\n",
        "#'''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import Workspace\n",
        "import azureml.core\n",
        "\n",
        "# Check core SDK version number\n",
        "print(\"SDK version:\", azureml.core.VERSION)\n",
        "\n",
        "#'''\n",
        "ws = Workspace.from_config()\n",
        "print('Workspace name: ' + ws.name, \n",
        "      'Azure region: ' + ws.location, \n",
        "      'Subscription id: ' + ws.subscription_id, \n",
        "      'Resource group: ' + ws.resource_group, sep = '\\n')\n",
        "#'''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "##NOTE: service deployment always gets the model from the current working dir.\n",
        "import os\n",
        "\n",
        "model_name = \"AdultCensus_runHistory.mml\" # \n",
        "model_name_dbfs = os.path.join(\"/dbfs\", model_name)\n",
        "\n",
        "print(\"copy model from dbfs to local\")\n",
        "model_local = \"file:\" + os.getcwd() + \"/\" + model_name\n",
        "dbutils.fs.cp(model_name, model_local, True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#Register the model\n",
        "from azureml.core.model import Model\n",
        "mymodel = Model.register(model_path = model_name, # this points to a local file\n",
        "                       model_name = model_name, # this is the name the model is registered as, am using same name for both path and name.                 \n",
        "                       description = \"ADB trained model by Parashar\",\n",
        "                       workspace = ws)\n",
        "\n",
        "print(mymodel.name, mymodel.description, mymodel.version)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#%%writefile score_sparkml.py\n",
        "score_sparkml = \"\"\"\n",
        " \n",
        "import json\n",
        " \n",
        "def init():\n",
        "    # One-time initialization of PySpark and predictive model\n",
        "    import pyspark\n",
        "    from azureml.core.model import Model\n",
        "    from pyspark.ml import PipelineModel\n",
        " \n",
        "    global trainedModel\n",
        "    global spark\n",
        " \n",
        "    spark = pyspark.sql.SparkSession.builder.appName(\"ADB and AML notebook by Parashar\").getOrCreate()\n",
        "    model_name = \"{model_name}\" #interpolated\n",
        "    model_path = Model.get_model_path(model_name)\n",
        "    trainedModel = PipelineModel.load(model_path)\n",
        "    \n",
        "def run(input_json):\n",
        "    if isinstance(trainedModel, Exception):\n",
        "        return json.dumps({{\"trainedModel\":str(trainedModel)}})\n",
        "      \n",
        "    try:\n",
        "        sc = spark.sparkContext\n",
        "        input_list = json.loads(input_json)\n",
        "        input_rdd = sc.parallelize(input_list)\n",
        "        input_df = spark.read.json(input_rdd)\n",
        "    \n",
        "        # Compute prediction\n",
        "        prediction = trainedModel.transform(input_df)\n",
        "        #result = prediction.first().prediction\n",
        "        predictions = prediction.collect()\n",
        " \n",
        "        #Get each scored result\n",
        "        preds = [str(x['prediction']) for x in predictions]\n",
        "        result = \",\".join(preds)\n",
        "        # you can return any data type as long as it is JSON-serializable\n",
        "        return result.tolist()\n",
        "    except Exception as e:\n",
        "        result = str(e)\n",
        "        return result\n",
        "    \n",
        "\"\"\".format(model_name=model_name)\n",
        " \n",
        "exec(score_sparkml)\n",
        " \n",
        "with open(\"score_sparkml.py\", \"w\") as file:\n",
        "    file.write(score_sparkml)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.conda_dependencies import CondaDependencies \n",
        "\n",
        "myacienv = CondaDependencies.create(conda_packages=['scikit-learn','numpy','pandas']) #showing how to add libs as an eg. - not needed for this model.\n",
        "\n",
        "with open(\"mydeployenv.yml\",\"w\") as f:\n",
        "    f.write(myacienv.serialize_to_string())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#deploy to ACI\n",
        "from azureml.core.webservice import AciWebservice, Webservice\n",
        "\n",
        "myaci_config = AciWebservice.deploy_configuration(\n",
        "    cpu_cores = 2, \n",
        "    memory_gb = 2, \n",
        "    tags = {'name':'Databricks Azure ML ACI'}, \n",
        "    description = 'This is for ADB and AML example. Azure Databricks & Azure ML SDK demo with ACI by Parashar.')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# this will take 10-15 minutes to finish\n",
        "\n",
        "service_name = \"aciws\"\n",
        "runtime = \"spark-py\" \n",
        "driver_file = \"score_sparkml.py\"\n",
        "my_conda_file = \"mydeployenv.yml\"\n",
        "\n",
        "# image creation\n",
        "from azureml.core.image import ContainerImage\n",
        "myimage_config = ContainerImage.image_configuration(execution_script = driver_file, \n",
        "                                    runtime = runtime, \n",
        "                                    conda_file = my_conda_file)\n",
        "\n",
        "# Webservice creation\n",
        "myservice = Webservice.deploy_from_model(\n",
        "  workspace=ws, \n",
        "  name=service_name,\n",
        "  deployment_config = myaci_config,\n",
        "  models = [mymodel],\n",
        "  image_config = myimage_config\n",
        "    )\n",
        "\n",
        "myservice.wait_for_deployment(show_output=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "help(Webservice)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# List images by ws\n",
        "\n",
        "for i in ContainerImage.list(workspace = ws):\n",
        "    print('{}(v.{} [{}]) stored at {} with build log {}'.format(i.name, i.version, i.creation_state, i.image_location, i.image_build_log_uri))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#for using the Web HTTP API \n",
        "print(myservice.scoring_uri)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import json\n",
        "\n",
        "#get the some sample data\n",
        "test_data_path = \"AdultCensusIncomeTest\"\n",
        "test = spark.read.parquet(test_data_path).limit(5)\n",
        "\n",
        "test_json = json.dumps(test.toJSON().collect())\n",
        "\n",
        "print(test_json)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#using data defined above predict if income is >50K (1) or <=50K (0)\n",
        "myservice.run(input_data=test_json)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#comment to not delete the web service\n",
        "#myservice.delete()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "pasha"
      },
      {
        "name": "wamartin"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "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": "3.7.0"
    },
    "name": "04.DeploytoACI",
    "notebookId": 3836944406456376
  },
  "nbformat": 4,
  "nbformat_minor": 1
 }
--- a/how-to-use-azureml/azure-databricks/amlsdk/ingest-data-02.ipynb
+++ b/how-to-use-azureml/azure-databricks/amlsdk/ingest-data-02.ipynb
@@ -0,0 +1,182 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
        "\n",
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![04ACI](files/tables/image1.JPG)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#Data Ingestion"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "import urllib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Download AdultCensusIncome.csv from Azure CDN. This file has 32,561 rows.\n",
        "basedataurl = \"https://amldockerdatasets.azureedge.net\"\n",
        "datafile = \"AdultCensusIncome.csv\"\n",
        "datafile_dbfs = os.path.join(\"/dbfs\", datafile)\n",
        "\n",
        "if os.path.isfile(datafile_dbfs):\n",
        "    print(\"found {} at {}\".format(datafile, datafile_dbfs))\n",
        "else:\n",
        "    print(\"downloading {} to {}\".format(datafile, datafile_dbfs))\n",
        "    urllib.request.urlretrieve(os.path.join(basedataurl, datafile), datafile_dbfs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Create a Spark dataframe out of the csv file.\n",
        "data_all = sqlContext.read.format('csv').options(header='true', inferSchema='true', ignoreLeadingWhiteSpace='true', ignoreTrailingWhiteSpace='true').load(datafile)\n",
        "print(\"({}, {})\".format(data_all.count(), len(data_all.columns)))\n",
        "data_all.printSchema()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#renaming columns\n",
        "columns_new = [col.replace(\"-\", \"_\") for col in data_all.columns]\n",
        "data_all = data_all.toDF(*columns_new)\n",
        "data_all.printSchema()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "display(data_all.limit(5))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#Data Preparation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Choose feature columns and the label column.\n",
        "label = \"income\"\n",
        "xvars = set(data_all.columns) - {label}\n",
        "\n",
        "print(\"label = {}\".format(label))\n",
        "print(\"features = {}\".format(xvars))\n",
        "\n",
        "data = data_all.select([*xvars, label])\n",
        "\n",
        "# Split data into train and test.\n",
        "train, test = data.randomSplit([0.75, 0.25], seed=123)\n",
        "\n",
        "print(\"train ({}, {})\".format(train.count(), len(train.columns)))\n",
        "print(\"test ({}, {})\".format(test.count(), len(test.columns)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#Data Persistence"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Write the train and test data sets to intermediate storage\n",
        "train_data_path = \"AdultCensusIncomeTrain\"\n",
        "test_data_path = \"AdultCensusIncomeTest\"\n",
        "\n",
        "train_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTrain\")\n",
        "test_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTest\")\n",
        "\n",
        "train.write.mode('overwrite').parquet(train_data_path)\n",
        "test.write.mode('overwrite').parquet(test_data_path)\n",
        "print(\"train and test datasets saved to {} and {}\".format(train_data_path_dbfs, test_data_path_dbfs))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "pasha"
      },
      {
        "name": "wamartin"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "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": "3.7.0"
    },
    "name": "02.Ingest_data",
    "notebookId": 3836944406456362
  },
  "nbformat": 4,
  "nbformat_minor": 1
 }
--- a/how-to-use-azureml/azure-databricks/amlsdk/installation-and-configuration-01.ipynb
+++ b/how-to-use-azureml/azure-databricks/amlsdk/installation-and-configuration-01.ipynb
@@ -0,0 +1,179 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
        "\n",
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package. You can select the option to attach the library to all clusters or just one cluster.\n",
        "\n",
        "**install azureml-sdk**\n",
        "* Source: Upload Python Egg or PyPi\n",
        "* PyPi Name: `azureml-sdk[databricks]`\n",
        "* Select Install Library"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import azureml.core\n",
        "\n",
        "# Check core SDK version number - based on build number of preview/master.\n",
        "print(\"SDK version:\", azureml.core.VERSION)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![04ACI](files/tables/image2b.JPG)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Please specify the Azure subscription Id, resource group name, workspace name, and the region in which you want to create the Azure Machine Learning Workspace.\n",
        "\n",
        "You can get the value of your Azure subscription ID from the Azure Portal, and then selecting Subscriptions from the menu on the left.\n",
        "\n",
        "For the resource_group, use the name of the resource group that contains your Azure Databricks Workspace.\n",
        "\n",
        "NOTE: If you provide a resource group name that does not exist, the resource group will be automatically created. This may or may not succeed in your environment, depending on the permissions you have on your Azure Subscription."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# subscription_id = \"<your-subscription-id>\"\n",
        "# resource_group = \"<your-existing-resource-group>\"\n",
        "# workspace_name = \"<a-new-or-existing-workspace; it is unrelated to Databricks workspace>\"\n",
        "# workspace_region = \"<your-resource group-region>\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# import the Workspace class and check the azureml SDK version\n",
        "# exist_ok checks if workspace exists or not.\n",
        "\n",
        "from azureml.core import Workspace\n",
        "\n",
        "ws = Workspace.create(name = workspace_name,\n",
        "                      subscription_id = subscription_id,\n",
        "                      resource_group = resource_group, \n",
        "                      location = workspace_region,\n",
        "                      exist_ok=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#get workspace details\n",
        "ws.get_details()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace(workspace_name = workspace_name,\n",
        "               subscription_id = subscription_id,\n",
        "               resource_group = resource_group)\n",
        "\n",
        "# persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
        "ws.write_config()\n",
        "##if you need to give a different path/filename please use this\n",
        "##write_config(path=\"/databricks/driver/aml_config/\",file_name=<alias_conf.cfg>)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "help(Workspace)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# import the Workspace class and check the azureml SDK version\n",
        "from azureml.core import Workspace\n",
        "\n",
        "ws = Workspace.from_config()\n",
        "#ws = Workspace.from_config(<full path>)\n",
        "print('Workspace name: ' + ws.name, \n",
        "      'Azure region: ' + ws.location, \n",
        "      'Subscription id: ' + ws.subscription_id, \n",
        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "pasha"
      },
      {
        "name": "wamartin"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "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": "3.7.0"
    },
    "name": "01.Installation_and_Configuration",
    "notebookId": 3836944406456490
  },
  "nbformat": 4,
  "nbformat_minor": 1
 }
--- a/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb
+++ b/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb
@@ -0,0 +1,559 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Automated ML on Azure Databricks\n",
        "\n",
        "In this example we use the scikit-learn's <a href=\"http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset\" target=\"_blank\">digit dataset</a> to showcase how you can use AutoML for a simple classification problem.\n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. Create Azure Machine Learning Workspace object and initialize your notebook directory to easily reload this object from a configuration file.\n",
        "2. Create an `Experiment` in an existing `Workspace`.\n",
        "3. Configure Automated ML using `AutoMLConfig`.\n",
        "4. Train the model using Azure Databricks.\n",
        "5. Explore the results.\n",
        "6. Test the best fitted model.\n",
        "\n",
        "Before running this notebook, please follow the <a href=\"https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks\" target=\"_blank\">readme for using Automated ML on Azure Databricks</a> for installing necessary libraries to your cluster."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We support installing AML SDK with Automated ML as library from GUI. When attaching a library follow <a href=\"https://docs.databricks.com/user-guide/libraries.html\" target=\"_blank\">this link</a> and add the below string as your PyPi package. You can select the option to attach the library to all clusters or just one cluster.\n",
        "\n",
        "**azureml-sdk with automated ml**\n",
        "* Source: Upload Python Egg or PyPi\n",
        "* PyPi Name: `azureml-sdk[automl_databricks]`\n",
        "* Select Install Library"
      ]
    },
    {
      "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 = \"<Your SubscriptionId>\" #you should be owner or contributor\n",
        "resource_group = \"<Resource group - new or existing>\" #you should be owner or contributor\n",
        "workspace_name = \"<workspace to be created>\" #your workspace name\n",
        "workspace_region = \"<azureregion>\" #your region"
      ]
    },
    {
      "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",
        "                      exist_ok=True)\n",
        "ws.get_details()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    },
    {
      "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": [
        "## Create a Folder to Host Sample Projects\n",
        "Finally, create a folder where all the sample projects will be hosted."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "sample_projects_folder = './sample_projects'\n",
        "\n",
        "if not os.path.isdir(sample_projects_folder):\n",
        "    os.mkdir(sample_projects_folder)\n",
        "    \n",
        "print('Sample projects will be created in {}.'.format(sample_projects_folder))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create an Experiment\n",
        "\n",
        "As part of the setup you have already created an Azure ML `Workspace` object. For Automated ML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import logging\n",
        "import os\n",
        "import random\n",
        "import time\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "from matplotlib.pyplot import imshow\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.train.automl import AutoMLConfig\n",
        "from azureml.train.automl.run import AutoMLRun"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Choose a name for the experiment and specify the project folder.\n",
        "experiment_name = 'automl-local-classification'\n",
        "project_folder = './sample_projects/automl-local-classification'\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace Name'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Project Directory'] = project_folder\n",
        "output['Experiment Name'] = experiment.name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "pd.DataFrame(data = output, index = ['']).T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Diagnostics\n",
        "\n",
        "Opt-in diagnostics for better experience, quality, and security of future releases."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.telemetry import set_diagnostics_collection\n",
        "set_diagnostics_collection(send_diagnostics = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Load Training Data Using DataPrep"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#Automated ML requires a dataflow, which is different from dataframe.\n",
        "#If your data is in a dataframe, please use read_pandas_dataframe to convert a dataframe to dataflow before usind dprep.\n",
        "\n",
        "import azureml.dataprep as dprep\n",
        "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
        "# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
        "simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
        "X_train = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1)  # Remove the header row.\n",
        "\n",
        "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
        "# and convert column types manually.\n",
        "# Here we read a comma delimited file and convert all columns to integers.\n",
        "y_train = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Review the Data Preparation Result\n",
        "You can peek the result of a Dataflow at any range using skip(i) and head(j). Doing so evaluates only j records for all the steps in the Dataflow, which makes it fast even against large datasets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "X_train.skip(1).head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Configure AutoML\n",
        "\n",
        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
        "|**task**|classification or regression|\n",
        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
        "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
        "|**n_cross_validations**|Number of cross validation splits.|\n",
        "|**spark_context**|Spark Context object. for Databricks, use spark_context=sc|\n",
        "|**max_concurrent_iterations**|Maximum number of iterations to execute in parallel. This should be <= number of worker nodes in your Azure Databricks cluster.|\n",
        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
        "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
        "|**preprocess**|set this to True to enable pre-processing of data eg. string to numeric using one-hot encoding|\n",
        "|**exit_score**|Target score for experiment. It is associated with the metric. eg. exit_score=0.995 will exit experiment after that|"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             debug_log = 'automl_errors.log',\n",
        "                             primary_metric = 'AUC_weighted',\n",
        "                             iteration_timeout_minutes = 10,\n",
        "                             iterations = 30,\n",
        "                             n_cross_validations = 10,\n",
        "                             max_concurrent_iterations = 2, #change it based on number of worker nodes\n",
        "                             verbosity = logging.INFO,\n",
        "                             spark_context=sc, #databricks/spark related\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) # for higher runs please use show_output=False and use the below"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Explore the Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Portal URL for Monitoring Runs\n",
        "\n",
        "The following will provide a link to the web interface to explore individual run details and status. In the future we might support output displayed in the notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "displayHTML(\"<a href={} target='_blank'>Your experiment in Azure Portal: {}</a>\".format(local_run.get_portal_url(), local_run.id))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The following will show the child runs and waits for the parent run to complete."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Retrieve All Child Runs after the experiment is completed (in portal)\n",
        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "children = list(local_run.get_children())\n",
        "metricslist = {}\n",
        "for run in children:\n",
        "    properties = run.get_properties()\n",
        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
        "    metricslist[int(properties['iteration'])] = metrics\n",
        "\n",
        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
        "rundata"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Retrieve the Best Model after the above run is complete \n",
        "\n",
        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run, fitted_model = local_run.get_output()\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Best Model Based on Any Other Metric after the above run is complete based on the child run\n",
        "Show the run and the model that has the smallest `log_loss` value:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "lookup_metric = \"log_loss\"\n",
        "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Test the Best Fitted Model\n",
        "\n",
        "#### Load Test Data - you can split the dataset beforehand & pass Train dataset to AutoML and use Test dataset to evaluate the best model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn import datasets\n",
        "digits = datasets.load_digits()\n",
        "X_test = digits.data[:10, :]\n",
        "y_test = digits.target[:10]\n",
        "images = digits.images[:10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Testing Our Best Fitted Model\n",
        "We will try to predict digits and see how our model works. This is just an example to show you."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Randomly select digits and test.\n",
        "for index in np.random.choice(len(y_test), 2, replace = False):\n",
        "    print(index)\n",
        "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
        "    label = y_test[index]\n",
        "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
        "    fig = plt.figure(1, figsize = (3,3))\n",
        "    ax1 = fig.add_axes((0,0,.8,.8))\n",
        "    ax1.set_title(title)\n",
        "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
        "    display(fig)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "When deploying an automated ML trained model, please specify _pippackages=['azureml-sdk[automl]']_ in your CondaDependencies.\n",
        "\n",
        "Please refer to only the **Deploy** section in this notebook - <a href=\"https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification-with-deployment\" target=\"_blank\">Deployment of Automated ML trained model</a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "savitam"
      },
      {
        "name": "wamartin"
      }
    ],
    "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": "3.7.0"
    },
    "name": "auto-ml-classification-local-adb",
    "notebookId": 817220787969977
  },
  "nbformat": 4,
  "nbformat_minor": 0
 }
--- a/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb
+++ b/how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb
@@ -0,0 +1,704 @@
 {
  "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": [
        "We support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package. You can select the option to attach the library to all clusters or just one cluster.\n",
        "\n",
        "**install azureml-sdk with Automated ML**\n",
        "* Source: Upload Python Egg or PyPi\n",
        "* PyPi Name: `azureml-sdk[automl_databricks]`\n",
        "* Select Install Library"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# AutoML : Classification with Local Compute on Azure DataBricks with deployment to ACI\n",
        "\n",
        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. Create Azure Machine Learning Workspace object and initialize your notebook directory to easily reload this object from a configuration file.\n",
        "2. Create an `Experiment` in an existing `Workspace`.\n",
        "3. Configure AutoML using `AutoMLConfig`.\n",
        "4. Train the model using AzureDataBricks.\n",
        "5. Explore the results.\n",
        "6. Register the model.\n",
        "7. Deploy the model.\n",
        "8. Test the best fitted model.\n",
        "\n",
        "Prerequisites:\n",
        "Before running this notebook, please follow the readme for installing necessary libraries to your cluster."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Register Machine Learning Services Resource Provider\n",
        "Microsoft.MachineLearningServices only needs to be registed once in the subscription. To register it:\n",
        "Start the Azure portal.\n",
        "Select your All services and then Subscription.\n",
        "Select the subscription that you want to use.\n",
        "Click on Resource providers\n",
        "Click the Register link next to Microsoft.MachineLearningServices"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Check the Azure ML Core SDK Version to Validate Your Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import azureml.core\n",
        "\n",
        "print(\"SDK Version:\", azureml.core.VERSION)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Initialize an Azure ML Workspace\n",
        "### What is an Azure ML Workspace and Why Do I Need One?\n",
        "\n",
        "An Azure ML workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an Azure ML workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, operationalization, and the monitoring of operationalized models.\n",
        "\n",
        "\n",
        "### What do I Need?\n",
        "\n",
        "To create or access an Azure ML workspace, you will need to import the Azure ML library and specify following information:\n",
        "* A name for your workspace. You can choose one.\n",
        "* Your subscription id. Use the `id` value from the `az account show` command output above.\n",
        "* The resource group name. The resource group organizes Azure resources and provides a default region for the resources in the group. The resource group will be created if it doesn't exist. Resource groups can be created and viewed in the [Azure portal](https://portal.azure.com)\n",
        "* Supported regions include `eastus2`, `eastus`,`westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "subscription_id = \"<Your SubscriptionId>\"\n",
        "resource_group = \"<Resource group - new or existing>\"\n",
        "workspace_name = \"<workspace to be created>\"\n",
        "workspace_region = \"<azureregion>\""
      ]
    },
    {
      "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",
        "                      exist_ok=True)\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": [
        "## Create a Folder to Host Sample Projects\n",
        "Finally, create a folder where all the sample projects will be hosted."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "sample_projects_folder = './sample_projects'\n",
        "\n",
        "if not os.path.isdir(sample_projects_folder):\n",
        "    os.mkdir(sample_projects_folder)\n",
        "    \n",
        "print('Sample projects will be created in {}.'.format(sample_projects_folder))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create an Experiment\n",
        "\n",
        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import logging\n",
        "import os\n",
        "import random\n",
        "import time\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "from matplotlib.pyplot import imshow\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.train.automl import AutoMLConfig\n",
        "from azureml.train.automl.run import AutoMLRun"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Choose a name for the experiment and specify the project folder.\n",
        "experiment_name = 'automl-local-classification'\n",
        "project_folder = './sample_projects/automl-local-classification'\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace Name'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Project Directory'] = project_folder\n",
        "output['Experiment Name'] = experiment.name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "pd.DataFrame(data = output, index = ['']).T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Diagnostics\n",
        "\n",
        "Opt-in diagnostics for better experience, quality, and security of future releases."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.telemetry import set_diagnostics_collection\n",
        "set_diagnostics_collection(send_diagnostics = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Load Training Data Using DataPrep"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import azureml.dataprep as dprep\n",
        "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
        "# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
        "simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
        "X_train = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1)  # Remove the header row.\n",
        "\n",
        "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
        "# and convert column types manually.\n",
        "# Here we read a comma delimited file and convert all columns to integers.\n",
        "y_train = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Review the Data Preparation Result\n",
        "You can peek the result of a Dataflow at any range using skip(i) and head(j). Doing so evaluates only j records for all the steps in the Dataflow, which makes it fast even against large datasets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "X_train.skip(1).head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Configure AutoML\n",
        "\n",
        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
        "|**task**|classification or regression|\n",
        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
        "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
        "|**n_cross_validations**|Number of cross validation splits.|\n",
        "|**spark_context**|Spark Context object. for Databricks, use spark_context=sc|\n",
        "|**max_concurrent_iterations**|Maximum number of iterations to execute in parallel. This should be <= number of worker nodes in your Azure Databricks cluster.|\n",
        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
        "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
        "|**preprocess**|set this to True to enable pre-processing of data eg. string to numeric using one-hot encoding|\n",
        "|**exit_score**|Target score for experiment. It is associated with the metric. eg. exit_score=0.995 will exit experiment after that|"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             debug_log = 'automl_errors.log',\n",
        "                             primary_metric = 'AUC_weighted',\n",
        "                             iteration_timeout_minutes = 10,\n",
        "                             iterations = 5,\n",
        "                             n_cross_validations = 2,\n",
        "                             max_concurrent_iterations = 4, #change it based on number of worker nodes\n",
        "                             verbosity = logging.INFO,\n",
        "                             spark_context=sc, #databricks/spark related\n",
        "                             X = X_train, \n",
        "                             y = y_train,\n",
        "                             enable_cache=False,\n",
        "                             path = project_folder)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train the Models\n",
        "\n",
        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
        "In this example, we specify `show_output = True` to print currently running iterations to the console."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run = experiment.submit(automl_config, show_output = True) # for higher runs please use show_output=False and use the below"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Explore the Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Portal URL for Monitoring Runs\n",
        "\n",
        "The following will provide a link to the web interface to explore individual run details and status. In the future we might support output displayed in the notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "displayHTML(\"<a href={} target='_blank'>Azure Portal: {}</a>\".format(local_run.get_portal_url(), local_run.id))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The following will show the child runs and waits for the parent run to complete."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Retrieve All Child Runs after the experiment is completed (in portal)\n",
        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "children = list(local_run.get_children())\n",
        "metricslist = {}\n",
        "for run in children:\n",
        "    properties = run.get_properties()\n",
        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
        "    metricslist[int(properties['iteration'])] = metrics\n",
        "\n",
        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
        "rundata"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Retrieve the Best Model after the above run is complete \n",
        "\n",
        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run, fitted_model = local_run.get_output()\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Best Model Based on Any Other Metric after the above run is complete based on the child run\n",
        "Show the run and the model that has the smallest `log_loss` value:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "lookup_metric = \"log_loss\"\n",
        "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 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 scoring script file later in the notebook."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create Scoring Script\n",
        "Replace model_id with name of model from output of above register cell"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%%writefile score.py\n",
        "import pickle\n",
        "import json\n",
        "import numpy\n",
        "import azureml.train.automl\n",
        "from sklearn.externals import joblib\n",
        "from azureml.core.model import Model\n",
        "\n",
        "\n",
        "def init():\n",
        "    global model\n",
        "    model_path = Model.get_model_path(model_name = '<<modelid>>') # this name is model.id of model that we want to deploy\n",
        "    # deserialize the model file back into a sklearn model\n",
        "    model = joblib.load(model_path)\n",
        "\n",
        "def run(rawdata):\n",
        "    try:\n",
        "        data = json.loads(rawdata)['data']\n",
        "        data = numpy.array(data)\n",
        "        result = model.predict(data)\n",
        "    except Exception as e:\n",
        "        result = str(e)\n",
        "        return json.dumps({\"error\": result})\n",
        "    return json.dumps({\"result\":result.tolist()})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Create a YAML File for the Environment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "\n",
        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-sdk[automl]'])\n",
        "\n",
        "conda_env_file_name = 'mydeployenv.yml'\n",
        "myenv.save_to_file('.', conda_env_file_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Create ACI config"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#deploy to ACI\n",
        "from azureml.core.webservice import AciWebservice, Webservice\n",
        "\n",
        "myaci_config = AciWebservice.deploy_configuration(\n",
        "    cpu_cores = 2, \n",
        "    memory_gb = 2, \n",
        "    tags = {'name':'Databricks Azure ML ACI'}, \n",
        "    description = 'This is for ADB and AutoML example.')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Deploy the Image as a Web Service on Azure Container Instance\n",
        "Replace servicename with any meaningful name of service"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\n",
        "# this will take 10-15 minutes to finish\n",
        "\n",
        "service_name = \"<<servicename>>\"\n",
        "runtime = \"spark-py\" \n",
        "driver_file = \"score.py\"\n",
        "my_conda_file = \"mydeployenv.yml\"\n",
        "\n",
        "# image creation\n",
        "from azureml.core.image import ContainerImage\n",
        "myimage_config = ContainerImage.image_configuration(execution_script = driver_file, \n",
        "                                    runtime = runtime, \n",
        "                                    conda_file = 'mydeployenv.yml')\n",
        "\n",
        "# Webservice creation\n",
        "myservice = Webservice.deploy_from_model(\n",
        "  workspace=ws, \n",
        "  name=service_name,\n",
        "  deployment_config = myaci_config,\n",
        "  models = [model],\n",
        "  image_config = myimage_config\n",
        "    )\n",
        "\n",
        "myservice.wait_for_deployment(show_output=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#for using the Web HTTP API \n",
        "print(myservice.scoring_uri)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Test the Best Fitted Model\n",
        "\n",
        "#### Load Test Data - you can split the dataset beforehand & pass Train dataset to AutoML and use Test dataset to evaluate the best model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn import datasets\n",
        "digits = datasets.load_digits()\n",
        "X_test = digits.data[:10, :]\n",
        "y_test = digits.target[:10]\n",
        "images = digits.images[:10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Testing Our Best Fitted Model\n",
        "We will try to predict digits and see how our model works. This is just an example to show you."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Randomly select digits and test.\n",
        "for index in np.random.choice(len(y_test), 2, replace = False):\n",
        "    print(index)\n",
        "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
        "    label = y_test[index]\n",
        "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
        "    fig = plt.figure(1, figsize = (3,3))\n",
        "    ax1 = fig.add_axes((0,0,.8,.8))\n",
        "    ax1.set_title(title)\n",
        "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
        "    display(fig)"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "savitam"
      },
      {
        "name": "wamartin"
      }
    ],
    "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": "3.7.0"
    },
    "name": "auto-ml-classification-local-adb",
    "notebookId": 3888835968049288
  },
  "nbformat": 4,
  "nbformat_minor": 0
 }
--- a/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb
+++ b/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb
@@ -38,13 +38,11 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "from azureml.core import Workspace, Run\n",
+        "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.webservice import AksWebservice\n",
        "from azureml.core.image import Image\n",
        "from azureml.core.model import Model\n",
        "\n",
        "import azureml.core\n",
        "import json\n",
        "print(azureml.core.VERSION)"
      ]
    },
@@ -52,8 +50,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## 2. Set up your configuration and create a workspace\n",
+        "## 2. Set up your configuration and create a workspace\n"
        "Follow Notebook 00 instructions to do this.\n"
      ]
    },
    {
@@ -103,8 +100,7 @@
        "\n",
        "### b. In your run function add:\n",
        "```python\n",
-        "print (\"saving input data\" + time.strftime(\"%H:%M:%S\"))\n",
+        "print (\"Prediction created\" + time.strftime(\"%H:%M:%S\"))```"
        "print (\"saving prediction data\" + time.strftime(\"%H:%M:%S\"))```"
      ]
    },
    {
@@ -120,7 +116,6 @@
        "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",
@@ -135,39 +130,20 @@
        "    # 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 (\"Prediction created\" + time.strftime(\"%H:%M:%S\"))\n",
-        "        #Print statement for appinsights custom traces:\n",
+        "        # you can return any datatype as long as it is JSON-serializable\n",
-        "        print (\"saving input data\" + time.strftime(\"%H:%M:%S\"))\n",
+        "        return result.tolist()\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",
+        "        error = str(e)\n",
-        "        print (result + time.strftime(\"%H:%M:%S\"))\n",
+        "        print (error + time.strftime(\"%H:%M:%S\"))\n",
-        "        return json.dumps({\"error\": result})"
+        "        return error"
      ]
    },
    {
@@ -222,6 +198,74 @@
        "image.wait_for_creation(show_output = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Deploy to ACI (Optional)"
      ]
    },
    {
      "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': \"diabetes\", 'type': \"regression\"}, \n",
        "                                               description = 'Predict diabetes using regression model',\n",
        "                                               enable_app_insights = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.webservice import Webservice\n",
        "\n",
        "aci_service_name = 'my-aci-service-4'\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": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%%time\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')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "if aci_service.state == \"Healthy\":\n",
        "    prediction = aci_service.run(input_data=test_sample)\n",
        "    print(prediction)\n",
        "else:\n",
        "    raise ValueError(\"Service deployment isn't healthy, can't call the service\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -233,7 +277,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Create AKS compute if you haven't done so (Notebook 11)"
+        "### Create AKS compute if you haven't done so."
      ]
    },
    {
@@ -245,7 +289,7 @@
        "# Use the default configuration (can also provide parameters to customize)\n",
        "prov_config = AksCompute.provisioning_configuration()\n",
        "\n",
-        "aks_name = 'my-aks-test1' \n",
+        "aks_name = 'my-aks-test3' \n",
        "# Create the cluster\n",
        "aks_target = ComputeTarget.create(workspace = ws, \n",
        "                                  name = aks_name, \n",
@@ -259,7 +303,15 @@
      "outputs": [],
      "source": [
        "%%time\n",
-        "aks_target.wait_for_completion(show_output = True)\n",
+        "aks_target.wait_for_completion(show_output = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print(aks_target.provisioning_state)\n",
        "print(aks_target.provisioning_errors)"
      ]
@@ -279,9 +331,10 @@
        "%%time\n",
        "resource_id = '/subscriptions/<subscriptionid>/resourcegroups/<resourcegroupname>/providers/Microsoft.ContainerService/managedClusters/<aksservername>'\n",
        "create_name= 'myaks4'\n",
-        "aks_target = AksCompute.attach(workspace = ws, \n",
+        "attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
        "aks_target = ComputeTarget.attach(workspace = ws, \n",
        "                                  name = create_name, \n",
-        "                                  #esource_id=resource_id)\n",
+        "                                  attach_configuration=attach_config)\n",
        "## Wait for the operation to complete\n",
        "aks_target.wait_for_provisioning(True)```"
      ]
@@ -317,9 +370,8 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "%%time\n",
+        "if aks_target.provisioning_state== \"Succeeded\": \n",
-        "aks_service_name ='aks-w-dc3'\n",
+        "    aks_service_name ='aks-w-dc5'\n",
        "\n",
        "    aks_service = Webservice.deploy_from_image(workspace = ws, \n",
        "                                               name = aks_service_name,\n",
        "                                               image = image,\n",
@@ -327,7 +379,9 @@
        "                                               deployment_target = aks_target\n",
        "                                               )\n",
        "    aks_service.wait_for_deployment(show_output = True)\n",
-        "print(aks_service.state)"
+        "    print(aks_service.state)\n",
        "else:\n",
        "    raise ValueError(\"AKS provisioning failed.\")"
      ]
    },
    {
@@ -344,7 +398,6 @@
      "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",
@@ -352,8 +405,11 @@
        "]})\n",
        "test_sample = bytes(test_sample,encoding='utf8')\n",
        "\n",
        "if aks_service.state == \"Healthy\":\n",
        "    prediction = aks_service.run(input_data=test_sample)\n",
-        "print(prediction)"
+        "    print(prediction)\n",
        "else:\n",
        "    raise ValueError(\"Service deployment isn't healthy, can't call the service\")"
      ]
    },
    {
@@ -384,12 +440,32 @@
      "source": [
        "aks_service.update(enable_app_insights=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Clean up"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%%time\n",
        "aks_service.delete()\n",
        "aci_service.delete()\n",
        "image.delete()\n",
        "model.delete()"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
-        "name": "marthalc"
+        "name": "jocier"
      }
    ],
    "kernelspec": {
@@ -407,7 +483,7 @@
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
+      "version": "3.6.3"
    }
  },
  "nbformat": 4,
--- a/how-to-use-azureml/deployment/enable-app-insights-in-production-service/sklearn_regression_model.pkl
+++ b/how-to-use-azureml/deployment/enable-app-insights-in-production-service/sklearn_regression_model.pkl
--- a/how-to-use-azureml/deployment/enable-data-collection-for-models-in-aks/enable-data-collection-for-models-in-aks.ipynb
+++ b/how-to-use-azureml/deployment/enable-data-collection-for-models-in-aks/enable-data-collection-for-models-in-aks.ipynb
@@ -0,0 +1,471 @@
 {
  "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\n",
        "from azureml.core.compute import AksCompute, ComputeTarget\n",
        "from azureml.core.webservice import Webservice, AksWebservice\n",
        "import azureml.core\n",
        "print(azureml.core.VERSION)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 2. Set up your configuration and create a workspace"
      ]
    },
    {
      "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",
        "        # you can return any data type as long as it is JSON-serializable\n",
        "        return result.tolist()\n",
        "    except Exception as e:\n",
        "        error = str(e)\n",
        "        print (error + time.strftime(\"%H:%M:%S\"))\n",
        "        return error"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 5. *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."
      ]
    },
    {
      "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",
        "    attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
        "    aks_target = ComputeTarget.attach(workspace = ws, \n",
        "                                      name = create_name, \n",
        "                                      attach_configuration=attach_config)\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": [
        "if aks_target.provisioning_state== \"Succeeded\": \n",
        "    aks_service_name ='aks-w-dc0'\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)\n",
        "else: \n",
        "    raise ValueError(\"aks provisioning failed, can't deploy service\")"
      ]
    },
    {
      "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",
        "if aks_service.state == \"Healthy\":\n",
        "    prediction = aks_service.run(input_data=test_sample)\n",
        "    print(prediction)\n",
        "else:\n",
        "    raise ValueError(\"Service deployment isn't healthy, can't call the service\")"
      ]
    },
    {
      "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 \u201cGet Data\u201d and click on \u201cAzure Blob Storage\u201d >> 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 \u201cName\u201d 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 \u201cContent\u201d 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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Clean up"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%%time\n",
        "aks_service.delete()\n",
        "image.delete()\n",
        "model.delete()"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "jocier"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/deployment/enable-data-collection-for-models-in-aks/sklearn_regression_model.pkl
+++ b/how-to-use-azureml/deployment/enable-data-collection-for-models-in-aks/sklearn_regression_model.pkl
--- a/how-to-use-azureml/deployment/onnx/README.md
+++ b/how-to-use-azureml/deployment/onnx/README.md
@@ -0,0 +1,33 @@
 # ONNX on Azure Machine Learning
 These tutorials show how to create and deploy Open Neural Network eXchange ([ONNX](http://onnx.ai)) models in Azure Machine Learning environments using [ONNX Runtime](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-build-deploy-onnx) for inference. Once deployed as a web service, you can ping the model with your own set of images to be analyzed!
 ## Tutorials
 0. [Configure your Azure Machine Learning Workspace](../../../configuration.ipynb)
 #### Obtain models from the [ONNX Model Zoo](https://github.com/onnx/models) and deploy with ONNX Runtime Inference
 1. [Handwritten Digit Classification (MNIST)](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb)
 2. [Facial Expression Recognition (Emotion FER+)](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb)
 #### Demo Notebooks from Microsoft Ignite 2018
 Note that the following notebooks do not have evaluation sections for the models since they were deployed as part of a live demo. You can find the respective pre-processing and post-processing code linked from the ONNX Model Zoo Github pages ([ResNet](https://github.com/onnx/models/tree/master/models/image_classification/resnet), [TinyYoloV2](https://github.com/onnx/models/tree/master/tiny_yolov2)), or experiment with the ONNX models by [running them in the browser](https://microsoft.github.io/onnxjs-demo/#/).
 3. [Image Recognition (ResNet50)](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb)
 4. [Convert Core ML Model to ONNX and deploy - Real Time Object Detection (TinyYOLO)](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb) 
 ## Documentation
 - [ONNX Runtime Python API Documentation](http://aka.ms/onnxruntime-python)
 - [Azure Machine Learning API Documentation](http://aka.ms/aml-docs)
 ## Related Articles
 - [Building and Deploying ONNX Runtime Models](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-build-deploy-onnx)
 - [Azure AI – Making AI Real for Business](https://aka.ms/aml-blog-overview) 
 - [What’s new in Azure Machine Learning](https://aka.ms/aml-blog-whats-new)
 ## License
 Copyright (c) Microsoft Corporation. All rights reserved.  
 Licensed under the MIT License.
 ## Acknowledgements
 These tutorials were developed by Vinitra Swamy and Prasanth Pulavarthi of the Microsoft AI Frameworks team and adapted for presentation at Microsoft Ignite 2018.
--- a/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb
+++ b/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb
@@ -33,7 +33,7 @@
        "To make the best use of your time, make sure you have done the following:\n",
        "\n",
        "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
-        "* Go through the [00.configuration.ipynb](../00.configuration.ipynb) notebook to:\n",
+        "* Go through the [configuration](../../../configuration.ipynb) notebook to:\n",
        "    * install the AML SDK\n",
        "    * create a workspace and its configuration file (config.json)"
      ]
@@ -59,8 +59,9 @@
        "You'll need to run the following commands to use this tutorial:\n",
        "\n",
        "```sh\n",
        "pip install coremltools\n",
        "pip install onnxmltools\n",
        "pip install coremltools  # use this on Linux and Mac\n",
        "pip install git+https://github.com/apple/coremltools  # use this on Windows\n",
        "```"
      ]
    },
@@ -70,7 +71,7 @@
      "source": [
        "## Convert model to ONNX\n",
        "\n",
-        "First we download the CoreML model. We use the CoreML model listed at https://coreml.store/tinyyolo. This may take a few minutes."
+        "First we download the CoreML model. We use the CoreML model from [Matthijs Hollemans's tutorial](https://github.com/hollance/YOLO-CoreML-MPSNNGraph). This may take a few minutes."
      ]
    },
    {
@@ -79,7 +80,10 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "!wget https://s3-us-west-2.amazonaws.com/coreml-models/TinyYOLO.mlmodel"
+        "import urllib.request\n",
        "\n",
        "coreml_model_url = \"https://github.com/hollance/YOLO-CoreML-MPSNNGraph/raw/master/TinyYOLO-CoreML/TinyYOLO-CoreML/TinyYOLO.mlmodel\"\n",
        "urllib.request.urlretrieve(coreml_model_url, filename=\"TinyYOLO.mlmodel\")\n"
      ]
    },
    {
@@ -173,9 +177,9 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "models = ws.models()\n",
+        "models = ws.models\n",
-        "for m in models:\n",
+        "for name, m in models.items():\n",
-        "    print(\"Name:\", m.name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
+        "    print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
      ]
    },
    {
@@ -244,7 +248,7 @@
      "source": [
        "from azureml.core.conda_dependencies import CondaDependencies \n",
        "\n",
-        "myenv = CondaDependencies.create(pip_packages=[\"numpy\",\"onnxruntime\"])\n",
+        "myenv = CondaDependencies.create(pip_packages=[\"numpy\",\"onnxruntime\",\"azureml-core\"])\n",
        "\n",
        "with open(\"myenv.yml\",\"w\") as f:\n",
        "    f.write(myenv.serialize_to_string())"
@@ -405,7 +409,7 @@
  "metadata": {
    "authors": [
      {
-        "name": "onnx"
+        "name": "viswamy"
      }
    ],
    "kernelspec": {
@@ -423,7 +427,7 @@
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
-      "version": "3.5.6"
+      "version": "3.6.5"
    }
  },
  "nbformat": 4,
--- a/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb
+++ b/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb
@@ -12,7 +12,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "# Facial Expression Recognition (Emotion FER+) using ONNX Runtime on Azure ML\n",
+        "# Facial Expression Recognition (FER+) using ONNX Runtime on Azure ML\n",
        "\n",
        "This example shows how to deploy an image classification neural network using the Facial Expression Recognition ([FER](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data)) dataset and Open Neural Network eXchange format ([ONNX](http://aka.ms/onnxdocarticle)) on the Azure Machine Learning platform. This tutorial will show you how to deploy a FER+ model from the [ONNX model zoo](https://github.com/onnx/models), use it to make predictions using ONNX Runtime Inference, and deploy it as a web service in Azure.\n",
        "\n",
@@ -34,7 +34,7 @@
        "## Prerequisites\n",
        "\n",
        "### 1. Install Azure ML SDK and create a new workspace\n",
-        "Please follow [Azure ML configuration notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) to set up your environment.\n",
+        "Please follow [Azure ML configuration notebook](../../../configuration.ipynb) to set up your environment.\n",
        "\n",
        "### 2. Install additional packages needed for this Notebook\n",
        "You need to install the popular plotting library `matplotlib`, the image manipulation library `opencv`, and the `onnx` library in the conda environment where Azure Maching Learning SDK is installed.\n",
@@ -158,7 +158,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "models = ws.models()\n",
+        "models = ws.models\n",
        "for name, m in models.items():\n",
        "    print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
      ]
@@ -197,7 +197,6 @@
      "source": [
        "# for images and plots in this notebook\n",
        "import matplotlib.pyplot as plt  \n",
        "from IPython.display import Image\n",
        "\n",
        "# display images inline\n",
        "%matplotlib inline"
@@ -325,11 +324,7 @@
      "source": [
        "from azureml.core.conda_dependencies import CondaDependencies \n",
        "\n",
-        "myenv = CondaDependencies()\n",
+        "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\"])\n",
        "myenv.add_pip_package(\"numpy\")\n",
        "myenv.add_pip_package(\"azureml-core\")\n",
        "myenv.add_pip_package(\"onnxruntime\")\n",
        "\n",
        "\n",
        "with open(\"myenv.yml\",\"w\") as f:\n",
        "    f.write(myenv.serialize_to_string())"
@@ -359,7 +354,7 @@
        "                                                  tags = {\"demo\": \"onnx\"})\n",
        "\n",
        "\n",
-        "image = ContainerImage.create(name = \"onnxtest\",\n",
+        "image = ContainerImage.create(name = \"onnximage\",\n",
        "                              # this is the model object\n",
        "                              models = [model],\n",
        "                              image_config = image_config,\n",
@@ -485,9 +480,8 @@
        "    \n",
        "    emotion_keys = list(emotion_table.keys())\n",
        "    emotions = []\n",
-        "    for i in range(N):\n",
+        "    for c in range(N):\n",
-        "        emotions.append(emotion_keys[classes[i]])\n",
+        "        emotions.append(emotion_keys[classes[c]])\n",
        "        \n",
        "    return emotions\n",
        "\n",
        "def softmax(x):\n",
@@ -539,9 +533,9 @@
        "# read in 3 testing images from .pb files\n",
        "test_data_size = 3\n",
        "\n",
-        "for i in np.arange(test_data_size):\n",
+        "for num in np.arange(test_data_size):\n",
-        "    input_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(i), 'input_0.pb')\n",
+        "    input_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(num), 'input_0.pb')\n",
-        "    output_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(i), 'output_0.pb')\n",
+        "    output_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(num), 'output_0.pb')\n",
        "    \n",
        "    # convert protobuf tensors to np arrays using the TensorProto reader from ONNX\n",
        "    tensor = onnx.TensorProto()\n",
@@ -555,7 +549,7 @@
        "        tensor.ParseFromString(f.read())\n",
        "    \n",
        "    output_data = numpy_helper.to_array(tensor)\n",
-        "    output_processed = emotion_map(postprocess(output_data))[0]\n",
+        "    output_processed = emotion_map(postprocess(output_data[0]))[0]\n",
        "    test_outputs.append(output_processed)"
      ]
    },
@@ -676,19 +670,19 @@
        "    \"\"\"Convert the input image into grayscale\"\"\"\n",
        "    return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])\n",
        "\n",
-        "def resize_img(img):\n",
+        "def resize_img(img_to_resize):\n",
-        "    \"\"\"Resize image to MNIST model input dimensions\"\"\"\n",
+        "    \"\"\"Resize image to FER+ model input dimensions\"\"\"\n",
-        "    img = cv2.resize(img, dsize=(64, 64), interpolation=cv2.INTER_AREA)\n",
+        "    r_img = cv2.resize(img_to_resize, dsize=(64, 64), interpolation=cv2.INTER_AREA)\n",
-        "    img.resize((1, 1, 64, 64))\n",
+        "    r_img.resize((1, 1, 64, 64))\n",
-        "    return img\n",
+        "    return r_img\n",
        "\n",
-        "def preprocess(img):\n",
+        "def preprocess(img_to_preprocess):\n",
        "    \"\"\"Resize input images and convert them to grayscale.\"\"\"\n",
-        "    if img.shape == (64, 64):\n",
+        "    if img_to_preprocess.shape == (64, 64):\n",
-        "        img.resize((1, 1, 64, 64))\n",
+        "        img_to_preprocess.resize((1, 1, 64, 64))\n",
-        "        return img\n",
+        "        return img_to_preprocess\n",
        "    \n",
-        "    grayscale = rgb2gray(img)\n",
+        "    grayscale = rgb2gray(img_to_preprocess)\n",
        "    processed_img = resize_img(grayscale)\n",
        "    return processed_img"
      ]
@@ -705,7 +699,9 @@
        "# Any PNG or JPG image file should work\n",
        "# Make sure to include the entire path with // instead of /\n",
        "\n",
-        "# e.g. your_test_image = \"C://Users//vinitra.swamy//Pictures//emotion_test_images//img_1.png\"\n",
+        "# e.g. your_test_image = \"C:/Users/vinitra.swamy/Pictures/face.png\"\n",
        "\n",
        "your_test_image = \"<path to file>\"\n",
        "\n",
        "import matplotlib.image as mpimg\n",
        "\n",
@@ -735,7 +731,7 @@
        "        r = json.loads(aci_service.run(input_data))\n",
        "        result = r['result'][0]\n",
        "        time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n",
-        "    except Exception as e:\n",
+        "    except KeyError as e:\n",
        "        print(str(e))\n",
        "\n",
        "    plt.figure(figsize = (16, 6))\n",
@@ -759,7 +755,7 @@
      "source": [
        "# remember to delete your service after you are done using it!\n",
        "\n",
-        "aci_service.delete()"
+        "# aci_service.delete()"
      ]
    },
    {
@@ -803,7 +799,7 @@
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
+      "version": "3.6.5"
    },
    "msauthor": "vinitra.swamy"
  },
--- a/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb
+++ b/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb
@@ -34,7 +34,7 @@
        "## Prerequisites\n",
        "\n",
        "### 1. Install Azure ML SDK and create a new workspace\n",
-        "Please follow [Azure ML configuration notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) to set up your environment.\n",
+        "Please follow [Azure ML configuration notebook](../../../configuration.ipynb) to set up your environment.\n",
        "\n",
        "### 2. Install additional packages needed for this tutorial notebook\n",
        "You need to install the popular plotting library `matplotlib`, the image manipulation library `opencv`, and the `onnx` library in the conda environment where Azure Maching Learning SDK is installed. \n",
@@ -165,7 +165,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "models = ws.models()\n",
+        "models = ws.models\n",
        "for name, m in models.items():\n",
        "    print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
      ]
@@ -255,25 +255,36 @@
        "    input_name = session.get_inputs()[0].name\n",
        "    output_name = session.get_outputs()[0].name \n",
        "    \n",
        "\n",
        "def preprocess(input_data_json):\n",
        "    # convert the JSON data into the tensor input\n",
        "    return np.array(json.loads(input_data_json)['data']).astype('float32')\n",
        "\n",
        "def postprocess(result):\n",
        "    # We use argmax to pick the highest confidence label\n",
        "    return int(np.argmax(np.array(result).squeeze(), axis=0))\n",
        "    \n",
        "def run(input_data):\n",
        "    '''Purpose: evaluate test input in Azure Cloud using onnxruntime.\n",
        "        We will call the run function later from our Jupyter Notebook \n",
        "        so our azure service can evaluate our model input in the cloud. '''\n",
        "\n",
        "    try:\n",
        "        # load in our data, convert to readable format\n",
-        "        data = np.array(json.loads(input_data)['data']).astype('float32')\n",
+        "        data = preprocess(input_data)\n",
        "        \n",
        "        # start timer\n",
        "        start = time.time()\n",
-        "        r = session.run([output_name], {input_name: data})[0]\n",
+        "        \n",
        "        r = session.run([output_name], {input_name: data})\n",
        "        \n",
        "        #end timer\n",
        "        end = time.time()\n",
-        "        result = choose_class(r[0])\n",
+        "        \n",
-        "        result_dict = {\"result\": [result],\n",
+        "        result = postprocess(r)\n",
-        "                      \"time_in_sec\": [end - start]}\n",
+        "        result_dict = {\"result\": result,\n",
        "                      \"time_in_sec\": end - start}\n",
        "    except Exception as e:\n",
        "        result_dict = {\"error\": str(e)}\n",
        "    \n",
-        "    return json.dumps(result_dict)\n",
+        "    return result_dict\n",
        "\n",
        "def choose_class(result_prob):\n",
        "    \"\"\"We use argmax to determine the right label to choose from our output\"\"\"\n",
@@ -297,11 +308,7 @@
      "source": [
        "from azureml.core.conda_dependencies import CondaDependencies \n",
        "\n",
-        "myenv = CondaDependencies()\n",
+        "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\"])\n",
        "myenv.add_pip_package(\"numpy\")\n",
        "myenv.add_pip_package(\"azureml-core\")\n",
        "myenv.add_pip_package(\"onnxruntime\")\n",
        "\n",
        "\n",
        "with open(\"myenv.yml\",\"w\") as f:\n",
        "    f.write(myenv.serialize_to_string())"
@@ -315,16 +322,6 @@
        "This step will likely take a few minutes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.image import ContainerImage\n",
        "help(ContainerImage.image_configuration)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -340,7 +337,7 @@
        "                                                  tags = {\"demo\": \"onnx\"}) \n",
        "\n",
        "\n",
-        "image = ContainerImage.create(name = \"onnxtest\",\n",
+        "image = ContainerImage.create(name = \"onnximage\",\n",
        "                              # this is the model object\n",
        "                              models = [model],\n",
        "                              image_config = image_config,\n",
@@ -403,7 +400,7 @@
      "source": [
        "from azureml.core.webservice import Webservice\n",
        "\n",
-        "aci_service_name = 'onnx-demo-mnist20'\n",
+        "aci_service_name = 'onnx-demo-mnist'\n",
        "print(\"Service\", aci_service_name)\n",
        "\n",
        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
@@ -437,7 +434,16 @@
        "\n",
        "If you've made it this far, you've deployed a working VM with a handwritten digit classifier running in the cloud using Azure ML. Congratulations!\n",
        "\n",
-        "Let's see how well our model deals with our test images."
+        "You can get the URL for the webservice with the code below. Let's now see how well our model deals with our test images."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print(aci_service.scoring_uri)"
      ]
    },
    {
@@ -558,14 +564,14 @@
        "    input_data = json.dumps({'data': test_inputs[i].tolist()})\n",
        "    \n",
        "    # predict using the deployed model\n",
-        "    r = json.loads(aci_service.run(input_data))\n",
+        "    r = aci_service.run(input_data)\n",
        "    \n",
        "    if \"error\" in r:\n",
        "        print(r['error'])\n",
        "        break\n",
        "        \n",
-        "    result = r['result'][0]\n",
+        "    result = r['result']\n",
-        "    time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n",
+        "    time_ms = np.round(r['time_in_sec'] * 1000, 2)\n",
        "    \n",
        "    ground_truth = int(np.argmax(test_outputs[i]))\n",
        "    \n",
@@ -615,19 +621,19 @@
        "    \"\"\"Convert the input image into grayscale\"\"\"\n",
        "    return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])\n",
        "\n",
-        "def resize_img(img):\n",
+        "def resize_img(img_to_resize):\n",
        "    \"\"\"Resize image to MNIST model input dimensions\"\"\"\n",
-        "    img = cv2.resize(img, dsize=(28, 28), interpolation=cv2.INTER_AREA)\n",
+        "    r_img = cv2.resize(img_to_resize, dsize=(28, 28), interpolation=cv2.INTER_AREA)\n",
-        "    img.resize((1, 1, 28, 28))\n",
+        "    r_img.resize((1, 1, 28, 28))\n",
-        "    return img\n",
+        "    return r_img\n",
        "\n",
-        "def preprocess(img):\n",
+        "def preprocess(img_to_preprocess):\n",
        "    \"\"\"Resize input images and convert them to grayscale.\"\"\"\n",
-        "    if img.shape == (28, 28):\n",
+        "    if img_to_preprocess.shape == (28, 28):\n",
-        "        img.resize((1, 1, 28, 28))\n",
+        "        img_to_preprocess.resize((1, 1, 28, 28))\n",
-        "        return img\n",
+        "        return img_to_preprocess\n",
        "    \n",
-        "    grayscale = rgb2gray(img)\n",
+        "    grayscale = rgb2gray(img_to_preprocess)\n",
        "    processed_img = resize_img(grayscale)\n",
        "    return processed_img"
      ]
@@ -643,6 +649,8 @@
        "\n",
        "# Any PNG or JPG image file should work\n",
        "\n",
        "your_test_image = \"<path to file>\"\n",
        "\n",
        "# e.g. your_test_image = \"C:/Users/vinitra.swamy/Pictures/handwritten_digit.png\"\n",
        "\n",
        "import matplotlib.image as mpimg\n",
@@ -670,10 +678,10 @@
        "    input_data = json.dumps({'data': img.tolist()})\n",
        "\n",
        "    try:\n",
-        "        r = json.loads(aci_service.run(input_data))\n",
+        "        r = aci_service.run(input_data)\n",
-        "        result = r['result'][0]\n",
+        "        result = r['result']\n",
-        "        time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n",
+        "        time_ms = np.round(r['time_in_sec'] * 1000, 2)\n",
-        "    except Exception as e:\n",
+        "    except KeyError as e:\n",
        "        print(str(e))\n",
        "\n",
        "    plt.figure(figsize = (16, 6))\n",
@@ -752,7 +760,7 @@
      "source": [
        "# remember to delete your service after you are done using it!\n",
        "\n",
-        "aci_service.delete()"
+        "# aci_service.delete()"
      ]
    },
    {
@@ -795,7 +803,7 @@
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
+      "version": "3.6.5"
    },
    "msauthor": "vinitra.swamy"
  },
--- a/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb
+++ b/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb
@@ -33,7 +33,7 @@
        "To make the best use of your time, make sure you have done the following:\n",
        "\n",
        "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
-        "* Go through the [00.configuration.ipynb](../00.configuration.ipynb) notebook to:\n",
+        "* Go through the [configuration notebook](../../../configuration.ipynb) to:\n",
        "    * install the AML SDK\n",
        "    * create a workspace and its configuration file (config.json)"
      ]
@@ -56,11 +56,21 @@
      "source": [
        "#### Download pre-trained ONNX model from ONNX Model Zoo.\n",
        "\n",
-        "Download the [ResNet50v2 model and test data](https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.tar.gz) and place it in the same folder as this tutorial notebook. You can unzip the file through the following line of code.\n",
+        "Download the [ResNet50v2 model and test data](https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.tar.gz) and extract it in the same folder as this tutorial notebook.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import urllib.request\n",
        "\n",
-        "```sh\n",
+        "onnx_model_url = \"https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.tar.gz\"\n",
-        "(myenv) $ tar xvzf resnet50v2.tar.gz\n",
+        "urllib.request.urlretrieve(onnx_model_url, filename=\"resnet50v2.tar.gz\")\n",
-        "```"
+        "\n",
        "!tar xvzf resnet50v2.tar.gz"
      ]
    },
    {
@@ -130,9 +140,9 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "models = ws.models()\n",
+        "models = ws.models\n",
-        "for m in models:\n",
+        "for name, m in models.items():\n",
-        "    print(\"Name:\", m.name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
+        "    print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
      ]
    },
    {
@@ -222,7 +232,7 @@
      "source": [
        "from azureml.core.conda_dependencies import CondaDependencies \n",
        "\n",
-        "myenv = CondaDependencies.create(pip_packages=[\"numpy\",\"onnxruntime\"])\n",
+        "myenv = CondaDependencies.create(pip_packages=[\"numpy\",\"onnxruntime\",\"azureml-core\"])\n",
        "\n",
        "with open(\"myenv.yml\",\"w\") as f:\n",
        "    f.write(myenv.serialize_to_string())"
@@ -383,7 +393,7 @@
  "metadata": {
    "authors": [
      {
-        "name": "onnx"
+        "name": "viswamy"
      }
    ],
    "kernelspec": {
@@ -401,7 +411,7 @@
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
-      "version": "3.5.6"
+      "version": "3.6.5"
    }
  },
  "nbformat": 4,
--- a/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb
+++ b/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb
@@ -122,9 +122,11 @@
        "        data = json.loads(raw_data)['data']\n",
        "        data = numpy.array(data)\n",
        "        result = model.predict(data)\n",
        "        # you can return any data type as long as it is JSON-serializable\n",
        "        return result.tolist()\n",
        "    except Exception as e:\n",
-        "        result = str(e)\n",
+        "        error = str(e)\n",
-        "    return json.dumps({\"result\": result.tolist()})"
+        "        return error"
      ]
    },
    {
@@ -222,7 +224,8 @@
        "\n",
        "create_name='my-existing-aks' \n",
        "# Create the cluster\n",
-        "aks_target = AksCompute.attach(workspace=ws, name=create_name, resource_id=resource_id)\n",
+        "attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
        "aks_target = ComputeTarget.attach(workspace=ws, name=create_name, attach_configuration=attach_config)\n",
        "# Wait for the operation to complete\n",
        "aks_target.wait_for_completion(True)\n",
        "'''"
@@ -332,7 +335,7 @@
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
-      "version": "3.6.5"
+      "version": "3.6.6"
    }
  },
  "nbformat": 4,
--- a/01.getting-started/12.enable-data-collection-for-models-in-aks/sklearn_regression_model.pkl
+++ b/01.getting-started/12.enable-data-collection-for-models-in-aks/sklearn_regression_model.pkl
--- a/how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb
+++ b/how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb
@@ -13,7 +13,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## 10. Register Model, Create Image and Deploy Service\n",
+        "## 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",
@@ -24,9 +24,9 @@
        " 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",
+        " * This notebook requires you to first complete [train-within-notebook](../../training/train-within-notebook/train-within-notebook.ipynb) example\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.  "
+        "The train-within-notebook example 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.  "
      ]
    },
    {
@@ -34,7 +34,7 @@
      "metadata": {},
      "source": [
        "## Prerequisites\n",
-        "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
+        "Make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
      ]
    },
    {
@@ -192,9 +192,11 @@
        "        data = json.loads(raw_data)['data']\n",
        "        data = numpy.array(data)\n",
        "        result = model.predict(data)\n",
        "        # you can return any datatype as long as it is JSON-serializable\n",
        "        return result.tolist()\n",
        "    except Exception as e:\n",
-        "        result = str(e)\n",
+        "        error = str(e)\n",
-        "    return json.dumps({\"result\": result.tolist()})"
+        "        return error"
      ]
    },
    {
@@ -239,7 +241,8 @@
        "                                 description = \"Image with ridge regression model\")\n",
        "\n",
        "image = Image.create(name = \"myimage1\",\n",
-        "                     # this is the model object \n",
+        "                     # this is the model object. note you can pass in 0-n models via this list-type parameter\n",
        "                     # in case you need to reference multiple models, or none at all, in your scoring script.\n",
        "                     models = [model],\n",
        "                     image_config = image_config, \n",
        "                     workspace = ws)"
@@ -387,13 +390,6 @@
      "source": [
        "aci_service.delete()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
@@ -417,7 +413,7 @@
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
-      "version": "3.6.5"
+      "version": "3.6.6"
    }
  },
  "nbformat": 4,
--- a/how-to-use-azureml/deployment/register-model-create-image-deploy-service/sklearn_regression_model.pkl
+++ b/how-to-use-azureml/deployment/register-model-create-image-deploy-service/sklearn_regression_model.pkl
--- a/how-to-use-azureml/machine-learning-pipelines/README.md
+++ b/how-to-use-azureml/machine-learning-pipelines/README.md
@@ -0,0 +1,51 @@
 # Azure Machine Learning Pipeline
 ## Overview
 The [Azure Machine Learning Pipelines](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines) enables data scientists to create and manage multiple simple and complex workflows concurrently. A typical pipeline would have multiple tasks to prepare data, train, deploy and evaluate models. Individual steps in the pipeline can make use of diverse compute options (for example: CPU for data preparation and GPU for training) and languages. 
 The Python-based Azure Machine Learning Pipeline SDK provides interfaces to work with Azure Machine Learning Pipelines. To get started quickly, the SDK includes imperative constructs for sequencing and parallelization of steps. With the use of declarative data dependencies, optimized execution of the tasks can be achieved. The SDK can be easily used from Jupyter Notebook or any other preferred IDE. The SDK includes a framework of pre-built modules for common tasks such as data transfer and compute provisioning.
 Data management and reuse across pipelines and pipeline runs is simplified using named and strictly versioned data sources and named inputs and outputs for processing tasks. Pipelines enable collaboration across teams of data scientists by recording all intermediate tasks and data.
 Learn more about how to [create your first machine learning pipeline](https://docs.microsoft.com/azure/machine-learning/service/how-to-create-your-first-pipeline).
 ### Why build pipelines?
 With pipelines, you can optimize your workflow with simplicity, speed, portability, and reuse. When building pipelines with Azure Machine Learning, you can focus on what you know best — machine learning — rather than infrastructure.
 Using distinct steps makes it possible to rerun only the steps you need as you tweak and test your workflow. Once the pipeline is designed, there is often more fine-tuning around the training loop of the pipeline. When you rerun a pipeline, the execution jumps to the steps that need to be rerun, such as an updated training script, and skips what hasn't changed. The same paradigm applies to unchanged scripts and metadata.
 With Azure Machine Learning, you can use distinct toolkits and frameworks for each step in your pipeline. Azure coordinates between the various compute targets you use so that your intermediate data can be shared with the downstream compute targets easily.
 ![MLLifecycle](aml-pipelines-concept.png)
 ### Azure Machine Learning Pipelines Features
 Azure Machine Learning Pipelines optimize for simplicity, speed, and efficiency. The following key concepts make it possible for a data scientist to focus on ML rather than infrastructure.
 **Unattended execution**: Schedule a few scripts to run in parallel or in sequence in a reliable and unattended manner. Since data prep and modeling can last days or weeks, you can now focus on other tasks while your pipeline is running.
 **Mixed and diverse compute**: Use multiple pipelines that are reliably coordinated across heterogeneous and scalable computes and storages. Individual pipeline steps can be run on different compute targets, such as HDInsight, GPU Data Science VMs, and Databricks, to make efficient use of available compute options.
 **Reusability**: Pipelines can be templatized for specific scenarios such as retraining and batch scoring. They can be triggered from external systems via simple REST calls.
 **Tracking and versioning**: Instead of manually tracking data and result paths as you iterate, use the pipelines SDK to explicitly name and version your data sources, inputs, and outputs as well as manage scripts and data separately for increased productivity.
 ### Notebooks 
 In this directory, there are two types of notebooks: 
 * The first type of notebooks will introduce you to core Azure Machine Learning Pipelines features. These notebooks below belong in this category, and are designed to go in sequence; they're all located in the "intro-to-pipelines" folder:
 1. [aml-pipelines-getting-started.ipynb](https://aka.ms/pl-get-started)
 2. [aml-pipelines-with-data-dependency-steps.ipynb](https://aka.ms/pl-data-dep)
 3. [aml-pipelines-publish-and-run-using-rest-endpoint.ipynb](https://aka.ms/pl-pub-rep)
 4. [aml-pipelines-data-transfer.ipynb](https://aka.ms/pl-data-trans)
 5. [aml-pipelines-use-databricks-as-compute-target.ipynb](https://aka.ms/pl-databricks)
 6. [aml-pipelines-use-adla-as-compute-target.ipynb](https://aka.ms/pl-adla)
 * The second type of notebooks illustrate more sophisticated scenarios, and are independent of each other. These notebooks include:
 1. [pipeline-batch-scoring.ipynb](https://aka.ms/pl-batch-score)
 2. [pipeline-style-transfer.ipynb](https://aka.ms/pl-style-trans)
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/20news.pkl
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/20news.pkl
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb
@@ -0,0 +1,328 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Azure Machine Learning Pipeline with DataTranferStep\n",
        "This notebook is used to demonstrate the use of DataTranferStep in Azure Machine Learning Pipeline.\n",
        "\n",
        "In certain cases, you will need to transfer data from one data location to another. For example, your data may be in Files storage and you may want to move it to Blob storage. Or, if your data is in an ADLS account and you want to make it available in the Blob storage. The built-in **DataTransferStep** class helps you transfer data in these situations.\n",
        "\n",
        "The below example shows how to move data in an ADLS account to Blob storage."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Azure Machine Learning and Pipeline SDK-specific imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "import azureml.core\n",
        "from azureml.core.compute import ComputeTarget, DataFactoryCompute\n",
        "from azureml.exceptions import ComputeTargetException\n",
        "from azureml.core import Workspace, Experiment\n",
        "from azureml.pipeline.core import Pipeline\n",
        "from azureml.core.datastore import Datastore\n",
        "from azureml.data.data_reference import DataReference\n",
        "from azureml.pipeline.steps import DataTransferStep\n",
        "\n",
        "# Check core SDK version number\n",
        "print(\"SDK version:\", azureml.core.VERSION)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Initialize Workspace\n",
        "\n",
        "Initialize a workspace object from persisted configuration. Make sure the config file is present at .\\config.json\n",
        "\n",
        "If you don't have a config.json file, please go through the configuration Notebook located here:\n",
        "https://github.com/Azure/MachineLearningNotebooks. \n",
        "\n",
        "This sets you up with a working config file that has information on your workspace, subscription id, etc. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "create workspace"
        ]
      },
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Register Datastores\n",
        "\n",
        "In the code cell below, you will need to fill in the appropriate values for the workspace name, datastore name, subscription id, resource group, store name, tenant id, client id, and client secret that are associated with your ADLS datastore. \n",
        "\n",
        "For background on registering your data store, consult this article:\n",
        "\n",
        "https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-service-to-service-authenticate-using-active-directory"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from msrest.exceptions import HttpOperationError\n",
        "\n",
        "datastore_name='MyAdlsDatastore'\n",
        "subscription_id=os.getenv(\"ADL_SUBSCRIPTION_62\", \"<my-subscription-id>\") # subscription id of ADLS account\n",
        "resource_group=os.getenv(\"ADL_RESOURCE_GROUP_62\", \"<my-resource-group>\") # resource group of ADLS account\n",
        "store_name=os.getenv(\"ADL_STORENAME_62\", \"<my-datastore-name>\") # ADLS account name\n",
        "tenant_id=os.getenv(\"ADL_TENANT_62\", \"<my-tenant-id>\") # tenant id of service principal\n",
        "client_id=os.getenv(\"ADL_CLIENTID_62\", \"<my-client-id>\") # client id of service principal\n",
        "client_secret=os.getenv(\"ADL_CLIENT_SECRET_62\", \"<my-client-secret>\") # the secret of service principal\n",
        "\n",
        "try:\n",
        "    adls_datastore = Datastore.get(ws, datastore_name)\n",
        "    print(\"found datastore with name: %s\" % datastore_name)\n",
        "except HttpOperationError:\n",
        "    adls_datastore = Datastore.register_azure_data_lake(\n",
        "        workspace=ws,\n",
        "        datastore_name=datastore_name,\n",
        "        subscription_id=subscription_id, # subscription id of ADLS account\n",
        "        resource_group=resource_group, # resource group of ADLS account\n",
        "        store_name=store_name, # ADLS account name\n",
        "        tenant_id=tenant_id, # tenant id of service principal\n",
        "        client_id=client_id, # client id of service principal\n",
        "        client_secret=client_secret) # the secret of service principal\n",
        "    print(\"registered datastore with name: %s\" % datastore_name)\n",
        "\n",
        "\n",
        "\n",
        "blob_datastore_name='MyBlobDatastore'\n",
        "account_name=os.getenv(\"BLOB_ACCOUNTNAME_62\", \"<my-account-name>\") # Storage account name\n",
        "container_name=os.getenv(\"BLOB_CONTAINER_62\", \"<my-container-name>\") # Name of Azure blob container\n",
        "account_key=os.getenv(\"BLOB_ACCOUNT_KEY_62\", \"<my-account-key>\") # Storage account key\n",
        "\n",
        "try:\n",
        "    blob_datastore = Datastore.get(ws, blob_datastore_name)\n",
        "    print(\"found blob datastore with name: %s\" % blob_datastore_name)\n",
        "except HttpOperationError:\n",
        "    blob_datastore = Datastore.register_azure_blob_container(\n",
        "        workspace=ws,\n",
        "        datastore_name=blob_datastore_name,\n",
        "        account_name=account_name, # Storage account name\n",
        "        container_name=container_name, # Name of Azure blob container\n",
        "        account_key=account_key) # Storage account key\"\n",
        "    print(\"registered blob datastore with name: %s\" % blob_datastore_name)\n",
        "\n",
        "# CLI:\n",
        "# az ml datastore register-blob -n <datastore-name> -a <account-name> -c <container-name> -k <account-key> [-t <sas-token>]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create DataReferences"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "adls_datastore = Datastore(workspace=ws, name=\"MyAdlsDatastore\")\n",
        "\n",
        "# adls\n",
        "adls_data_ref = DataReference(\n",
        "    datastore=adls_datastore,\n",
        "    data_reference_name=\"adls_test_data\",\n",
        "    path_on_datastore=\"testdata\")\n",
        "\n",
        "blob_datastore = Datastore(workspace=ws, name=\"MyBlobDatastore\")\n",
        "\n",
        "# blob data\n",
        "blob_data_ref = DataReference(\n",
        "    datastore=blob_datastore,\n",
        "    data_reference_name=\"blob_test_data\",\n",
        "    path_on_datastore=\"testdata\")\n",
        "\n",
        "print(\"obtained adls, blob data references\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup Data Factory Account"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "data_factory_name = 'adftest'\n",
        "\n",
        "def get_or_create_data_factory(workspace, factory_name):\n",
        "    try:\n",
        "        return DataFactoryCompute(workspace, factory_name)\n",
        "    except ComputeTargetException as e:\n",
        "        if 'ComputeTargetNotFound' in e.message:\n",
        "            print('Data factory not found, creating...')\n",
        "            provisioning_config = DataFactoryCompute.provisioning_configuration()\n",
        "            data_factory = ComputeTarget.create(workspace, factory_name, provisioning_config)\n",
        "            data_factory.wait_for_completion()\n",
        "            return data_factory\n",
        "        else:\n",
        "            raise e\n",
        "            \n",
        "data_factory_compute = get_or_create_data_factory(ws, data_factory_name)\n",
        "\n",
        "print(\"setup data factory account complete\")\n",
        "\n",
        "# CLI:\n",
        "# Create: az ml computetarget setup datafactory -n <name>\n",
        "# BYOC: az ml computetarget attach datafactory -n <name> -i <resource-id>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create a DataTransferStep"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**DataTransferStep** is used to transfer data between Azure Blob, Azure Data Lake Store, and Azure SQL database.\n",
        "\n",
        "- **name:** Name of module\n",
        "- **source_data_reference:** Input connection that serves as source of data transfer operation.\n",
        "- **destination_data_reference:** Input connection that serves as destination of data transfer operation.\n",
        "- **compute_target:** Azure Data Factory to use for transferring data.\n",
        "- **allow_reuse:** Whether the step should reuse results of previous DataTransferStep when run with same inputs. Set as False to force data to be transferred again.\n",
        "\n",
        "Optional arguments to explicitly specify whether a path corresponds to a file or a directory. These are useful when storage contains both file and directory with the same name or when creating a new destination path.\n",
        "\n",
        "- **source_reference_type:** An optional string specifying the type of source_data_reference. Possible values include: 'file', 'directory'. When not specified, we use the type of existing path or directory if it's a new path.\n",
        "- **destination_reference_type:** An optional string specifying the type of destination_data_reference. Possible values include: 'file', 'directory'. When not specified, we use the type of existing path or directory if it's a new path."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "transfer_adls_to_blob = DataTransferStep(\n",
        "    name=\"transfer_adls_to_blob\",\n",
        "    source_data_reference=adls_data_ref,\n",
        "    destination_data_reference=blob_data_ref,\n",
        "    compute_target=data_factory_compute)\n",
        "\n",
        "print(\"data transfer step created\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Build and Submit the Experiment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "pipeline = Pipeline(\n",
        "    description=\"data_transfer_101\",\n",
        "    workspace=ws,\n",
        "    steps=[transfer_adls_to_blob])\n",
        "\n",
        "pipeline_run = Experiment(ws, \"Data_Transfer_example\").submit(pipeline)\n",
        "pipeline_run.wait_for_completion()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### View Run Details"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(pipeline_run).show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Next: Databricks as a Compute Target\n",
        "To use Databricks as a compute target from Azure Machine Learning Pipeline, a DatabricksStep is used. This [notebook](./aml-pipelines-use-databricks-as-compute-target.ipynb) demonstrates the use of a DatabricksStep in an Azure Machine Learning Pipeline."
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "diray"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb
@@ -0,0 +1,602 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Azure Machine Learning Pipelines: Getting Started\n",
        "\n",
        "## Overview\n",
        "\n",
        "Read [Azure Machine Learning Pipelines](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines) overview, or the [readme article](../README.md) on Azure Machine Learning Pipelines to get more information.\n",
        " \n",
        "\n",
        "This Notebook shows basic construction of a **pipeline** that runs jobs unattended in different compute clusters. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Prerequisites and Azure Machine Learning Basics\n",
        "Make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Azure Machine Learning Imports\n",
        "\n",
        "In this first code cell, we import key Azure Machine Learning modules that we will use below. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import azureml.core\n",
        "from azureml.core import Workspace, Experiment, Datastore\n",
        "from azureml.core.compute import AmlCompute\n",
        "from azureml.core.compute import ComputeTarget\n",
        "from azureml.widgets import RunDetails\n",
        "\n",
        "# Check core SDK version number\n",
        "print(\"SDK version:\", azureml.core.VERSION)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Pipeline-specific SDK imports\n",
        "\n",
        "Here, we import key pipeline modules, whose use will be illustrated in the examples below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.pipeline.core import Pipeline\n",
        "from azureml.pipeline.steps import PythonScriptStep\n",
        "\n",
        "print(\"Pipeline SDK-specific imports completed\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Initialize Workspace\n",
        "\n",
        "Initialize a [workspace](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace(class%29) object from persisted configuration."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "create workspace"
        ]
      },
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')\n",
        "\n",
        "# Default datastore (Azure file storage)\n",
        "def_file_store = ws.get_default_datastore() \n",
        "# The above call is equivalent to Datastore(ws, \"workspacefilestore\") or simply Datastore(ws)\n",
        "print(\"Default datastore's name: {}\".format(def_file_store.name))\n",
        "\n",
        "# Blob storage associated with the workspace\n",
        "# The following call GETS the Azure Blob Store associated with your workspace.\n",
        "# Note that workspaceblobstore is **the name of this store and CANNOT BE CHANGED and must be used as is** \n",
        "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
        "print(\"Blobstore's name: {}\".format(def_blob_store.name))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# project folder\n",
        "project_folder = '.'\n",
        "    \n",
        "print('Sample projects will be created in {}.'.format(project_folder))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Required data and script files for the the tutorial\n",
        "Sample files required to finish this tutorial are already copied to the project folder specified above. Even though the .py provided in the samples don't have much \"ML work,\" as a data scientist, you will work on this extensively as part of your work. To complete this tutorial, the contents of these files are not very important. The one-line files are for demostration purpose only."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Datastore concepts\n",
        "A [Datastore](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore(class) is a place where data can be stored that is then made accessible to a compute either by means of mounting or copying the data to the compute target. \n",
        "\n",
        "A Datastore can either be backed by an Azure File Storage (default) or by an Azure Blob Storage.\n",
        "\n",
        "In this next step, we will upload the training and test set into the workspace's default storage (File storage), and another piece of data to Azure Blob Storage. When to use [Azure Blobs](https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction), [Azure Files](https://docs.microsoft.com/en-us/azure/storage/files/storage-files-introduction), or [Azure Disks](https://docs.microsoft.com/en-us/azure/virtual-machines/linux/managed-disks-overview) is [detailed here](https://docs.microsoft.com/en-us/azure/storage/common/storage-decide-blobs-files-disks).\n",
        "\n",
        "**Please take good note of the concept of the datastore.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Upload data to default datastore\n",
        "Default datastore on workspace is the Azure  File storage. The workspace has a Blob storage associated with it as well. Let's upload a file to each of these storages."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# get_default_datastore() gets the default Azure File Store associated with your workspace.\n",
        "# Here we are reusing the def_file_store object we obtained earlier\n",
        "\n",
        "# target_path is the directory at the destination\n",
        "def_file_store.upload_files(['./20news.pkl'], \n",
        "                                  target_path = '20newsgroups', \n",
        "                                  overwrite = True, \n",
        "                                  show_progress = True)\n",
        "\n",
        "# Here we are reusing the def_blob_store we created earlier\n",
        "def_blob_store.upload_files([\"./20news.pkl\"], target_path=\"20newsgroups\", overwrite=True)\n",
        "\n",
        "print(\"Upload calls completed\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### (Optional) See your files using Azure Portal\n",
        "Once you successfully uploaded the files, you can browse to them (or upload more files) using [Azure Portal](https://portal.azure.com). At the portal, make sure you have selected **AzureML Nursery** as your subscription (click *Resource Groups* and then select the subscription). Then look for your **Machine Learning Workspace** (it has your *alias* as the name). It has a link to your storage. Click on the storage link. It will take you to a page where you can see [Blobs](https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction), [Files](https://docs.microsoft.com/en-us/azure/storage/files/storage-files-introduction), [Tables](https://docs.microsoft.com/en-us/azure/storage/tables/table-storage-overview), and [Queues](https://docs.microsoft.com/en-us/azure/storage/queues/storage-queues-introduction). We have just uploaded a file to the Blob storage and another one to the File storage. You should be able to see both of these files in their respective locations. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Compute Targets\n",
        "A compute target specifies where to execute your program such as a remote Docker on a VM, or a cluster. A compute target needs to be addressable and accessible by you.\n",
        "\n",
        "**You need at least one compute target to send your payload to. We are planning to use Azure Machine Learning Compute exclusively for this tutorial for all steps. However in some cases you may require multiple compute targets as some steps may run in one compute target like Azure Machine Learning Compute, and some other steps in the same pipeline could run in a different compute target.**\n",
        "\n",
        "*The example belows show creating/retrieving/attaching to an Azure Machine Learning Compute instance.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### List of Compute Targets on the workspace"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "cts = ws.compute_targets\n",
        "for ct in cts:\n",
        "    print(ct)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Retrieve or create a Azure Machine Learning compute\n",
        "Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's create a new Azure Machine Learning Compute in the current workspace, if it doesn't already exist. We will then run the training script on this compute target.\n",
        "\n",
        "If we could not find the compute with the given name in the previous cell, then we will create a new compute here. We will create an Azure Machine Learning Compute containing **STANDARD_D2_V2 CPU VMs**. This process is broken down into the following steps:\n",
        "\n",
        "1. Create the configuration\n",
        "2. Create the Azure Machine Learning compute\n",
        "\n",
        "**This process will take about 3 minutes and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "aml_compute_target = \"aml-compute\"\n",
        "try:\n",
        "    aml_compute = AmlCompute(ws, aml_compute_target)\n",
        "    print(\"found existing compute target.\")\n",
        "except ComputeTargetException:\n",
        "    print(\"creating new compute target\")\n",
        "    \n",
        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n",
        "                                                                min_nodes = 1, \n",
        "                                                                max_nodes = 4)    \n",
        "    aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n",
        "    aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
        "    \n",
        "print(\"Azure Machine Learning Compute attached\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# For a more detailed view of current Azure Machine Learning Compute status, use get_status()\n",
        "# example: un-comment the following line.\n",
        "# print(aml_compute.get_status().serialize())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Wait for this call to finish before proceeding (you will see the asterisk turning to a number).**\n",
        "\n",
        "Now that you have created the compute target, let's see what the workspace's compute_targets() function returns. You should now see one entry named 'amlcompute' of type AmlCompute."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Now that we have completed learning the basics of Azure Machine Learning (AML), let's go ahead and start understanding the Pipeline concepts.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Creating a Step in a Pipeline\n",
        "A Step is a unit of execution. Step typically needs a target of execution (compute target), a script to execute, and may require script arguments and inputs, and can produce outputs. The step also could take a number of other parameters. Azure Machine Learning Pipelines provides the following built-in Steps:\n",
        "\n",
        "- [**PythonScriptStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.python_script_step.pythonscriptstep?view=azure-ml-py): Add a step to run a Python script in a Pipeline.\n",
        "- [**AdlaStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.adla_step.adlastep?view=azure-ml-py): Adds a step to run U-SQL script using Azure Data Lake Analytics.\n",
        "- [**DataTransferStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.data_transfer_step.datatransferstep?view=azure-ml-py): Transfers data between Azure Blob and Data Lake accounts.\n",
        "- [**DatabricksStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep?view=azure-ml-py): Adds a DataBricks notebook as a step in a Pipeline.\n",
        "- [**HyperDriveStep**](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.hyper_drive_step.hyperdrivestep?view=azure-ml-py): Creates a Hyper Drive step for Hyper Parameter Tuning in a Pipeline.\n",
        "\n",
        "The following code will create a PythonScriptStep to be executed in the Azure Machine Learning Compute we created above using train.py, one of the files already made available in the project folder.\n",
        "\n",
        "A **PythonScriptStep** is a basic, built-in step to run a Python Script on a compute target. It takes a script name and optionally other parameters like arguments for the script, compute target, inputs and outputs. If no compute target is specified, default compute target for the workspace is used."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Uses default values for PythonScriptStep construct.\n",
        "\n",
        "# Syntax\n",
        "# PythonScriptStep(\n",
        "#     script_name, \n",
        "#     name=None, \n",
        "#     arguments=None, \n",
        "#     compute_target=None, \n",
        "#     runconfig=None, \n",
        "#     inputs=None, \n",
        "#     outputs=None, \n",
        "#     params=None, \n",
        "#     source_directory=None, \n",
        "#     allow_reuse=True, \n",
        "#     version=None, \n",
        "#     hash_paths=None)\n",
        "# This returns a Step\n",
        "step1 = PythonScriptStep(name=\"train_step\",\n",
        "                         script_name=\"train.py\", \n",
        "                         compute_target=aml_compute, \n",
        "                         source_directory=project_folder,\n",
        "                         allow_reuse=False)\n",
        "print(\"Step1 created\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Note:** In the above call to PythonScriptStep(), the flag *allow_reuse* determines whether the step should reuse previous results when run with the same settings/inputs. This flag's default value is *True*; the default is set to *True* because, when inputs and parameters have not changed, we typically do not want to re-run a given pipeline step. \n",
        "\n",
        "If *allow_reuse* is set to *False*, a new run will always be generated for this step during pipeline execution. The *allow_reuse* flag can come in handy in situations where you do *not* want to re-run a pipeline step."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Running a few steps in parallel\n",
        "Here we are looking at a simple scenario where we are running a few steps (all involving PythonScriptStep)  in parallel. Running nodes in **parallel** is the default behavior for steps in a pipeline.\n",
        "\n",
        "We already have one step defined earlier. Let's define few more steps."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# All steps use files already available in the project_folder\n",
        "# All steps use the same Azure Machine Learning compute target as well\n",
        "step2 = PythonScriptStep(name=\"compare_step\",\n",
        "                         script_name=\"compare.py\", \n",
        "                         compute_target=aml_compute, \n",
        "                         source_directory=project_folder)\n",
        "\n",
        "step3 = PythonScriptStep(name=\"extract_step\",\n",
        "                         script_name=\"extract.py\", \n",
        "                         compute_target=aml_compute, \n",
        "                         source_directory=project_folder)\n",
        "\n",
        "# list of steps to run\n",
        "steps = [step1, step2, step3]\n",
        "print(\"Step lists created\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Build the pipeline\n",
        "Once we have the steps (or steps collection), we can build the [pipeline](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py). By deafult, all these steps will run in **parallel** once we submit the pipeline for run.\n",
        "\n",
        "A pipeline is created with a list of steps and a workspace. Submit a pipeline using [submit](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment%28class%29?view=azure-ml-py#submit). When submit is called, a [PipelineRun](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinerun?view=azure-ml-py) is created which in turn creates [StepRun](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.steprun?view=azure-ml-py) objects for each step in the workflow."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Syntax\n",
        "# Pipeline(workspace, \n",
        "#          steps, \n",
        "#          description=None, \n",
        "#          default_datastore_name=None, \n",
        "#          default_source_directory=None, \n",
        "#          resolve_closure=True, \n",
        "#          _workflow_provider=None, \n",
        "#          _service_endpoint=None)\n",
        "\n",
        "pipeline1 = Pipeline(workspace=ws, steps=steps)\n",
        "print (\"Pipeline is built\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Validate the pipeline\n",
        "You have the option to [validate](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py#validate) the pipeline prior to submitting for run. The platform runs validation steps such as checking for circular dependencies and parameter checks etc. even if you do not explicitly call validate method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "pipeline1.validate()\n",
        "print(\"Pipeline validation complete\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Submit the pipeline\n",
        "[Submitting](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py#submit) the pipeline involves creating an [Experiment](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment?view=azure-ml-py) object and providing the built pipeline for submission. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Submit syntax\n",
        "# submit(experiment_name, \n",
        "#        pipeline_parameters=None, \n",
        "#        continue_on_node_failure=False, \n",
        "#        regenerate_outputs=False)\n",
        "\n",
        "pipeline_run1 = Experiment(ws, 'Hello_World1').submit(pipeline1, regenerate_outputs=True)\n",
        "print(\"Pipeline is submitted for execution\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Note:** If regenerate_outputs is set to True, a new submit will always force generation of all step outputs, and disallow data reuse for any step of this run. Once this run is complete, however, subsequent runs may reuse the results of this run.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Examine the pipeline run\n",
        "\n",
        "#### Use RunDetails Widget\n",
        "We are going to use the RunDetails widget to examine the run of the pipeline. You can click each row below to get more details on the step runs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "RunDetails(pipeline_run1).show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Use Pipeline SDK objects\n",
        "You can cycle through the node_run objects and examine job logs, stdout, and stderr of each of the steps."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "step_runs = pipeline_run1.get_children()\n",
        "for step_run in step_runs:\n",
        "    status = step_run.get_status()\n",
        "    print('Script:', step_run.name, 'status:', status)\n",
        "    \n",
        "    # Change this if you want to see details even if the Step has succeeded.\n",
        "    if status == \"Failed\":\n",
        "        joblog = step_run.get_job_log()\n",
        "        print('job log:', joblog)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Get additonal run details\n",
        "If you wait until the pipeline_run is finished, you may be able to get additional details on the run. **Since this is a blocking call, the following code is commented out.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#pipeline_run1.wait_for_completion()\n",
        "#for step_run in pipeline_run1.get_children():\n",
        "#    print(\"{}: {}\".format(step_run.name, step_run.get_metrics()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Running a few steps in sequence\n",
        "Now let's see how we run a few steps in sequence. We already have three steps defined earlier. Let's *reuse* those steps for this part.\n",
        "\n",
        "We will reuse step1, step2, step3, but build the pipeline in such a way that we chain step3 after step2 and step2 after step1. Note that there is no explicit data dependency between these steps, but still steps can be made dependent by using the [run_after](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.builder.pipelinestep?view=azure-ml-py#run-after) construct."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "step2.run_after(step1)\n",
        "step3.run_after(step2)\n",
        "\n",
        "# Try a loop\n",
        "#step2.run_after(step3)\n",
        "\n",
        "# Now, construct the pipeline using the steps.\n",
        "\n",
        "# We can specify the \"final step\" in the chain, \n",
        "# Pipeline will take care of \"transitive closure\" and \n",
        "# figure out the implicit or explicit dependencies\n",
        "# https://www.geeksforgeeks.org/transitive-closure-of-a-graph/\n",
        "pipeline2 = Pipeline(workspace=ws, steps=[step3])\n",
        "print (\"Pipeline is built\")\n",
        "\n",
        "pipeline2.validate()\n",
        "print(\"Simple validation complete\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "pipeline_run2 = Experiment(ws, 'Hello_World2').submit(pipeline2)\n",
        "print(\"Pipeline is submitted for execution\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "RunDetails(pipeline_run2).show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Next: Pipelines with data dependency\n",
        "The next [notebook](./aml-pipelines-with-data-dependency-steps.ipynb) demostrates how to construct a pipeline with data dependency."
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "diray"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb
@@ -0,0 +1,365 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# How to Publish a Pipeline and Invoke the REST endpoint\n",
        "In this notebook, we will see how we can publish a pipeline and then invoke the REST endpoint."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Prerequisites and Azure Machine Learning Basics\n",
        "Make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n",
        "\n",
        "### Initialization Steps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import azureml.core\n",
        "from azureml.core import Workspace, Datastore\n",
        "from azureml.core.compute import AmlCompute\n",
        "from azureml.core.compute import ComputeTarget\n",
        "\n",
        "# Check core SDK version number\n",
        "print(\"SDK version:\", azureml.core.VERSION)\n",
        "\n",
        "from azureml.data.data_reference import DataReference\n",
        "from azureml.pipeline.core import Pipeline, PipelineData\n",
        "from azureml.pipeline.steps import PythonScriptStep\n",
        "from azureml.pipeline.core.graph import PipelineParameter\n",
        "\n",
        "print(\"Pipeline SDK-specific imports completed\")\n",
        "\n",
        "ws = Workspace.from_config()\n",
        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')\n",
        "\n",
        "# Default datastore (Azure file storage)\n",
        "def_file_store = ws.get_default_datastore() \n",
        "print(\"Default datastore's name: {}\".format(def_file_store.name))\n",
        "\n",
        "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
        "print(\"Blobstore's name: {}\".format(def_blob_store.name))\n",
        "\n",
        "# project folder\n",
        "project_folder = '.'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Compute Targets\n",
        "#### Retrieve an already attached  Azure Machine Learning Compute"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "aml_compute_target = \"aml-compute\"\n",
        "try:\n",
        "    aml_compute = AmlCompute(ws, aml_compute_target)\n",
        "    print(\"found existing compute target.\")\n",
        "except ComputeTargetException:\n",
        "    print(\"creating new compute target\")\n",
        "    \n",
        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n",
        "                                                                min_nodes = 1, \n",
        "                                                                max_nodes = 4)    \n",
        "    aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n",
        "    aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# For a more detailed view of current Azure Machine Learning Compute status, use get_status()\n",
        "# example: un-comment the following line.\n",
        "# print(aml_compute.get_status().serialize())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Building Pipeline Steps with Inputs and Outputs\n",
        "As mentioned earlier, a step in the pipeline can take data as input. This data can be a data source that lives in one of the accessible data locations, or intermediate data produced by a previous step in the pipeline."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Reference the data uploaded to blob storage using DataReference\n",
        "# Assign the datasource to blob_input_data variable\n",
        "blob_input_data = DataReference(\n",
        "    datastore=def_blob_store,\n",
        "    data_reference_name=\"test_data\",\n",
        "    path_on_datastore=\"20newsgroups/20news.pkl\")\n",
        "print(\"DataReference object created\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Define intermediate data using PipelineData\n",
        "processed_data1 = PipelineData(\"processed_data1\",datastore=def_blob_store)\n",
        "print(\"PipelineData object created\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Define a Step that consumes a datasource and produces intermediate data.\n",
        "In this step, we define a step that consumes a datasource and produces intermediate data.\n",
        "\n",
        "**Open `train.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.** "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# trainStep consumes the datasource (Datareference) in the previous step\n",
        "# and produces processed_data1\n",
        "trainStep = PythonScriptStep(\n",
        "    script_name=\"train.py\", \n",
        "        arguments=[\"--input_data\", blob_input_data, \"--output_train\", processed_data1],\n",
        "    inputs=[blob_input_data],\n",
        "    outputs=[processed_data1],\n",
        "    compute_target=aml_compute, \n",
        "    source_directory=project_folder\n",
        ")\n",
        "print(\"trainStep created\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Define a Step that consumes intermediate data and produces intermediate data\n",
        "In this step, we define a step that consumes an intermediate data and produces intermediate data.\n",
        "\n",
        "**Open `extract.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.** "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# extractStep to use the intermediate data produced by step4\n",
        "# This step also produces an output processed_data2\n",
        "processed_data2 = PipelineData(\"processed_data2\", datastore=def_blob_store)\n",
        "\n",
        "extractStep = PythonScriptStep(\n",
        "    script_name=\"extract.py\",\n",
        "    arguments=[\"--input_extract\", processed_data1, \"--output_extract\", processed_data2],\n",
        "    inputs=[processed_data1],\n",
        "    outputs=[processed_data2],\n",
        "    compute_target=aml_compute, \n",
        "    source_directory=project_folder)\n",
        "print(\"extractStep created\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Define a Step that consumes multiple intermediate data and produces intermediate data\n",
        "In this step, we define a step that consumes multiple intermediate data and produces intermediate data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### PipelineParameter"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This step also has a [PipelineParameter](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.graph.pipelineparameter?view=azure-ml-py) argument that help with calling the REST endpoint of the published pipeline."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# We will use this later in publishing pipeline\n",
        "pipeline_param = PipelineParameter(name=\"pipeline_arg\", default_value=10)\n",
        "print(\"pipeline parameter created\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Open `compare.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Now define step6 that takes two inputs (both intermediate data), and produce an output\n",
        "processed_data3 = PipelineData(\"processed_data3\", datastore=def_blob_store)\n",
        "\n",
        "\n",
        "\n",
        "compareStep = PythonScriptStep(\n",
        "    script_name=\"compare.py\",\n",
        "    arguments=[\"--compare_data1\", processed_data1, \"--compare_data2\", processed_data2, \"--output_compare\", processed_data3, \"--pipeline_param\", pipeline_param],\n",
        "    inputs=[processed_data1, processed_data2],\n",
        "    outputs=[processed_data3],    \n",
        "    compute_target=aml_compute, \n",
        "    source_directory=project_folder)\n",
        "print(\"compareStep created\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Build the pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "pipeline1 = Pipeline(workspace=ws, steps=[compareStep])\n",
        "print (\"Pipeline is built\")\n",
        "\n",
        "pipeline1.validate()\n",
        "print(\"Simple validation complete\") "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Publish the pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "published_pipeline1 = pipeline1.publish(name=\"My_New_Pipeline\", description=\"My Published Pipeline Description\")\n",
        "print(published_pipeline1.id)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Run published pipeline using its REST endpoint"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.authentication import AzureCliAuthentication\n",
        "import requests\n",
        "\n",
        "cli_auth = AzureCliAuthentication()\n",
        "aad_token = cli_auth.get_authentication_header()\n",
        "\n",
        "rest_endpoint1 = published_pipeline1.endpoint\n",
        "\n",
        "print(rest_endpoint1)\n",
        "\n",
        "# specify the param when running the pipeline\n",
        "response = requests.post(rest_endpoint1, \n",
        "                         headers=aad_token, \n",
        "                         json={\"ExperimentName\": \"My_Pipeline1\",\n",
        "                               \"RunSource\": \"SDK\",\n",
        "                               \"ParameterAssignments\": {\"pipeline_arg\": 45}})\n",
        "run_id = response.json()[\"Id\"]\n",
        "\n",
        "print(run_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Next: Data Transfer\n",
        "The next [notebook](./aml-pipelines-data-transfer.ipynb) will showcase data transfer steps between different types of data stores."
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "diray"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb
@@ -0,0 +1,366 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# AML Pipeline with AdlaStep\n",
        "This notebook is used to demonstrate the use of AdlaStep in AML Pipeline."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## AML and Pipeline SDK-specific imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "import azureml.core\n",
        "from azureml.exceptions import ComputeTargetException\n",
        "from azureml.core import Workspace, Experiment\n",
        "from azureml.pipeline.core import Pipeline, PipelineData\n",
        "from azureml.pipeline.steps import AdlaStep\n",
        "from azureml.core.datastore import Datastore\n",
        "from azureml.data.data_reference import DataReference\n",
        "\n",
        "# Check core SDK version number\n",
        "print(\"SDK version:\", azureml.core.VERSION)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Initialize Workspace\n",
        "\n",
        "Initialize a workspace object from persisted configuration. Make sure the config file is present at .\\config.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "create workspace"
        ]
      },
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "script_folder = '.'\n",
        "experiment_name = \"adla_101_experiment\"\n",
        "ws._initialize_folder(experiment_name=experiment_name, directory=script_folder)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Register Datastore"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from msrest.exceptions import HttpOperationError\n",
        "\n",
        "datastore_name='MyAdlsDatastore'\n",
        "subscription_id=os.getenv(\"ADL_SUBSCRIPTION_62\", \"<my-subscription-id>\") # subscription id of ADLS account\n",
        "resource_group=os.getenv(\"ADL_RESOURCE_GROUP_62\", \"<my-resource-group>\") # resource group of ADLS account\n",
        "store_name=os.getenv(\"ADL_STORENAME_62\", \"<my-datastore-name>\") # ADLS account name\n",
        "tenant_id=os.getenv(\"ADL_TENANT_62\", \"<my-tenant-id>\") # tenant id of service principal\n",
        "client_id=os.getenv(\"ADL_CLIENTID_62\", \"<my-client-id>\") # client id of service principal\n",
        "client_secret=os.getenv(\"ADL_CLIENT_62_SECRET\", \"<my-client-secret>\") # the secret of service principal\n",
        "\n",
        "try:\n",
        "    adls_datastore = Datastore.get(ws, datastore_name)\n",
        "    print(\"found datastore with name: %s\" % datastore_name)\n",
        "except HttpOperationError:\n",
        "    adls_datastore = Datastore.register_azure_data_lake(\n",
        "        workspace=ws,\n",
        "        datastore_name=datastore_name,\n",
        "        subscription_id=subscription_id, # subscription id of ADLS account\n",
        "        resource_group=resource_group, # resource group of ADLS account\n",
        "        store_name=store_name, # ADLS account name\n",
        "        tenant_id=tenant_id, # tenant id of service principal\n",
        "        client_id=client_id, # client id of service principal\n",
        "        client_secret=client_secret) # the secret of service principal\n",
        "    print(\"registered datastore with name: %s\" % datastore_name)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create DataReferences and PipelineData\n",
        "\n",
        "In the code cell below, replace datastorename with your default datastore name. Copy the file `testdata.txt` (located in the pipeline folder that this notebook is in) to the path on the datastore."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "datastorename = \"MyAdlsDatastore\"\n",
        "\n",
        "adls_datastore = Datastore(workspace=ws, name=datastorename)\n",
        "script_input = DataReference(\n",
        "    datastore=adls_datastore,\n",
        "    data_reference_name=\"script_input\",\n",
        "    path_on_datastore=\"testdata/testdata.txt\")\n",
        "\n",
        "script_output = PipelineData(\"script_output\", datastore=adls_datastore)\n",
        "\n",
        "print(\"Created Pipeline Data\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup Data Lake Account\n",
        "\n",
        "ADLA can only use data that is located in the default data store associated with that ADLA account. Through Azure portal, check the name of the default data store corresponding to the ADLA account you are using below. Replace the value associated with `adla_compute_name` in the code cell below accordingly."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "adla_compute_name = 'testadl' # Replace this with your default compute\n",
        "\n",
        "from azureml.core.compute import ComputeTarget, AdlaCompute\n",
        "\n",
        "def get_or_create_adla_compute(workspace, compute_name):\n",
        "    try:\n",
        "        return AdlaCompute(workspace, compute_name)\n",
        "    except ComputeTargetException as e:\n",
        "        if 'ComputeTargetNotFound' in e.message:\n",
        "            print('adla compute not found, creating...')\n",
        "            provisioning_config = AdlaCompute.provisioning_configuration()\n",
        "            new_adla_compute = ComputeTarget.create(workspace, compute_name, provisioning_config)\n",
        "            new_adla_compute.wait_for_completion()\n",
        "            return new_adla_compute\n",
        "        else:\n",
        "            raise e\n",
        "            \n",
        "adla_compute = get_or_create_adla_compute(ws, adla_compute_name)\n",
        "\n",
        "# CLI:\n",
        "# Create: az ml computetarget setup adla -n <name>\n",
        "# BYOC: az ml computetarget attach adla -n <name> -i <resource-id>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Once the above code cell completes, run the below to check your ADLA compute status:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print(\"ADLA compute state:{}\".format(adla_compute.provisioning_state))\n",
        "print(\"ADLA compute state:{}\".format(adla_compute.provisioning_errors))\n",
        "print(\"Using ADLA compute:{}\".format(adla_compute.cluster_resource_id))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create an AdlaStep"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**AdlaStep** is used to run U-SQL script using Azure Data Lake Analytics.\n",
        "\n",
        "- **name:** Name of module\n",
        "- **script_name:** name of U-SQL script\n",
        "- **inputs:** List of input port bindings\n",
        "- **outputs:** List of output port bindings\n",
        "- **adla_compute:** the ADLA compute to use for this job\n",
        "- **params:** Dictionary of name-value pairs to pass to U-SQL job *(optional)*\n",
        "- **degree_of_parallelism:** the degree of parallelism to use for this job *(optional)*\n",
        "- **priority:** the priority value to use for the current job *(optional)*\n",
        "- **runtime_version:** the runtime version of the Data Lake Analytics engine *(optional)*\n",
        "- **root_folder:** folder that contains the script, assemblies etc. *(optional)*\n",
        "- **hash_paths:** list of paths to hash to detect a change (script file is always hashed) *(optional)*\n",
        "\n",
        "### Remarks\n",
        "\n",
        "You can use `@@name@@` syntax in your script to refer to inputs, outputs, and params.\n",
        "\n",
        "* if `name` is the name of an input or output port binding, any occurences of `@@name@@` in the script\n",
        "are replaced with actual data path of corresponding port binding.\n",
        "* if `name` matches any key in `params` dict, any occurences of `@@name@@` will be replaced with\n",
        "corresponding value in dict.\n",
        "\n",
        "#### Sample script\n",
        "\n",
        "```\n",
        "@resourcereader =\n",
        "    EXTRACT query string\n",
        "    FROM \"@@script_input@@\"\n",
        "    USING Extractors.Csv();\n",
        "\n",
        "\n",
        "OUTPUT @resourcereader\n",
        "TO \"@@script_output@@\"\n",
        "USING Outputters.Csv();\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "adla_step = AdlaStep(\n",
        "    name='adla_script_step',\n",
        "    script_name='test_adla_script.usql',\n",
        "    inputs=[script_input],\n",
        "    outputs=[script_output],\n",
        "    compute_target=adla_compute)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Build and Submit the Experiment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "pipeline = Pipeline(\n",
        "    description=\"adla_102\",\n",
        "    workspace=ws, \n",
        "    steps=[adla_step],\n",
        "    default_source_directory=script_folder)\n",
        "\n",
        "pipeline_run = Experiment(ws, experiment_name).submit(pipeline)\n",
        "pipeline_run.wait_for_completion()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### View Run Details"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(pipeline_run).show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Examine the run\n",
        "You can cycle through the node_run objects and examine job logs, stdout, and stderr of each of the steps."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "step_runs = pipeline_run.get_children()\n",
        "for step_run in step_runs:\n",
        "    status = step_run.get_status()\n",
        "    print('node', step_run.name, 'status:', status)\n",
        "    if status == \"Failed\":\n",
        "        joblog = step_run.get_job_log()\n",
        "        print('job log:', joblog)\n",
        "        stdout_log = step_run.get_stdout_log()\n",
        "        print('stdout log:', stdout_log)\n",
        "        stderr_log = step_run.get_stderr_log()\n",
        "        print('stderr log:', stderr_log)\n",
        "        with open(\"logs-\" + step_run.name + \".txt\", \"w\") as f:\n",
        "            f.write(joblog)\n",
        "            print(\"Job log written to logs-\"+ step_run.name + \".txt\")\n",
        "    if status == \"Finished\":\n",
        "        stdout_log = step_run.get_stdout_log()\n",
        "        print('stdout log:', stdout_log)"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "diray"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb
@@ -0,0 +1,698 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Using Databricks as a Compute Target from Azure Machine Learning Pipeline\n",
        "To use Databricks as a compute target from [Azure Machine Learning Pipeline](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines), a [DatabricksStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep?view=azure-ml-py) is used. This notebook demonstrates the use of DatabricksStep in Azure Machine Learning Pipeline.\n",
        "\n",
        "The notebook will show:\n",
        "1. Running an arbitrary Databricks notebook that the customer has in Databricks workspace\n",
        "2. Running an arbitrary Python script that the customer has in DBFS\n",
        "3. Running an arbitrary Python script that is available on local computer (will upload to DBFS, and then run in Databricks) \n",
        "4. Running a JAR job that the customer has in DBFS.\n",
        "\n",
        "## Before you begin:\n",
        "\n",
        "1. **Create an Azure Databricks workspace** in the same subscription where you have your Azure Machine Learning workspace. You will need details of this workspace later on to define DatabricksStep. [Click here](https://ms.portal.azure.com/#blade/HubsExtension/Resources/resourceType/Microsoft.Databricks%2Fworkspaces) for more information.\n",
        "2. **Create PAT (access token)**: Manually create a Databricks access token at the Azure Databricks portal. See [this](https://docs.databricks.com/api/latest/authentication.html#generate-a-token) for more information.\n",
        "3. **Add demo notebook to ADB**: This notebook has a sample you can use as is. Launch Azure Databricks attached to your Azure Machine Learning workspace and add a new notebook. \n",
        "4. **Create/attach a Blob storage** for use from ADB"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Add demo notebook to ADB Workspace\n",
        "Copy and paste the below code to create a new notebook in your ADB workspace."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "```python\n",
        "# direct access\n",
        "dbutils.widgets.get(\"myparam\")\n",
        "p = getArgument(\"myparam\")\n",
        "print (\"Param -\\'myparam':\")\n",
        "print (p)\n",
        "\n",
        "dbutils.widgets.get(\"input\")\n",
        "i = getArgument(\"input\")\n",
        "print (\"Param -\\'input':\")\n",
        "print (i)\n",
        "\n",
        "dbutils.widgets.get(\"output\")\n",
        "o = getArgument(\"output\")\n",
        "print (\"Param -\\'output':\")\n",
        "print (o)\n",
        "\n",
        "n = i + \"/testdata.txt\"\n",
        "df = spark.read.csv(n)\n",
        "\n",
        "display (df)\n",
        "\n",
        "data = [('value1', 'value2')]\n",
        "df2 = spark.createDataFrame(data)\n",
        "\n",
        "z = o + \"/output.txt\"\n",
        "df2.write.csv(z)\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Azure Machine Learning and Pipeline SDK-specific imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "import azureml.core\n",
        "from azureml.core.runconfig import JarLibrary\n",
        "from azureml.core.compute import ComputeTarget, DatabricksCompute\n",
        "from azureml.exceptions import ComputeTargetException\n",
        "from azureml.core import Workspace, Experiment\n",
        "from azureml.pipeline.core import Pipeline, PipelineData\n",
        "from azureml.pipeline.steps import DatabricksStep\n",
        "from azureml.core.datastore import Datastore\n",
        "from azureml.data.data_reference import DataReference\n",
        "\n",
        "# Check core SDK version number\n",
        "print(\"SDK version:\", azureml.core.VERSION)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Initialize Workspace\n",
        "\n",
        "Initialize a workspace object from persisted configuration. Make sure the config file is present at .\\config.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Attach Databricks compute target\n",
        "Next, you need to add your Databricks workspace to Azure Machine Learning as a compute target and give it a name. You will use this name to refer to your Databricks workspace compute target inside Azure Machine Learning.\n",
        "\n",
        "- **Resource Group** - The resource group name of your Azure Machine Learning workspace\n",
        "- **Databricks Workspace Name** - The workspace name of your Azure Databricks workspace\n",
        "- **Databricks Access Token** - The access token you created in ADB\n",
        "\n",
        "**The Databricks workspace need to be present in the same subscription as your AML workspace**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Replace with your account info before running.\n",
        " \n",
        "db_compute_name=os.getenv(\"DATABRICKS_COMPUTE_NAME\", \"<my-databricks-compute-name>\") # Databricks compute name\n",
        "db_resource_group=os.getenv(\"DATABRICKS_RESOURCE_GROUP\", \"<my-db-resource-group>\") # Databricks resource group\n",
        "db_workspace_name=os.getenv(\"DATABRICKS_WORKSPACE_NAME\", \"<my-db-workspace-name>\") # Databricks workspace name\n",
        "db_access_token=os.getenv(\"DATABRICKS_ACCESS_TOKEN\", \"<my-access-token>\") # Databricks access token\n",
        " \n",
        "try:\n",
        "    databricks_compute = DatabricksCompute(workspace=ws, name=db_compute_name)\n",
        "    print('Compute target {} already exists'.format(db_compute_name))\n",
        "except ComputeTargetException:\n",
        "    print('Compute not found, will use below parameters to attach new one')\n",
        "    print('db_compute_name {}'.format(db_compute_name))\n",
        "    print('db_resource_group {}'.format(db_resource_group))\n",
        "    print('db_workspace_name {}'.format(db_workspace_name))\n",
        "    print('db_access_token {}'.format(db_access_token))\n",
        " \n",
        "    config = DatabricksCompute.attach_configuration(\n",
        "        resource_group = db_resource_group,\n",
        "        workspace_name = db_workspace_name,\n",
        "        access_token= db_access_token)\n",
        "    databricks_compute=ComputeTarget.attach(ws, db_compute_name, config)\n",
        "    databricks_compute.wait_for_completion(True)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data Connections with Inputs and Outputs\n",
        "The DatabricksStep supports Azure Bloband ADLS for inputs and outputs. You also will need to define a [Secrets](https://docs.azuredatabricks.net/user-guide/secrets/index.html) scope to enable authentication to external data sources such as Blob and ADLS from Databricks.\n",
        "\n",
        "- Databricks documentation on [Azure Blob](https://docs.azuredatabricks.net/spark/latest/data-sources/azure/azure-storage.html)\n",
        "- Databricks documentation on [ADLS](https://docs.databricks.com/spark/latest/data-sources/azure/azure-datalake.html)\n",
        "\n",
        "### Type of Data Access\n",
        "Databricks allows to interact with Azure Blob and ADLS in two ways.\n",
        "- **Direct Access**: Databricks allows you to interact with Azure Blob or ADLS URIs directly. The input or output URIs will be mapped to a Databricks widget param in the Databricks notebook.\n",
        "- **Mouting**: You will be supplied with additional parameters and secrets that will enable you to mount your ADLS or Azure Blob input or output location in your Databricks notebook."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Direct Access: Python sample code\n",
        "If you have a data reference named \"input\" it will represent the URI of the input and you can access it directly in the Databricks python notebook like so:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "```python\n",
        "dbutils.widgets.get(\"input\")\n",
        "y = getArgument(\"input\")\n",
        "df = spark.read.csv(y)\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Mounting: Python sample code for Azure Blob\n",
        "Given an Azure Blob data reference named \"input\" the following widget params will be made available in the Databricks notebook:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "```python\n",
        "# This contains the input URI\n",
        "dbutils.widgets.get(\"input\")\n",
        "myinput_uri = getArgument(\"input\")\n",
        "\n",
        "# How to get the input datastore name inside ADB notebook\n",
        "# This contains the name of a Databricks secret (in the predefined \"amlscope\" secret scope) \n",
        "# that contians an access key or sas for the Azure Blob input (this name is obtained by appending \n",
        "# the name of the input with \"_blob_secretname\". \n",
        "dbutils.widgets.get(\"input_blob_secretname\") \n",
        "myinput_blob_secretname = getArgument(\"input_blob_secretname\")\n",
        "\n",
        "# This contains the required configuration for mounting\n",
        "dbutils.widgets.get(\"input_blob_config\")\n",
        "myinput_blob_config = getArgument(\"input_blob_config\")\n",
        "\n",
        "# Usage\n",
        "dbutils.fs.mount(\n",
        "  source = myinput_uri,\n",
        "  mount_point = \"/mnt/input\",\n",
        "  extra_configs = {myinput_blob_config:dbutils.secrets.get(scope = \"amlscope\", key = myinput_blob_secretname)})\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Mounting: Python sample code for ADLS\n",
        "Given an ADLS data reference named \"input\" the following widget params will be made available in the Databricks notebook:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "```python\n",
        "# This contains the input URI\n",
        "dbutils.widgets.get(\"input\") \n",
        "myinput_uri = getArgument(\"input\")\n",
        "\n",
        "# This contains the client id for the service principal \n",
        "# that has access to the adls input\n",
        "dbutils.widgets.get(\"input_adls_clientid\") \n",
        "myinput_adls_clientid = getArgument(\"input_adls_clientid\")\n",
        "\n",
        "# This contains the name of a Databricks secret (in the predefined \"amlscope\" secret scope) \n",
        "# that contains the secret for the above mentioned service principal\n",
        "dbutils.widgets.get(\"input_adls_secretname\") \n",
        "myinput_adls_secretname = getArgument(\"input_adls_secretname\")\n",
        "\n",
        "# This contains the refresh url for the mounting configs\n",
        "dbutils.widgets.get(\"input_adls_refresh_url\") \n",
        "myinput_adls_refresh_url = getArgument(\"input_adls_refresh_url\")\n",
        "\n",
        "# Usage \n",
        "configs = {\"dfs.adls.oauth2.access.token.provider.type\": \"ClientCredential\",\n",
        "           \"dfs.adls.oauth2.client.id\": myinput_adls_clientid,\n",
        "           \"dfs.adls.oauth2.credential\": dbutils.secrets.get(scope = \"amlscope\", key =myinput_adls_secretname),\n",
        "           \"dfs.adls.oauth2.refresh.url\": myinput_adls_refresh_url}\n",
        "\n",
        "dbutils.fs.mount(\n",
        "  source = myinput_uri,\n",
        "  mount_point = \"/mnt/output\",\n",
        "  extra_configs = configs)\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Use Databricks from Azure Machine Learning Pipeline\n",
        "To use Databricks as a compute target from Azure Machine Learning Pipeline, a DatabricksStep is used. Let's define a datasource (via DataReference) and intermediate data (via PipelineData) to be used in DatabricksStep."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Use the default blob storage\n",
        "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
        "print('Datastore {} will be used'.format(def_blob_store.name))\n",
        "\n",
        "# We are uploading a sample file in the local directory to be used as a datasource\n",
        "def_blob_store.upload_files(files=[\"./testdata.txt\"], target_path=\"dbtest\", overwrite=False)\n",
        "\n",
        "step_1_input = DataReference(datastore=def_blob_store, path_on_datastore=\"dbtest\",\n",
        "                                     data_reference_name=\"input\")\n",
        "\n",
        "step_1_output = PipelineData(\"output\", datastore=def_blob_store)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Add a DatabricksStep\n",
        "Adds a Databricks notebook as a step in a Pipeline.\n",
        "- ***name:** Name of the Module\n",
        "- **inputs:** List of input connections for data consumed by this step. Fetch this inside the notebook using dbutils.widgets.get(\"input\")\n",
        "- **outputs:** List of output port definitions for outputs produced by this step. Fetch this inside the notebook using dbutils.widgets.get(\"output\")\n",
        "- **existing_cluster_id:** Cluster ID of an existing Interactive cluster on the Databricks workspace. If you are providing this, do not provide any of the parameters below that are used to create a new cluster such as spark_version, node_type, etc.\n",
        "- **spark_version:** Version of spark for the databricks run cluster. default value: 4.0.x-scala2.11\n",
        "- **node_type:** Azure vm node types for the databricks run cluster. default value: Standard_D3_v2\n",
        "- **num_workers:** Specifies a static number of workers for the databricks run cluster\n",
        "- **min_workers:** Specifies a min number of workers to use for auto-scaling the databricks run cluster\n",
        "- **max_workers:** Specifies a max number of workers to use for auto-scaling the databricks run cluster\n",
        "- **spark_env_variables:** Spark environment variables for the databricks run cluster (dictionary of {str:str}). default value: {'PYSPARK_PYTHON': '/databricks/python3/bin/python3'}\n",
        "- **notebook_path:** Path to the notebook in the databricks instance. If you are providing this, do not provide python script related paramaters or JAR related parameters.\n",
        "- **notebook_params:** Parameters  for the databricks notebook (dictionary of {str:str}). Fetch this inside the notebook using dbutils.widgets.get(\"myparam\")\n",
        "- **python_script_path:** The path to the python script in the DBFS or S3. If you are providing this, do not provide python_script_name which is used for uploading script from local machine.\n",
        "- **python_script_params:** Parameters for the python script (list of str)\n",
        "- **main_class_name:** The name of the entry point in a JAR module. If you are providing this, do not provide any python script or notebook related parameters.\n",
        "- **jar_params:** Parameters for the JAR module (list of str)\n",
        "- **python_script_name:** name of a python script on your local machine (relative to source_directory). If you are providing this do not provide python_script_path which is used to execute a remote python script; or any of the JAR or notebook related parameters.\n",
        "- **source_directory:** folder that contains the script and other files\n",
        "- **hash_paths:** list of paths to hash to detect a change in source_directory (script file is always hashed)\n",
        "- **run_name:** Name in databricks for this run\n",
        "- **timeout_seconds:** Timeout for the databricks run\n",
        "- **runconfig:** Runconfig to use. Either pass runconfig or each library type as a separate parameter but do not mix the two\n",
        "- **maven_libraries:** maven libraries for the databricks run\n",
        "- **pypi_libraries:** pypi libraries for the databricks run\n",
        "- **egg_libraries:** egg libraries for the databricks run\n",
        "- **jar_libraries:** jar libraries for the databricks run\n",
        "- **rcran_libraries:** rcran libraries for the databricks run\n",
        "- **compute_target:** Azure Databricks compute\n",
        "- **allow_reuse:** Whether the step should reuse previous results when run with the same settings/inputs\n",
        "- **version:** Optional version tag to denote a change in functionality for the step\n",
        "\n",
        "\\* *denotes required fields*  \n",
        "*You must provide exactly one of num_workers or min_workers and max_workers paramaters*  \n",
        "*You must provide exactly one of databricks_compute or databricks_compute_name parameters*\n",
        "\n",
        "## Use runconfig to specify library dependencies\n",
        "You can use a runconfig to specify the library dependencies for your cluster in Databricks. The runconfig will contain a databricks section as follows:\n",
        "```yaml\n",
        "environment:\n",
        "# Databricks details\n",
        "  databricks:\n",
        "# List of maven libraries.\n",
        "    mavenLibraries:\n",
        "    - coordinates: org.jsoup:jsoup:1.7.1\n",
        "      repo: ''\n",
        "      exclusions:\n",
        "      - slf4j:slf4j\n",
        "      - '*:hadoop-client'\n",
        "# List of PyPi libraries\n",
        "    pypiLibraries:\n",
        "    - package: beautifulsoup4\n",
        "      repo: ''\n",
        "# List of RCran libraries\n",
        "    rcranLibraries:\n",
        "    - package: ada\n",
        "      repo: http://cran.us.r-project.org\n",
        "# List of JAR libraries\n",
        "    jarLibraries:\n",
        "    - library: dbfs:/mnt/libraries/library.jar\n",
        "# List of Egg libraries\n",
        "    eggLibraries:\n",
        "    - library: dbfs:/mnt/libraries/library.egg\n",
        "```\n",
        "\n",
        "You can then create a RunConfiguration object using this file and pass it as the runconfig parameter to DatabricksStep.\n",
        "```python\n",
        "from azureml.core.runconfig import RunConfiguration\n",
        "\n",
        "runconfig = RunConfiguration()\n",
        "runconfig.load(path='<directory_where_runconfig_is_stored>', name='<runconfig_file_name>')\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 1. Running the demo notebook already added to the Databricks workspace\n",
        "Create a notebook in the Azure Databricks workspace, and provide the path to that notebook as the value associated with the environment variable \"DATABRICKS_NOTEBOOK_PATH\". This will then set the variable\u00c2\u00a0notebook_path\u00c2\u00a0when you run the code cell below:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "notebook_path=os.getenv(\"DATABRICKS_NOTEBOOK_PATH\", \"<my-databricks-notebook-path>\") # Databricks notebook path\n",
        "\n",
        "dbNbStep = DatabricksStep(\n",
        "    name=\"DBNotebookInWS\",\n",
        "    inputs=[step_1_input],\n",
        "    outputs=[step_1_output],\n",
        "    num_workers=1,\n",
        "    notebook_path=notebook_path,\n",
        "    notebook_params={'myparam': 'testparam'},\n",
        "    run_name='DB_Notebook_demo',\n",
        "    compute_target=databricks_compute,\n",
        "    allow_reuse=False\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Build and submit the Experiment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "steps = [dbNbStep]\n",
        "pipeline = Pipeline(workspace=ws, steps=steps)\n",
        "pipeline_run = Experiment(ws, 'DB_Notebook_demo').submit(pipeline)\n",
        "pipeline_run.wait_for_completion()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### View Run Details"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(pipeline_run).show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 2. Running a Python script that is already added in DBFS\n",
        "To run a Python script that is already uploaded to DBFS, follow the instructions below. You will first upload the Python script to DBFS using the [CLI](https://docs.azuredatabricks.net/user-guide/dbfs-databricks-file-system.html).\n",
        "\n",
        "The commented out code in the below cell assumes that you have uploaded `train-db-dbfs.py` to the root folder in DBFS. You can upload `train-db-dbfs.py` to the root folder in DBFS using this commandline so you can use `python_script_path = \"dbfs:/train-db-dbfs.py\"`:\n",
        "\n",
        "```\n",
        "dbfs cp ./train-db-dbfs.py dbfs:/train-db-dbfs.py\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "python_script_path = \"dbfs:/train-db-dbfs.py\"\n",
        "\n",
        "dbPythonInDbfsStep = DatabricksStep(\n",
        "    name=\"DBPythonInDBFS\",\n",
        "    inputs=[step_1_input],\n",
        "    num_workers=1,\n",
        "    python_script_path=python_script_path,\n",
        "    python_script_params={'--input_data'},\n",
        "    run_name='DB_Python_demo',\n",
        "    compute_target=databricks_compute,\n",
        "    allow_reuse=False\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Build and submit the Experiment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "steps = [dbPythonInDbfsStep]\n",
        "pipeline = Pipeline(workspace=ws, steps=steps)\n",
        "pipeline_run = Experiment(ws, 'DB_Python_demo').submit(pipeline)\n",
        "pipeline_run.wait_for_completion()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### View Run Details"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(pipeline_run).show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 3. Running a Python script in Databricks that currenlty is in local computer\n",
        "To run a Python script that is currently in your local computer, follow the instructions below. \n",
        "\n",
        "The commented out code below code assumes that you have `train-db-local.py` in the `scripts` subdirectory under the current working directory.\n",
        "\n",
        "In this case, the Python script will be uploaded first to DBFS, and then the script will be run in Databricks."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "python_script_name = \"train-db-local.py\"\n",
        "source_directory = \".\"\n",
        "\n",
        "dbPythonInLocalMachineStep = DatabricksStep(\n",
        "    name=\"DBPythonInLocalMachine\",\n",
        "    inputs=[step_1_input],\n",
        "    num_workers=1,\n",
        "    python_script_name=python_script_name,\n",
        "    source_directory=source_directory,\n",
        "    run_name='DB_Python_Local_demo',\n",
        "    compute_target=databricks_compute,\n",
        "    allow_reuse=False\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Build and submit the Experiment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "steps = [dbPythonInLocalMachineStep]\n",
        "pipeline = Pipeline(workspace=ws, steps=steps)\n",
        "pipeline_run = Experiment(ws, 'DB_Python_Local_demo').submit(pipeline)\n",
        "pipeline_run.wait_for_completion()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### View Run Details"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(pipeline_run).show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 4. Running a JAR job that is alreay added in DBFS\n",
        "To run a JAR job that is already uploaded to DBFS, follow the instructions below. You will first upload the JAR file to DBFS using the [CLI](https://docs.azuredatabricks.net/user-guide/dbfs-databricks-file-system.html).\n",
        "\n",
        "The commented out code in the below cell assumes that you have uploaded `train-db-dbfs.jar` to the root folder in DBFS. You can upload `train-db-dbfs.jar` to the root folder in DBFS using this commandline so you can use `jar_library_dbfs_path = \"dbfs:/train-db-dbfs.jar\"`:\n",
        "\n",
        "```\n",
        "dbfs cp ./train-db-dbfs.jar dbfs:/train-db-dbfs.jar\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "main_jar_class_name = \"com.microsoft.aeva.Main\"\n",
        "jar_library_dbfs_path = \"dbfs:/train-db-dbfs.jar\"\n",
        "\n",
        "dbJarInDbfsStep = DatabricksStep(\n",
        "    name=\"DBJarInDBFS\",\n",
        "    inputs=[step_1_input],\n",
        "    num_workers=1,\n",
        "    main_class_name=main_jar_class_name,\n",
        "    jar_params={'arg1', 'arg2'},\n",
        "    run_name='DB_JAR_demo',\n",
        "    jar_libraries=[JarLibrary(jar_library_dbfs_path)],\n",
        "    compute_target=databricks_compute,\n",
        "    allow_reuse=False\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Build and submit the Experiment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "steps = [dbJarInDbfsStep]\n",
        "pipeline = Pipeline(workspace=ws, steps=steps)\n",
        "pipeline_run = Experiment(ws, 'DB_JAR_demo').submit(pipeline)\n",
        "pipeline_run.wait_for_completion()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### View Run Details"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(pipeline_run).show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Next: ADLA as a Compute Target\n",
        "To use ADLA as a compute target from Azure Machine Learning Pipeline, a AdlaStep is used. This [notebook](./aml-pipelines-use-adla-as-compute-target.ipynb) demonstrates the use of AdlaStep in Azure Machine Learning Pipeline."
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "diray"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "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": "3.6.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb
@@ -0,0 +1,414 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Azure Machine Learning Pipelines with Data Dependency\n",
        "In this notebook, we will see how we can build a pipeline with implicit data dependancy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Prerequisites and Azure Machine Learning Basics\n",
        "Make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n",
        "\n",
        "### Azure Machine Learning and Pipeline SDK-specific Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import azureml.core\n",
        "from azureml.core import Workspace, Experiment, Datastore\n",
        "from azureml.core.compute import AmlCompute\n",
        "from azureml.core.compute import ComputeTarget\n",
        "from azureml.widgets import RunDetails\n",
        "\n",
        "# Check core SDK version number\n",
        "print(\"SDK version:\", azureml.core.VERSION)\n",
        "\n",
        "from azureml.data.data_reference import DataReference\n",
        "from azureml.pipeline.core import Pipeline, PipelineData\n",
        "from azureml.pipeline.steps import PythonScriptStep\n",
        "print(\"Pipeline SDK-specific imports completed\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Initialize Workspace\n",
        "\n",
        "Initialize a [workspace](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace(class%29) object from persisted configuration."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "create workspace"
        ]
      },
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')\n",
        "\n",
        "# Default datastore (Azure file storage)\n",
        "def_file_store = ws.get_default_datastore() \n",
        "print(\"Default datastore's name: {}\".format(def_file_store.name))\n",
        "\n",
        "def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
        "print(\"Blobstore's name: {}\".format(def_blob_store.name))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# project folder\n",
        "project_folder = '.'\n",
        "    \n",
        "print('Sample projects will be created in {}.'.format(project_folder))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Required data and script files for the the tutorial\n",
        "Sample files required to finish this tutorial are already copied to the project folder specified above. Even though the .py provided in the samples don't have much \"ML work,\" as a data scientist, you will work on this extensively as part of your work. To complete this tutorial, the contents of these files are not very important. The one-line files are for demostration purpose only."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Compute Targets\n",
        "See the list of Compute Targets on the workspace."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "cts = ws.compute_targets\n",
        "for ct in cts:\n",
        "    print(ct)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Retrieve or create a Aml compute\n",
        "Azure Machine Learning Compute is a service for provisioning and managing clusters of Azure virtual machines for running machine learning workloads. Let's create a new Aml Compute in the current workspace, if it doesn't already exist. We will then run the training script on this compute target."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "aml_compute_target = \"aml-compute\"\n",
        "try:\n",
        "    aml_compute = AmlCompute(ws, aml_compute_target)\n",
        "    print(\"found existing compute target.\")\n",
        "except ComputeTargetException:\n",
        "    print(\"creating new compute target\")\n",
        "    \n",
        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n",
        "                                                                min_nodes = 1, \n",
        "                                                                max_nodes = 4)    \n",
        "    aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n",
        "    aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
        "    \n",
        "print(\"Aml Compute attached\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# For a more detailed view of current Azure Machine Learning Compute status, use get_status()\n",
        "# example: un-comment the following line.\n",
        "# print(aml_compute.get_status().serialize())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Wait for this call to finish before proceeding (you will see the asterisk turning to a number).**\n",
        "\n",
        "Now that you have created the compute target, let's see what the workspace's compute_targets() function returns. You should now see one entry named 'amlcompute' of type AmlCompute."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Building Pipeline Steps with Inputs and Outputs\n",
        "As mentioned earlier, a step in the pipeline can take data as input. This data can be a data source that lives in one of the accessible data locations, or intermediate data produced by a previous step in the pipeline.\n",
        "\n",
        "### Datasources\n",
        "Datasource is represented by **[DataReference](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.data_reference.datareference?view=azure-ml-py)** object and points to data that lives in or is accessible from Datastore. DataReference could be a pointer to a file or a directory."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Reference the data uploaded to blob storage using DataReference\n",
        "# Assign the datasource to blob_input_data variable\n",
        "\n",
        "# DataReference(datastore, \n",
        "#               data_reference_name=None, \n",
        "#               path_on_datastore=None, \n",
        "#               mode='mount', \n",
        "#               path_on_compute=None, \n",
        "#               overwrite=False)\n",
        "\n",
        "blob_input_data = DataReference(\n",
        "    datastore=def_blob_store,\n",
        "    data_reference_name=\"test_data\",\n",
        "    path_on_datastore=\"20newsgroups/20news.pkl\")\n",
        "print(\"DataReference object created\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Intermediate/Output Data\n",
        "Intermediate data (or output of a Step) is represented by **[PipelineData](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py)** object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps.\n",
        "\n",
        "#### Constructing PipelineData\n",
        "- **name:** [*Required*] Name of the data item within the pipeline graph\n",
        "- **datastore_name:** Name of the Datastore to write this output to\n",
        "- **output_name:** Name of the output\n",
        "- **output_mode:** Specifies \"upload\" or \"mount\" modes for producing output (default: mount)\n",
        "- **output_path_on_compute:** For \"upload\" mode, the path to which the module writes this output during execution\n",
        "- **output_overwrite:** Flag to overwrite pre-existing data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Define intermediate data using PipelineData\n",
        "# Syntax\n",
        "\n",
        "# PipelineData(name, \n",
        "#              datastore=None, \n",
        "#              output_name=None, \n",
        "#              output_mode='mount', \n",
        "#              output_path_on_compute=None, \n",
        "#              output_overwrite=None, \n",
        "#              data_type=None, \n",
        "#              is_directory=None)\n",
        "\n",
        "# Naming the intermediate data as processed_data1 and assigning it to the variable processed_data1.\n",
        "processed_data1 = PipelineData(\"processed_data1\",datastore=def_blob_store)\n",
        "print(\"PipelineData object created\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Pipelines steps using datasources and intermediate data\n",
        "Machine learning pipelines can have many steps and these steps could use or reuse datasources and intermediate data. Here's how we construct such a pipeline:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Define a Step that consumes a datasource and produces intermediate data.\n",
        "In this step, we define a step that consumes a datasource and produces intermediate data.\n",
        "\n",
        "**Open `train.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.** "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# step4 consumes the datasource (Datareference) in the previous step\n",
        "# and produces processed_data1\n",
        "trainStep = PythonScriptStep(\n",
        "    script_name=\"train.py\", \n",
        "    arguments=[\"--input_data\", blob_input_data, \"--output_train\", processed_data1],\n",
        "    inputs=[blob_input_data],\n",
        "    outputs=[processed_data1],\n",
        "    compute_target=aml_compute, \n",
        "    source_directory=project_folder\n",
        ")\n",
        "print(\"trainStep created\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Define a Step that consumes intermediate data and produces intermediate data\n",
        "In this step, we define a step that consumes an intermediate data and produces intermediate data.\n",
        "\n",
        "**Open `extract.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.** "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# step5 to use the intermediate data produced by step4\n",
        "# This step also produces an output processed_data2\n",
        "processed_data2 = PipelineData(\"processed_data2\", datastore=def_blob_store)\n",
        "\n",
        "extractStep = PythonScriptStep(\n",
        "    script_name=\"extract.py\",\n",
        "    arguments=[\"--input_extract\", processed_data1, \"--output_extract\", processed_data2],\n",
        "    inputs=[processed_data1],\n",
        "    outputs=[processed_data2],\n",
        "    compute_target=aml_compute, \n",
        "    source_directory=project_folder)\n",
        "print(\"extractStep created\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Define a Step that consumes multiple intermediate data and produces intermediate data\n",
        "In this step, we define a step that consumes multiple intermediate data and produces intermediate data.\n",
        "\n",
        "**Open `compare.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Now define step6 that takes two inputs (both intermediate data), and produce an output\n",
        "processed_data3 = PipelineData(\"processed_data3\", datastore=def_blob_store)\n",
        "\n",
        "compareStep = PythonScriptStep(\n",
        "    script_name=\"compare.py\",\n",
        "    arguments=[\"--compare_data1\", processed_data1, \"--compare_data2\", processed_data2, \"--output_compare\", processed_data3],\n",
        "    inputs=[processed_data1, processed_data2],\n",
        "    outputs=[processed_data3],    \n",
        "    compute_target=aml_compute, \n",
        "    source_directory=project_folder)\n",
        "print(\"compareStep created\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Build the pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "pipeline1 = Pipeline(workspace=ws, steps=[compareStep])\n",
        "print (\"Pipeline is built\")\n",
        "\n",
        "pipeline1.validate()\n",
        "print(\"Simple validation complete\") "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "pipeline_run1 = Experiment(ws, 'Data_dependency').submit(pipeline1)\n",
        "print(\"Pipeline is submitted for execution\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "RunDetails(pipeline_run1).show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Next: Publishing the Pipeline and calling it from the REST endpoint\n",
        "See this [notebook](./aml-pipelines-publish-and-run-using-rest-endpoint.ipynb) to understand how the pipeline is published and you can call the REST endpoint to run the pipeline."
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "diray"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/compare.py
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/compare.py
@@ -0,0 +1,24 @@
 # Copyright (c) Microsoft. All rights reserved.
 # Licensed under the MIT license.
 import argparse
 import os
 print("In compare.py")
 print("As a data scientist, this is where I use my compare code.")
 parser = argparse.ArgumentParser("compare")
 parser.add_argument("--compare_data1", type=str, help="compare_data1 data")
 parser.add_argument("--compare_data2", type=str, help="compare_data2 data")
 parser.add_argument("--output_compare", type=str, help="output_compare directory")
 parser.add_argument("--pipeline_param", type=int, help="pipeline parameter")
 args = parser.parse_args()
 print("Argument 1: %s" % args.compare_data1)
 print("Argument 2: %s" % args.compare_data2)
 print("Argument 3: %s" % args.output_compare)
 print("Argument 4: %s" % args.pipeline_param)
 if not (args.output_compare is None):
    os.makedirs(args.output_compare, exist_ok=True)
    print("%s created" % args.output_compare)
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/extract.py
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/extract.py
@@ -0,0 +1,21 @@
 # Copyright (c) Microsoft. All rights reserved.
 # Licensed under the MIT license.
 import argparse
 import os
 print("In extract.py")
 print("As a data scientist, this is where I use my extract code.")
 parser = argparse.ArgumentParser("extract")
 parser.add_argument("--input_extract", type=str, help="input_extract data")
 parser.add_argument("--output_extract", type=str, help="output_extract directory")
 args = parser.parse_args()
 print("Argument 1: %s" % args.input_extract)
 print("Argument 2: %s" % args.output_extract)
 if not (args.output_extract is None):
    os.makedirs(args.output_extract, exist_ok=True)
    print("%s created" % args.output_extract)
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/train-db-dbfs.py
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/train-db-dbfs.py
@@ -0,0 +1,5 @@
 # Copyright (c) Microsoft. All rights reserved.
 # Licensed under the MIT license.
 print("In train.py")
 print("As a data scientist, this is where I use my training code.")
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/train-db-local.py
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/train-db-local.py
@@ -0,0 +1,5 @@
 # Copyright (c) Microsoft. All rights reserved.
 # Licensed under the MIT license.
 print("In train.py")
 print("As a data scientist, this is where I use my training code.")
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/train.py
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/train.py
@@ -0,0 +1,22 @@
 # Copyright (c) Microsoft. All rights reserved.
 # Licensed under the MIT license.
 import argparse
 import os
 print("In train.py")
 print("As a data scientist, this is where I use my training code.")
 parser = argparse.ArgumentParser("train")
 parser.add_argument("--input_data", type=str, help="input data")
 parser.add_argument("--output_train", type=str, help="output_train directory")
 args = parser.parse_args()
 print("Argument 1: %s" % args.input_data)
 print("Argument 2: %s" % args.output_train)
 if not (args.output_train is None):
    os.makedirs(args.output_train, exist_ok=True)
    print("%s created" % args.output_train)
--- a/how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/batch_scoring.py
+++ b/how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/batch_scoring.py
@@ -0,0 +1,119 @@
 # Copyright (c) Microsoft. All rights reserved.
 # Licensed under the MIT license.
 import os
 import argparse
 import datetime
 import time
 import tensorflow as tf
 from math import ceil
 import numpy as np
 import shutil
 from tensorflow.contrib.slim.python.slim.nets import inception_v3
 from azureml.core.model import Model
 slim = tf.contrib.slim
 parser = argparse.ArgumentParser(description="Start a tensorflow model serving")
 parser.add_argument('--model_name', dest="model_name", required=True)
 parser.add_argument('--label_dir', dest="label_dir", required=True)
 parser.add_argument('--dataset_path', dest="dataset_path", required=True)
 parser.add_argument('--output_dir', dest="output_dir", required=True)
 parser.add_argument('--batch_size', dest="batch_size", type=int, required=True)
 args = parser.parse_args()
 image_size = 299
 num_channel = 3
 # create output directory if it does not exist
 os.makedirs(args.output_dir, exist_ok=True)
 def get_class_label_dict(label_file):
    label = []
    proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()
    for l in proto_as_ascii_lines:
        label.append(l.rstrip())
    return label
 class DataIterator:
    def __init__(self, data_dir):
        self.file_paths = []
        image_list = os.listdir(data_dir)
        # total_size = len(image_list)
        self.file_paths = [data_dir + '/' + file_name.rstrip() for file_name in image_list]
        self.labels = [1 for file_name in self.file_paths]
    @property
    def size(self):
        return len(self.labels)
    def input_pipeline(self, batch_size):
        images_tensor = tf.convert_to_tensor(self.file_paths, dtype=tf.string)
        labels_tensor = tf.convert_to_tensor(self.labels, dtype=tf.int64)
        input_queue = tf.train.slice_input_producer([images_tensor, labels_tensor], shuffle=False)
        labels = input_queue[1]
        images_content = tf.read_file(input_queue[0])
        image_reader = tf.image.decode_jpeg(images_content, channels=num_channel, name="jpeg_reader")
        float_caster = tf.cast(image_reader, tf.float32)
        new_size = tf.constant([image_size, image_size], dtype=tf.int32)
        images = tf.image.resize_images(float_caster, new_size)
        images = tf.divide(tf.subtract(images, [0]), [255])
        image_batch, label_batch = tf.train.batch([images, labels], batch_size=batch_size, capacity=5 * batch_size)
        return image_batch
 def main(_):
    # start_time = datetime.datetime.now()
    label_file_name = os.path.join(args.label_dir, "labels.txt")
    label_dict = get_class_label_dict(label_file_name)
    classes_num = len(label_dict)
    test_feeder = DataIterator(data_dir=args.dataset_path)
    total_size = len(test_feeder.labels)
    count = 0
    # get model from model registry
    model_path = Model.get_model_path(args.model_name)
    with tf.Session() as sess:
        test_images = test_feeder.input_pipeline(batch_size=args.batch_size)
        with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
            input_images = tf.placeholder(tf.float32, [args.batch_size, image_size, image_size, num_channel])
            logits, _ = inception_v3.inception_v3(input_images,
                                                  num_classes=classes_num,
                                                  is_training=False)
            probabilities = tf.argmax(logits, 1)
        sess.run(tf.global_variables_initializer())
        sess.run(tf.local_variables_initializer())
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(sess=sess, coord=coord)
        saver = tf.train.Saver()
        saver.restore(sess, model_path)
        out_filename = os.path.join(args.output_dir, "result-labels.txt")
        with open(out_filename, "w") as result_file:
            i = 0
            while count < total_size and not coord.should_stop():
                test_images_batch = sess.run(test_images)
                file_names_batch = test_feeder.file_paths[i * args.batch_size:
                                                          min(test_feeder.size, (i + 1) * args.batch_size)]
                results = sess.run(probabilities, feed_dict={input_images: test_images_batch})
                new_add = min(args.batch_size, total_size - count)
                count += new_add
                i += 1
                for j in range(new_add):
                    result_file.write(os.path.basename(file_names_batch[j]) + ": " + label_dict[results[j]] + "\n")
                result_file.flush()
            coord.request_stop()
            coord.join(threads)
        # copy the file to artifacts
        shutil.copy(out_filename, "./outputs/")
        # Move the processed data out of the blob so that the next run can process the data.
 if __name__ == "__main__":
    tf.app.run()
--- a/how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.ipynb
@@ -5,7 +5,6 @@
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
@@ -13,7 +12,15 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "This notebook demonstrates how to run batch scoring job. __[Inception-V3 model](https://arxiv.org/abs/1512.00567)__  and unlabeled images from __[ImageNet](http://image-net.org/)__ dataset will be used. It registers a pretrained inception model in model registry then uses the model to do batch scoring on images in a blob container."
+        "# Using Azure Machine Learning Pipelines for batch prediction\n",
        "\n",
        "In this notebook we will demonstrate how to run a batch scoring job using Azure Machine Learning pipelines. Our example job will be to take an already-trained image classification model, and run that model on some unlabeled images. The image classification model that we'll use is the __[Inception-V3 model](https://arxiv.org/abs/1512.00567)__  and we'll run this model on unlabeled images from the __[ImageNet](http://image-net.org/)__ dataset. \n",
        "\n",
        "The outline of this notebook is as follows:\n",
        "\n",
        "- Register the pretrained inception model into the model registry. \n",
        "- Store the dataset images in a blob container.\n",
        "- Use the registered model to do batch scoring on the images in the data blob container."
      ]
    },
    {
@@ -21,7 +28,22 @@
      "metadata": {},
      "source": [
        "## Prerequisites\n",
-        "Make sure you go through the [00. Installation and Configuration](./00.configuration.ipynb) Notebook first if you haven't.\n"
+        "Make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import Experiment\n",
        "from azureml.core.compute import AmlCompute, ComputeTarget\n",
        "from azureml.core.datastore import Datastore\n",
        "from azureml.core.runconfig import CondaDependencies, RunConfiguration\n",
        "from azureml.data.data_reference import DataReference\n",
        "from azureml.pipeline.core import Pipeline, PipelineData\n",
        "from azureml.pipeline.steps import PythonScriptStep"
      ]
    },
    {
@@ -31,19 +53,34 @@
      "outputs": [],
      "source": [
        "import os\n",
-        "from azureml.core import Workspace, Run, Experiment\n",
+        "from azureml.core import Workspace\n",
        "\n",
        "ws = Workspace.from_config()\n",
        "print('Workspace name: ' + ws.name, \n",
        "      'Azure region: ' + ws.location, \n",
        "      'Subscription id: ' + ws.subscription_id, \n",
-        "      'Resource group: ' + ws.resource_group, sep = '\\n')\n",
+        "      'Resource group: ' + ws.resource_group, sep = '\\n')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Set up machine learning resources"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Set up datastores\n",
        "First, let\u00e2\u20ac\u2122s access the datastore that has the model, labels, and images. \n",
        "\n",
-        "# Also create a Project and attach to Workspace\n",
+        "### Create a datastore that points to a blob container containing sample images\n",
        "scripts_folder = \"scripts\"\n",
        "\n",
-        "if not os.path.isdir(scripts_folder):\n",
+        "We have created a public blob container `sampledata` on an account named `pipelinedata`, containing images from the ImageNet evaluation set. In the next step, we create a datastore with the name `images_datastore`, which points to this container. In the call to `register_azure_blob_container` below, setting the `overwrite` flag to `True` overwrites any datastore that was created previously with that name. \n",
-        "    os.mkdir(scripts_folder)"
+        "\n",
        "This step can be changed to point to your blob container by providing your own `datastore_name`, `container_name`, and `account_name`."
      ]
    },
    {
@@ -52,19 +89,72 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "from azureml.core.compute import BatchAiCompute, ComputeTarget\n",
+        "account_name = \"pipelinedata\"\n",
-        "from azureml.core.datastore import Datastore\n",
+        "datastore_name=\"images_datastore\"\n",
-        "from azureml.data.data_reference import DataReference\n",
+        "container_name=\"sampledata\"\n",
-        "from azureml.pipeline.core import Pipeline, PipelineData\n",
+        "\n",
-        "from azureml.pipeline.steps import PythonScriptStep\n",
+        "batchscore_blob = Datastore.register_azure_blob_container(ws, \n",
-        "from azureml.core.runconfig import CondaDependencies, RunConfiguration"
+        "                      datastore_name=datastore_name, \n",
        "                      container_name= container_name, \n",
        "                      account_name=account_name, \n",
        "                      overwrite=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Create and attach Compute targets\n",
+        "Next, let\u00e2\u20ac\u2122s specify the default datastore for the outputs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def_data_store = ws.get_default_datastore()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Configure data references\n",
        "Now you need to add references to the data, as inputs to the appropriate pipeline steps in your pipeline. A data source in a pipeline is represented by a DataReference object. The DataReference object points to data that lives in, or is accessible from, a datastore. We need DataReference objects corresponding to the following: the directory containing the input images, the directory in which the pretrained model is stored, the directory containing the labels, and the output directory."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "input_images = DataReference(datastore=batchscore_blob, \n",
        "                             data_reference_name=\"input_images\",\n",
        "                             path_on_datastore=\"batchscoring/images\",\n",
        "                             mode=\"download\"\n",
        "                            )\n",
        "model_dir = DataReference(datastore=batchscore_blob, \n",
        "                          data_reference_name=\"input_model\",\n",
        "                          path_on_datastore=\"batchscoring/models\",\n",
        "                          mode=\"download\"                          \n",
        "                         )\n",
        "label_dir = DataReference(datastore=batchscore_blob, \n",
        "                          data_reference_name=\"input_labels\",\n",
        "                          path_on_datastore=\"batchscoring/labels\",\n",
        "                          mode=\"download\"                          \n",
        "                         )\n",
        "output_dir = PipelineData(name=\"scores\", \n",
        "                          datastore=def_data_store, \n",
        "                          output_path_on_compute=\"batchscoring/results\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create and attach Compute targets\n",
        "Use the below code to create and attach Compute targets. "
      ]
    },
@@ -74,171 +164,49 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "# Batch AI compute\n",
+        "# choose a name for your cluster\n",
-        "cluster_name = \"gpu-cluster\"\n",
+        "aml_compute_name = os.environ.get(\"AML_COMPUTE_NAME\", \"gpu-cluster\")\n",
-        "try:\n",
+        "cluster_min_nodes = os.environ.get(\"AML_COMPUTE_MIN_NODES\", 0)\n",
-        "    cluster = BatchAiCompute(ws, cluster_name)\n",
+        "cluster_max_nodes = os.environ.get(\"AML_COMPUTE_MAX_NODES\", 1)\n",
-        "    print(\"found existing cluster.\")\n",
+        "vm_size = os.environ.get(\"AML_COMPUTE_SKU\", \"STANDARD_NC6\")\n",
-        "except:\n",
+        "\n",
-        "    print(\"creating new cluster\")\n",
+        "\n",
-        "    provisioning_config = BatchAiCompute.provisioning_configuration(vm_size = \"STANDARD_NC6\",\n",
+        "if aml_compute_name in ws.compute_targets:\n",
-        "                                                                    autoscale_enabled = True,\n",
+        "    compute_target = ws.compute_targets[aml_compute_name]\n",
-        "                                                                    cluster_min_nodes = 0, \n",
+        "    if compute_target and type(compute_target) is AmlCompute:\n",
-        "                                                                    cluster_max_nodes = 1)\n",
+        "        print('found compute target. just use it. ' + aml_compute_name)\n",
        "else:\n",
        "    print('creating a new compute target...')\n",
        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = vm_size, # NC6 is GPU-enabled\n",
        "                                                                vm_priority = 'lowpriority', # optional\n",
        "                                                                min_nodes = cluster_min_nodes, \n",
        "                                                                max_nodes = cluster_max_nodes)\n",
        "\n",
        "    # create the cluster\n",
-        "    cluster = ComputeTarget.create(ws, cluster_name, provisioning_config)\n",
+        "    compute_target = ComputeTarget.create(ws, aml_compute_name, provisioning_config)\n",
-        "    cluster.wait_for_completion(show_output=True)"
+        "    \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 Azure Machine Learning Compute  status, use get_status()\n",
        "    print(compute_target.get_status().serialize())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "# Python scripts to run"
+        "## Prepare the Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "Python scripts that run the batch scoring. `batchai_score.py` takes input images in `dataset_path`, pretrained models in `model_dir` and outputs a `results-label.txt` to `output_dir`."
+        "### Download the Model\n",
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%%writefile $scripts_folder/batchai_score.py\n",
        "import os\n",
        "import argparse\n",
        "import datetime,time\n",
        "import tensorflow as tf\n",
        "from math import ceil\n",
        "import numpy as np\n",
        "import shutil\n",
        "from tensorflow.contrib.slim.python.slim.nets import inception_v3\n",
        "from azureml.core.model import Model\n",
        "\n",
-        "slim = tf.contrib.slim\n",
+        "Download and extract the model from http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz to `\"models\"`"
        "\n",
        "parser = argparse.ArgumentParser(description=\"Start a tensorflow model serving\")\n",
        "parser.add_argument('--model_name', dest=\"model_name\", required=True)\n",
        "parser.add_argument('--label_dir', dest=\"label_dir\", required=True)\n",
        "parser.add_argument('--dataset_path', dest=\"dataset_path\", required=True)\n",
        "parser.add_argument('--output_dir', dest=\"output_dir\", required=True)\n",
        "parser.add_argument('--batch_size', dest=\"batch_size\", type=int, required=True)\n",
        "\n",
        "args = parser.parse_args()\n",
        "\n",
        "image_size = 299\n",
        "num_channel = 3\n",
        "\n",
        "# create output directory if it does not exist\n",
        "os.makedirs(args.output_dir, exist_ok=True)\n",
        "\n",
        "def get_class_label_dict(label_file):\n",
        "  label = []\n",
        "  proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()\n",
        "  for l in proto_as_ascii_lines:\n",
        "    label.append(l.rstrip())\n",
        "  return label\n",
        "\n",
        "\n",
        "class DataIterator:\n",
        "    def __init__(self, data_dir):\n",
        "        self.file_paths = []\n",
        "        image_list = os.listdir(data_dir)\n",
        "        total_size = len(image_list)\n",
        "        self.file_paths = [data_dir + '/' + file_name.rstrip() for file_name in image_list ]\n",
        "\n",
        "        self.labels = [1 for file_name in self.file_paths]\n",
        "\n",
        "    @property\n",
        "    def size(self):\n",
        "        return len(self.labels)\n",
        "\n",
        "    def input_pipeline(self, batch_size):\n",
        "        images_tensor = tf.convert_to_tensor(self.file_paths, dtype=tf.string)\n",
        "        labels_tensor = tf.convert_to_tensor(self.labels, dtype=tf.int64)\n",
        "        input_queue = tf.train.slice_input_producer([images_tensor, labels_tensor], shuffle=False)\n",
        "        labels = input_queue[1]\n",
        "        images_content = tf.read_file(input_queue[0])\n",
        "\n",
        "        image_reader = tf.image.decode_jpeg(images_content, channels=num_channel, name=\"jpeg_reader\")\n",
        "        float_caster = tf.cast(image_reader, tf.float32)\n",
        "        new_size = tf.constant([image_size, image_size], dtype=tf.int32)\n",
        "        images = tf.image.resize_images(float_caster, new_size)\n",
        "        images = tf.divide(tf.subtract(images, [0]), [255])\n",
        "\n",
        "        image_batch, label_batch = tf.train.batch([images, labels], batch_size=batch_size, capacity=5 * batch_size)\n",
        "        return image_batch\n",
        "\n",
        "def main(_):\n",
        "    start_time = datetime.datetime.now()\n",
        "    label_file_name = os.path.join(args.label_dir, \"labels.txt\")\n",
        "    label_dict = get_class_label_dict(label_file_name)\n",
        "    classes_num = len(label_dict)\n",
        "    test_feeder = DataIterator(data_dir=args.dataset_path)\n",
        "    total_size = len(test_feeder.labels)\n",
        "    count = 0\n",
        "    # get model from model registry\n",
        "    model_path = Model.get_model_path(args.model_name)\n",
        "    with tf.Session() as sess:\n",
        "        test_images = test_feeder.input_pipeline(batch_size=args.batch_size)\n",
        "        with slim.arg_scope(inception_v3.inception_v3_arg_scope()):\n",
        "            input_images = tf.placeholder(tf.float32, [args.batch_size, image_size, image_size, num_channel])\n",
        "            logits, _ = inception_v3.inception_v3(input_images,\n",
        "                                                        num_classes=classes_num,\n",
        "                                                        is_training=False)\n",
        "            probabilities = tf.argmax(logits, 1)\n",
        "\n",
        "        sess.run(tf.global_variables_initializer())\n",
        "        sess.run(tf.local_variables_initializer())\n",
        "        coord = tf.train.Coordinator()\n",
        "        threads = tf.train.start_queue_runners(sess=sess, coord=coord)\n",
        "        saver = tf.train.Saver()\n",
        "        saver.restore(sess, model_path)\n",
        "        out_filename = os.path.join(args.output_dir, \"result-labels.txt\")\n",
        "        with open(out_filename, \"w\") as result_file:\n",
        "            i = 0\n",
        "            while count < total_size and not coord.should_stop():\n",
        "                test_images_batch = sess.run(test_images)\n",
        "                file_names_batch = test_feeder.file_paths[i*args.batch_size: min(test_feeder.size, (i+1)*args.batch_size)]\n",
        "                results = sess.run(probabilities, feed_dict={input_images: test_images_batch})\n",
        "                new_add = min(args.batch_size, total_size-count)\n",
        "                count += new_add\n",
        "                i += 1\n",
        "                for j in range(new_add):\n",
        "                    result_file.write(os.path.basename(file_names_batch[j]) + \": \" + label_dict[results[j]] + \"\\n\")\n",
        "                result_file.flush()\n",
        "            coord.request_stop()\n",
        "            coord.join(threads)\n",
        "            \n",
        "        # copy the file to artifacts\n",
        "        shutil.copy(out_filename, \"./outputs/\")\n",
        "        # Move the processed data out of the blob so that the next run can process the data.\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    tf.app.run()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Prepare Model and Input data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Download Model\n",
        "\n",
        "Download and extract model from http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz to `\"models\"`"
      ]
    },
    {
@@ -272,99 +240,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Create a datastore that points to blob container containing sample images\n",
+        "### Register the model with Workspace"
        "\n",
        "We have created a public blob container `sampledata` on an account named `pipelinedata` containing images from ImageNet evaluation set. In the next step, we create a datastore with name `images_datastore` that points to this container. The `overwrite=True` step overwrites any datastore that was created previously with that name. \n",
        "\n",
        "This step can be changed to point to your blob container by providing an additional `account_key` parameter with `account_name`. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "account_name = \"pipelinedata\"\n",
        "sample_data = Datastore.register_azure_blob_container(ws, datastore_name=\"images_datastore\", container_name=\"sampledata\", \n",
        "                                                        account_name=account_name, \n",
        "                                                        overwrite=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Output datastore"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We write the outputs to the default datastore"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "default_ds = ws.get_default_datastore()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Specify where the data is stored or will be written to"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "from azureml.data.data_reference import DataReference\n",
        "from azureml.pipeline.core import Pipeline, PipelineData\n",
        "from azureml.core import Datastore\n",
        "from azureml.core import Experiment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "input_images = DataReference(datastore=sample_data, \n",
        "                             data_reference_name=\"input_images\",\n",
        "                             path_on_datastore=\"batchscoring/images\",\n",
        "                             mode=\"download\"\n",
        "                            )\n",
        "model_dir = DataReference(datastore=sample_data, \n",
        "                          data_reference_name=\"input_model\",\n",
        "                          path_on_datastore=\"batchscoring/models\",\n",
        "                          mode=\"download\"                          \n",
        "                         )\n",
        "label_dir = DataReference(datastore=sample_data, \n",
        "                          data_reference_name=\"input_labels\",\n",
        "                          path_on_datastore=\"batchscoring/labels\",\n",
        "                          mode=\"download\"                          \n",
        "                         )\n",
        "output_dir = PipelineData(name=\"scores\", \n",
        "                          datastore_name=default_ds.name, \n",
        "                          output_path_on_compute=\"batchscoring/results\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Register the model with Workspace"
      ]
    },
    {
@@ -390,7 +266,40 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "# Specify environment to run the script"
+        "## Write your scoring script"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To do the scoring, we use a batch scoring script `batch_scoring.py`, which is located in the same directory that this notebook is in. You can take a look at this script to see how you might modify it for your custom batch scoring task.\n",
        "\n",
        "The python script `batch_scoring.py` takes input images, applies the image classification model to these images, and outputs a classification result to a results file.\n",
        "\n",
        "The script `batch_scoring.py` takes the following parameters:\n",
        "\n",
        "- `--model_name`: the name of the model being used, which is expected to be in the `model_dir` directory\n",
        "- `--label_dir` : the directory holding the `labels.txt` file \n",
        "- `--dataset_path`: the directory containing the input images\n",
        "- `--output_dir` : the script will run the model on the data and output a `results-label.txt` to this directory\n",
        "- `--batch_size` : the batch size used in running the model.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Build and run the batch scoring pipeline\n",
        "You have everything you need to build the pipeline. Let\u00e2\u20ac\u2122s put all these together."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "###  Specify the environment to run the script\n",
        "Specify the conda dependencies for your script. You will need this object when you create the pipeline step later on."
      ]
    },
    {
@@ -399,27 +308,23 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "cd = CondaDependencies.create(pip_packages=[\"tensorflow-gpu==1.4.0\", \"azureml-defaults\"])\n",
+        "from azureml.core.runconfig import DEFAULT_GPU_IMAGE\n",
        "\n",
        "cd = CondaDependencies.create(pip_packages=[\"tensorflow-gpu==1.10.0\", \"azureml-defaults\"])\n",
        "\n",
        "# Runconfig\n",
-        "batchai_run_config = RunConfiguration(conda_dependencies=cd)\n",
+        "amlcompute_run_config = RunConfiguration(conda_dependencies=cd)\n",
-        "batchai_run_config.environment.docker.enabled = True\n",
+        "amlcompute_run_config.environment.docker.enabled = True\n",
-        "batchai_run_config.environment.docker.gpu_support = True\n",
+        "amlcompute_run_config.environment.docker.gpu_support = True\n",
-        "batchai_run_config.environment.docker.base_image = \"microsoft/mmlspark:gpu-0.12\"\n",
+        "amlcompute_run_config.environment.docker.base_image = DEFAULT_GPU_IMAGE\n",
-        "batchai_run_config.environment.spark.precache_packages = False"
+        "amlcompute_run_config.environment.spark.precache_packages = False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Steps to run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Specify the parameters for your pipeline\n",
        "A subset of the parameters to the python script can be given as input when we re-run a `PublishedPipeline`. In the current example, we define `batch_size` taken by the script as such parameter."
      ]
    },
@@ -433,6 +338,14 @@
        "batch_size_param = PipelineParameter(name=\"param_batch_size\", default_value=20)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create the pipeline step\n",
        "Create the pipeline step using the script, environment configuration, and parameters. Specify the compute target you already attached to your workspace as the target of execution of the script. We will use PythonScriptStep to create the pipeline step."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -442,21 +355,28 @@
        "inception_model_name = \"inception_v3.ckpt\"\n",
        "\n",
        "batch_score_step = PythonScriptStep(\n",
-        "    name=\"batch ai scoring\",\n",
+        "    name=\"batch_scoring\",\n",
-        "    script_name=\"batchai_score.py\",\n",
+        "    script_name=\"batch_scoring.py\",\n",
        "    arguments=[\"--dataset_path\", input_images, \n",
        "               \"--model_name\", \"inception\",\n",
        "               \"--label_dir\", label_dir, \n",
        "               \"--output_dir\", output_dir, \n",
        "               \"--batch_size\", batch_size_param],\n",
-        "    target=cluster,\n",
+        "    compute_target=compute_target,\n",
        "    inputs=[input_images, label_dir],\n",
        "    outputs=[output_dir],\n",
-        "    runconfig=batchai_run_config,\n",
+        "    runconfig=amlcompute_run_config\n",
        "    source_directory=scripts_folder\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Run the pipeline\n",
        "At this point you can run the pipeline and examine the output it produced. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -471,7 +391,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "# Monitor run"
+        "### Monitor the run"
      ]
    },
    {
@@ -480,7 +400,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "from azureml.train.widgets import RunDetails\n",
+        "from azureml.widgets import RunDetails\n",
        "RunDetails(pipeline_run).show()"
      ]
    },
@@ -497,7 +417,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "# Download and review output"
+        "### Download and review output"
      ]
    },
    {
@@ -526,14 +446,15 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "# Publish a pipeline and rerun using a REST call"
+        "## Publish a pipeline and rerun using a REST call"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Create a published pipeline"
+        "### Create a published pipeline\n",
        "Once you are satisfied with the outcome of the run, you can publish the pipeline to run it with different input values later. When you publish a pipeline, you will get a REST endpoint that accepts invoking of the pipeline with the set of parameters you have already incorporated above using PipelineParameter."
      ]
    },
    {
@@ -543,7 +464,7 @@
      "outputs": [],
      "source": [
        "published_pipeline = pipeline_run.publish_pipeline(\n",
-        "    name=\"Inception v3 scoring\", description=\"Batch scoring using Inception v3 model\", version=\"1.0\")\n",
+        "    name=\"Inception_v3_scoring\", description=\"Batch scoring using Inception v3 model\", version=\"1.0\")\n",
        "\n",
        "published_id = published_pipeline.id"
      ]
@@ -552,14 +473,14 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Rerun using REST call"
+        "## Rerun the pipeline using the REST endpoint"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Get AAD token"
+        "### Get AAD token"
      ]
    },
    {
@@ -579,7 +500,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Run published pipeline using its REST endpoint"
+        "### Run published pipeline"
      ]
    },
    {
@@ -588,11 +509,12 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "from azureml.pipeline.core import PublishedPipeline\n",
+        "rest_endpoint = published_pipeline.endpoint\n",
        "\n",
        "rest_endpoint = PublishedPipeline.get_endpoint(published_id, ws)\n",
        "# specify batch size when running the pipeline\n",
-        "response = requests.post(rest_endpoint, headers=aad_token, json={\"param_batch_size\": 50})\n",
+        "response = requests.post(rest_endpoint, \n",
        "                         headers=aad_token, \n",
        "                         json={\"ExperimentName\": \"batch_scoring\",\n",
        "                               \"ParameterAssignments\": {\"param_batch_size\": 50}})\n",
        "run_id = response.json()[\"Id\"]"
      ]
    },
@@ -600,7 +522,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Monitor the new run"
+        "### Monitor the new run"
      ]
    },
    {
@@ -610,7 +532,7 @@
      "outputs": [],
      "source": [
        "from azureml.pipeline.core.run import PipelineRun\n",
-        "published_pipeline_run = PipelineRun(ws.experiments()[\"batch_scoring\"], run_id)\n",
+        "published_pipeline_run = PipelineRun(ws.experiments[\"batch_scoring\"], run_id)\n",
        "\n",
        "RunDetails(published_pipeline_run).show()"
      ]
@@ -637,7 +559,7 @@
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
+      "version": "3.6.7"
    }
  },
  "nbformat": 4,
--- a/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/neural_style.py
+++ b/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/neural_style.py
@@ -0,0 +1,187 @@
 # Copyright (c) Microsoft. All rights reserved.
 # Licensed under the MIT license.
 # Original source: https://github.com/pytorch/examples/blob/master/fast_neural_style/neural_style/neural_style.py
 import argparse
 import os
 import sys
 import re
 from PIL import Image
 import torch
 from torchvision import transforms
 def load_image(filename, size=None, scale=None):
    img = Image.open(filename)
    if size is not None:
        img = img.resize((size, size), Image.ANTIALIAS)
    elif scale is not None:
        img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), Image.ANTIALIAS)
    return img
 def save_image(filename, data):
    img = data.clone().clamp(0, 255).numpy()
    img = img.transpose(1, 2, 0).astype("uint8")
    img = Image.fromarray(img)
    img.save(filename)
 class TransformerNet(torch.nn.Module):
    def __init__(self):
        super(TransformerNet, self).__init__()
        # Initial convolution layers
        self.conv1 = ConvLayer(3, 32, kernel_size=9, stride=1)
        self.in1 = torch.nn.InstanceNorm2d(32, affine=True)
        self.conv2 = ConvLayer(32, 64, kernel_size=3, stride=2)
        self.in2 = torch.nn.InstanceNorm2d(64, affine=True)
        self.conv3 = ConvLayer(64, 128, kernel_size=3, stride=2)
        self.in3 = torch.nn.InstanceNorm2d(128, affine=True)
        # Residual layers
        self.res1 = ResidualBlock(128)
        self.res2 = ResidualBlock(128)
        self.res3 = ResidualBlock(128)
        self.res4 = ResidualBlock(128)
        self.res5 = ResidualBlock(128)
        # Upsampling Layers
        self.deconv1 = UpsampleConvLayer(128, 64, kernel_size=3, stride=1, upsample=2)
        self.in4 = torch.nn.InstanceNorm2d(64, affine=True)
        self.deconv2 = UpsampleConvLayer(64, 32, kernel_size=3, stride=1, upsample=2)
        self.in5 = torch.nn.InstanceNorm2d(32, affine=True)
        self.deconv3 = ConvLayer(32, 3, kernel_size=9, stride=1)
        # Non-linearities
        self.relu = torch.nn.ReLU()
    def forward(self, X):
        y = self.relu(self.in1(self.conv1(X)))
        y = self.relu(self.in2(self.conv2(y)))
        y = self.relu(self.in3(self.conv3(y)))
        y = self.res1(y)
        y = self.res2(y)
        y = self.res3(y)
        y = self.res4(y)
        y = self.res5(y)
        y = self.relu(self.in4(self.deconv1(y)))
        y = self.relu(self.in5(self.deconv2(y)))
        y = self.deconv3(y)
        return y
 class ConvLayer(torch.nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride):
        super(ConvLayer, self).__init__()
        reflection_padding = kernel_size // 2
        self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
        self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride)
    def forward(self, x):
        out = self.reflection_pad(x)
        out = self.conv2d(out)
        return out
 class ResidualBlock(torch.nn.Module):
    """ResidualBlock
    introduced in: https://arxiv.org/abs/1512.03385
    recommended architecture: http://torch.ch/blog/2016/02/04/resnets.html
    """
    def __init__(self, channels):
        super(ResidualBlock, self).__init__()
        self.conv1 = ConvLayer(channels, channels, kernel_size=3, stride=1)
        self.in1 = torch.nn.InstanceNorm2d(channels, affine=True)
        self.conv2 = ConvLayer(channels, channels, kernel_size=3, stride=1)
        self.in2 = torch.nn.InstanceNorm2d(channels, affine=True)
        self.relu = torch.nn.ReLU()
    def forward(self, x):
        residual = x
        out = self.relu(self.in1(self.conv1(x)))
        out = self.in2(self.conv2(out))
        out = out + residual
        return out
 class UpsampleConvLayer(torch.nn.Module):
    """UpsampleConvLayer
    Upsamples the input and then does a convolution. This method gives better results
    compared to ConvTranspose2d.
    ref: http://distill.pub/2016/deconv-checkerboard/
    """
    def __init__(self, in_channels, out_channels, kernel_size, stride, upsample=None):
        super(UpsampleConvLayer, self).__init__()
        self.upsample = upsample
        if upsample:
            self.upsample_layer = torch.nn.Upsample(mode='nearest', scale_factor=upsample)
        reflection_padding = kernel_size // 2
        self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
        self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride)
    def forward(self, x):
        x_in = x
        if self.upsample:
            x_in = self.upsample_layer(x_in)
        out = self.reflection_pad(x_in)
        out = self.conv2d(out)
        return out
 def stylize(args):
    device = torch.device("cuda" if args.cuda else "cpu")
    with torch.no_grad():
        style_model = TransformerNet()
        state_dict = torch.load(os.path.join(args.model_dir, args.style+".pth"))
        # remove saved deprecated running_* keys in InstanceNorm from the checkpoint
        for k in list(state_dict.keys()):
            if re.search(r'in\d+\.running_(mean|var)$', k):
                del state_dict[k]
        style_model.load_state_dict(state_dict)
        style_model.to(device)
        filenames = os.listdir(args.content_dir)
        for filename in filenames:
            print("Processing {}".format(filename))
            full_path = os.path.join(args.content_dir, filename)
            content_image = load_image(full_path, scale=args.content_scale)
            content_transform = transforms.Compose([
                transforms.ToTensor(),
                transforms.Lambda(lambda x: x.mul(255))
            ])
            content_image = content_transform(content_image)
            content_image = content_image.unsqueeze(0).to(device)
            output = style_model(content_image).cpu()
            output_path = os.path.join(args.output_dir, filename)
            save_image(output_path, output[0])
 def main():
    arg_parser = argparse.ArgumentParser(description="parser for fast-neural-style")
    arg_parser.add_argument("--content-scale", type=float, default=None,
                                 help="factor for scaling down the content image")
    arg_parser.add_argument("--model-dir", type=str, required=True,
                                 help="saved model to be used for stylizing the image.")
    arg_parser.add_argument("--cuda", type=int, required=True,
                                 help="set it to 1 for running on GPU, 0 for CPU")
    arg_parser.add_argument("--style", type=str,
                                 help="style name")
    arg_parser.add_argument("--content-dir", type=str, required=True,
            help="directory holding the images")
    arg_parser.add_argument("--output-dir", type=str, required=True,
            help="directory holding the output images")
    args = arg_parser.parse_args()
    if args.cuda and not torch.cuda.is_available():
        print("ERROR: cuda is not available, try running on CPU")
        sys.exit(1)
    os.makedirs(args.output_dir, exist_ok=True)
    stylize(args)
 if __name__ == "__main__":
    main()
--- a/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/neural_style_mpi.py
+++ b/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/neural_style_mpi.py
@@ -0,0 +1,207 @@
 # Original source: https://github.com/pytorch/examples/blob/master/fast_neural_style/neural_style/neural_style.py
 import argparse
 import os
 import sys
 import re
 from PIL import Image
 import torch
 from torchvision import transforms
 from mpi4py import MPI
 def load_image(filename, size=None, scale=None):
    img = Image.open(filename)
    if size is not None:
        img = img.resize((size, size), Image.ANTIALIAS)
    elif scale is not None:
        img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), Image.ANTIALIAS)
    return img
 def save_image(filename, data):
    img = data.clone().clamp(0, 255).numpy()
    img = img.transpose(1, 2, 0).astype("uint8")
    img = Image.fromarray(img)
    img.save(filename)
 class TransformerNet(torch.nn.Module):
    def __init__(self):
        super(TransformerNet, self).__init__()
        # Initial convolution layers
        self.conv1 = ConvLayer(3, 32, kernel_size=9, stride=1)
        self.in1 = torch.nn.InstanceNorm2d(32, affine=True)
        self.conv2 = ConvLayer(32, 64, kernel_size=3, stride=2)
        self.in2 = torch.nn.InstanceNorm2d(64, affine=True)
        self.conv3 = ConvLayer(64, 128, kernel_size=3, stride=2)
        self.in3 = torch.nn.InstanceNorm2d(128, affine=True)
        # Residual layers
        self.res1 = ResidualBlock(128)
        self.res2 = ResidualBlock(128)
        self.res3 = ResidualBlock(128)
        self.res4 = ResidualBlock(128)
        self.res5 = ResidualBlock(128)
        # Upsampling Layers
        self.deconv1 = UpsampleConvLayer(128, 64, kernel_size=3, stride=1, upsample=2)
        self.in4 = torch.nn.InstanceNorm2d(64, affine=True)
        self.deconv2 = UpsampleConvLayer(64, 32, kernel_size=3, stride=1, upsample=2)
        self.in5 = torch.nn.InstanceNorm2d(32, affine=True)
        self.deconv3 = ConvLayer(32, 3, kernel_size=9, stride=1)
        # Non-linearities
        self.relu = torch.nn.ReLU()
    def forward(self, X):
        y = self.relu(self.in1(self.conv1(X)))
        y = self.relu(self.in2(self.conv2(y)))
        y = self.relu(self.in3(self.conv3(y)))
        y = self.res1(y)
        y = self.res2(y)
        y = self.res3(y)
        y = self.res4(y)
        y = self.res5(y)
        y = self.relu(self.in4(self.deconv1(y)))
        y = self.relu(self.in5(self.deconv2(y)))
        y = self.deconv3(y)
        return y
 class ConvLayer(torch.nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride):
        super(ConvLayer, self).__init__()
        reflection_padding = kernel_size // 2
        self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
        self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride)
    def forward(self, x):
        out = self.reflection_pad(x)
        out = self.conv2d(out)
        return out
 class ResidualBlock(torch.nn.Module):
    """ResidualBlock
    introduced in: https://arxiv.org/abs/1512.03385
    recommended architecture: http://torch.ch/blog/2016/02/04/resnets.html
    """
    def __init__(self, channels):
        super(ResidualBlock, self).__init__()
        self.conv1 = ConvLayer(channels, channels, kernel_size=3, stride=1)
        self.in1 = torch.nn.InstanceNorm2d(channels, affine=True)
        self.conv2 = ConvLayer(channels, channels, kernel_size=3, stride=1)
        self.in2 = torch.nn.InstanceNorm2d(channels, affine=True)
        self.relu = torch.nn.ReLU()
    def forward(self, x):
        residual = x
        out = self.relu(self.in1(self.conv1(x)))
        out = self.in2(self.conv2(out))
        out = out + residual
        return out
 class UpsampleConvLayer(torch.nn.Module):
    """UpsampleConvLayer
    Upsamples the input and then does a convolution. This method gives better results
    compared to ConvTranspose2d.
    ref: http://distill.pub/2016/deconv-checkerboard/
    """
    def __init__(self, in_channels, out_channels, kernel_size, stride, upsample=None):
        super(UpsampleConvLayer, self).__init__()
        self.upsample = upsample
        if upsample:
            self.upsample_layer = torch.nn.Upsample(mode='nearest', scale_factor=upsample)
        reflection_padding = kernel_size // 2
        self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
        self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride)
    def forward(self, x):
        x_in = x
        if self.upsample:
            x_in = self.upsample_layer(x_in)
        out = self.reflection_pad(x_in)
        out = self.conv2d(out)
        return out
 def stylize(args, comm):
    rank = comm.Get_rank()
    size = comm.Get_size()
    device = torch.device("cuda" if args.cuda else "cpu")
    with torch.no_grad():
        style_model = TransformerNet()
        state_dict = torch.load(os.path.join(args.model_dir, args.style + ".pth"))
        # remove saved deprecated running_* keys in InstanceNorm from the checkpoint
        for k in list(state_dict.keys()):
            if re.search(r'in\d+\.running_(mean|var)$', k):
                del state_dict[k]
        style_model.load_state_dict(state_dict)
        style_model.to(device)
        filenames = os.listdir(args.content_dir)
        filenames = sorted(filenames)
        partition_size = len(filenames) // size
        partitioned_filenames = filenames[rank * partition_size: (rank + 1) * partition_size]
        print("RANK {} - is processing {} images out of the total {}".format(rank, len(partitioned_filenames),
                                                                             len(filenames)))
        output_paths = []
        for filename in partitioned_filenames:
            # print("Processing {}".format(filename))
            full_path = os.path.join(args.content_dir, filename)
            content_image = load_image(full_path, scale=args.content_scale)
            content_transform = transforms.Compose([
                transforms.ToTensor(),
                transforms.Lambda(lambda x: x.mul(255))
            ])
            content_image = content_transform(content_image)
            content_image = content_image.unsqueeze(0).to(device)
            output = style_model(content_image).cpu()
            output_path = os.path.join(args.output_dir, filename)
            save_image(output_path, output[0])
            output_paths.append(output_path)
        print("RANK {} - number of pre-aggregated output files {}".format(rank, len(output_paths)))
        output_paths_list = comm.gather(output_paths, root=0)
        if rank == 0:
            print("RANK {} - number of aggregated output files {}".format(rank, len(output_paths_list)))
            print("RANK {} - end".format(rank))
 def main():
    arg_parser = argparse.ArgumentParser(description="parser for fast-neural-style")
    arg_parser.add_argument("--content-scale", type=float, default=None,
                            help="factor for scaling down the content image")
    arg_parser.add_argument("--model-dir", type=str, required=True,
                            help="saved model to be used for stylizing the image.")
    arg_parser.add_argument("--cuda", type=int, required=True,
                            help="set it to 1 for running on GPU, 0 for CPU")
    arg_parser.add_argument("--style", type=str, help="style name")
    arg_parser.add_argument("--content-dir", type=str, required=True,
                            help="directory holding the images")
    arg_parser.add_argument("--output-dir", type=str, required=True,
                            help="directory holding the output images")
    args = arg_parser.parse_args()
    comm = MPI.COMM_WORLD
    if args.cuda and not torch.cuda.is_available():
        print("ERROR: cuda is not available, try running on CPU")
        sys.exit(1)
    os.makedirs(args.output_dir, exist_ok=True)
    stylize(args, comm)
 if __name__ == "__main__":
    main()
--- a/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer.ipynb
@@ -0,0 +1,609 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Neural style transfer on video\n",
        "Using modified code from `pytorch`'s neural style [example](https://pytorch.org/tutorials/advanced/neural_style_tutorial.html), we show how to setup a pipeline for doing style transfer on video. The pipeline has following steps:\n",
        "1. Split a video into images\n",
        "2. Run neural style on each image using one of the provided models (from `pytorch` pretrained models for this example).\n",
        "3. Stitch the image back into a video."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Prerequisites\n",
        "Make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Initialize Workspace\n",
        "\n",
        "Initialize a workspace object from persisted configuration."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "from azureml.core import Workspace, Experiment\n",
        "\n",
        "ws = Workspace.from_config()\n",
        "print('Workspace name: ' + ws.name, \n",
        "      'Azure region: ' + ws.location, \n",
        "      'Subscription id: ' + ws.subscription_id, \n",
        "      'Resource group: ' + ws.resource_group, sep = '\\n')\n",
        "\n",
        "scripts_folder = \"scripts_folder\"\n",
        "\n",
        "if not os.path.isdir(scripts_folder):\n",
        "    os.mkdir(scripts_folder)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.compute import AmlCompute, ComputeTarget\n",
        "from azureml.core.datastore import Datastore\n",
        "from azureml.data.data_reference import DataReference\n",
        "from azureml.pipeline.core import Pipeline, PipelineData\n",
        "from azureml.pipeline.steps import PythonScriptStep, MpiStep\n",
        "from azureml.core.runconfig import CondaDependencies, RunConfiguration\n",
        "from azureml.core.compute_target import ComputeTargetException"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Create or use existing compute"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# AmlCompute\n",
        "cpu_cluster_name = \"cpucluster\"\n",
        "try:\n",
        "    cpu_cluster = AmlCompute(ws, cpu_cluster_name)\n",
        "    print(\"found existing cluster.\")\n",
        "except ComputeTargetException:\n",
        "    print(\"creating new cluster\")\n",
        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_v2\",\n",
        "                                                                    max_nodes = 1)\n",
        "\n",
        "    # create the cluster\n",
        "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, provisioning_config)\n",
        "    cpu_cluster.wait_for_completion(show_output=True)\n",
        "    \n",
        "# AmlCompute\n",
        "gpu_cluster_name = \"gpucluster\"\n",
        "try:\n",
        "    gpu_cluster = AmlCompute(ws, gpu_cluster_name)\n",
        "    print(\"found existing cluster.\")\n",
        "except ComputeTargetException:\n",
        "    print(\"creating new cluster\")\n",
        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_NC6\",\n",
        "                                                                    max_nodes = 3)\n",
        "\n",
        "    # create the cluster\n",
        "    gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, provisioning_config)\n",
        "    gpu_cluster.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Python Scripts\n",
        "We use an edited version of `neural_style_mpi.py` (original is [here](https://github.com/pytorch/examples/blob/master/fast_neural_style/neural_style/neural_style_mpi.py)). Scripts to split and stitch the video are thin wrappers to calls to `ffmpeg`. \n",
        "\n",
        "We install `ffmpeg` through conda dependencies."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import shutil\n",
        "shutil.copy(\"neural_style_mpi.py\", scripts_folder)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%%writefile $scripts_folder/process_video.py\n",
        "import argparse\n",
        "import glob\n",
        "import os\n",
        "import subprocess\n",
        "\n",
        "parser = argparse.ArgumentParser(description=\"Process input video\")\n",
        "parser.add_argument('--input_video', required=True)\n",
        "parser.add_argument('--output_audio', required=True)\n",
        "parser.add_argument('--output_images', required=True)\n",
        "\n",
        "args = parser.parse_args()\n",
        "\n",
        "os.makedirs(args.output_audio, exist_ok=True)\n",
        "os.makedirs(args.output_images, exist_ok=True)\n",
        "\n",
        "subprocess.run(\"ffmpeg -i {} {}/video.aac\"\n",
        "              .format(args.input_video, args.output_audio),\n",
        "               shell=True, check=True\n",
        "              )\n",
        "\n",
        "subprocess.run(\"ffmpeg -i {} {}/%05d_video.jpg -hide_banner\"\n",
        "              .format(args.input_video, args.output_images),\n",
        "               shell=True, check=True\n",
        "              )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%%writefile $scripts_folder/stitch_video.py\n",
        "import argparse\n",
        "import os\n",
        "import subprocess\n",
        "\n",
        "parser = argparse.ArgumentParser(description=\"Process input video\")\n",
        "parser.add_argument('--images_dir', required=True)\n",
        "parser.add_argument('--input_audio', required=True)\n",
        "parser.add_argument('--output_dir', required=True)\n",
        "\n",
        "args = parser.parse_args()\n",
        "\n",
        "os.makedirs(args.output_dir, exist_ok=True)\n",
        "\n",
        "subprocess.run(\"ffmpeg -framerate 30 -i {}/%05d_video.jpg -c:v libx264 -profile:v high -crf 20 -pix_fmt yuv420p \"\n",
        "               \"-y {}/video_without_audio.mp4\"\n",
        "               .format(args.images_dir, args.output_dir),\n",
        "               shell=True, check=True\n",
        "              )\n",
        "\n",
        "subprocess.run(\"ffmpeg -i {}/video_without_audio.mp4 -i {}/video.aac -map 0:0 -map 1:0 -vcodec \"\n",
        "               \"copy -acodec copy -y {}/video_with_audio.mp4\"\n",
        "               .format(args.output_dir, args.input_audio, args.output_dir),\n",
        "               shell=True, check=True\n",
        "              )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# datastore for input video\n",
        "account_name = \"happypathspublic\"\n",
        "video_ds = Datastore.register_azure_blob_container(ws, \"videos\", \"videos\",\n",
        "                                            account_name=account_name, overwrite=True)\n",
        "\n",
        "# datastore for models\n",
        "models_ds = Datastore.register_azure_blob_container(ws, \"models\", \"styletransfer\", \n",
        "                                                        account_name=\"pipelinedata\", \n",
        "                                                        overwrite=True)\n",
        "                                                        \n",
        "# downloaded models from https://pytorch.org/tutorials/advanced/neural_style_tutorial.html are kept here\n",
        "models_dir = DataReference(data_reference_name=\"models\", datastore=models_ds, \n",
        "                           path_on_datastore=\"saved_models\", mode=\"download\")\n",
        "\n",
        "# the default blob store attached to a workspace\n",
        "default_datastore = ws.get_default_datastore()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Sample video"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "orangutan_video = DataReference(datastore=video_ds,\n",
        "                            data_reference_name=\"video\",\n",
        "                            path_on_datastore=\"orangutan.mp4\", mode=\"download\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "cd = CondaDependencies()\n",
        "\n",
        "cd.add_channel(\"conda-forge\")\n",
        "cd.add_conda_package(\"ffmpeg\")\n",
        "\n",
        "cd.add_channel(\"pytorch\")\n",
        "cd.add_conda_package(\"pytorch\")\n",
        "cd.add_conda_package(\"torchvision\")\n",
        "\n",
        "# Runconfig\n",
        "amlcompute_run_config = RunConfiguration(conda_dependencies=cd)\n",
        "amlcompute_run_config.environment.docker.enabled = True\n",
        "amlcompute_run_config.environment.docker.gpu_support = True\n",
        "amlcompute_run_config.environment.docker.base_image = \"pytorch/pytorch\"\n",
        "amlcompute_run_config.environment.spark.precache_packages = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ffmpeg_audio = PipelineData(name=\"ffmpeg_audio\", datastore=default_datastore)\n",
        "ffmpeg_images = PipelineData(name=\"ffmpeg_images\", datastore=default_datastore)\n",
        "processed_images = PipelineData(name=\"processed_images\", datastore=default_datastore)\n",
        "output_video = PipelineData(name=\"output_video\", datastore=default_datastore)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Define tweakable parameters to pipeline\n",
        "These parameters can be changed when the pipeline is published and rerun from a REST call"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.pipeline.core.graph import PipelineParameter\n",
        "# create a parameter for style (one of \"candy\", \"mosaic\", \"rain_princess\", \"udnie\") to transfer the images to\n",
        "style_param = PipelineParameter(name=\"style\", default_value=\"mosaic\")\n",
        "# create a parameter for the number of nodes to use in step no. 2 (style transfer)\n",
        "nodecount_param = PipelineParameter(name=\"nodecount\", default_value=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "split_video_step = PythonScriptStep(\n",
        "    name=\"split video\",\n",
        "    script_name=\"process_video.py\",\n",
        "    arguments=[\"--input_video\", orangutan_video,\n",
        "               \"--output_audio\", ffmpeg_audio,\n",
        "               \"--output_images\", ffmpeg_images,\n",
        "              ],\n",
        "    compute_target=cpu_cluster,\n",
        "    inputs=[orangutan_video],\n",
        "    outputs=[ffmpeg_images, ffmpeg_audio],\n",
        "    runconfig=amlcompute_run_config,\n",
        "    source_directory=scripts_folder\n",
        ")\n",
        "\n",
        "# create a MPI step for distributing style transfer step across multiple nodes in AmlCompute \n",
        "# using 'nodecount_param' PipelineParameter\n",
        "distributed_style_transfer_step = MpiStep(\n",
        "    name=\"mpi style transfer\",\n",
        "    script_name=\"neural_style_mpi.py\",\n",
        "    arguments=[\"--content-dir\", ffmpeg_images,\n",
        "               \"--output-dir\", processed_images,\n",
        "               \"--model-dir\", models_dir,\n",
        "               \"--style\", style_param,\n",
        "               \"--cuda\", 1\n",
        "              ],\n",
        "    compute_target=gpu_cluster,\n",
        "    node_count=nodecount_param, \n",
        "    process_count_per_node=1,\n",
        "    inputs=[models_dir, ffmpeg_images],\n",
        "    outputs=[processed_images],\n",
        "    pip_packages=[\"mpi4py\", \"torch\", \"torchvision\"],\n",
        "    runconfig=amlcompute_run_config,\n",
        "    use_gpu=True,\n",
        "    source_directory=scripts_folder\n",
        ")\n",
        "\n",
        "stitch_video_step = PythonScriptStep(\n",
        "    name=\"stitch\",\n",
        "    script_name=\"stitch_video.py\",\n",
        "    arguments=[\"--images_dir\", processed_images, \n",
        "               \"--input_audio\", ffmpeg_audio, \n",
        "               \"--output_dir\", output_video],\n",
        "    compute_target=cpu_cluster,\n",
        "    inputs=[processed_images, ffmpeg_audio],\n",
        "    outputs=[output_video],\n",
        "    runconfig=amlcompute_run_config,\n",
        "    source_directory=scripts_folder\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Run the pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "pipeline = Pipeline(workspace=ws, steps=[stitch_video_step])\n",
        "# submit the pipeline and provide values for the PipelineParameters used in the pipeline\n",
        "pipeline_run = Experiment(ws, 'style_transfer').submit(pipeline, pipeline_params={\"style\": \"mosaic\", \"nodecount\": 3})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Monitor using widget"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(pipeline_run).show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Downloads the video in `output_video` folder"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Download output video"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def download_video(run, target_dir=None):\n",
        "    stitch_run = run.find_step_run(\"stitch\")[0]\n",
        "    port_data = stitch_run.get_output_data(\"output_video\")\n",
        "    port_data.download(target_dir, show_progress=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "pipeline_run.wait_for_completion()\n",
        "download_video(pipeline_run, \"output_video_mosaic\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Publish pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "published_pipeline = pipeline_run.publish_pipeline(\n",
        "    name=\"batch score style transfer\", description=\"style transfer\", version=\"1.0\")\n",
        "\n",
        "published_id = published_pipeline.id"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#  Re-run pipeline through REST calls for other styles"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Get AAD token"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.authentication import AzureCliAuthentication\n",
        "import requests\n",
        "\n",
        "cli_auth = AzureCliAuthentication()\n",
        "aad_token = cli_auth.get_authentication_header()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Get endpoint URL"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "rest_endpoint = published_pipeline.endpoint"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Send request and monitor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# run the pipeline using PipelineParameter values style='candy' and nodecount=2\n",
        "response = requests.post(rest_endpoint, \n",
        "                         headers=aad_token,\n",
        "                         json={\"ExperimentName\": \"style_transfer\",\n",
        "                               \"ParameterAssignments\": {\"style\": \"candy\", \"nodecount\": 2}})                         \n",
        "run_id = response.json()[\"Id\"]\n",
        "\n",
        "from azureml.pipeline.core.run import PipelineRun\n",
        "published_pipeline_run_candy = PipelineRun(ws.experiments[\"style_transfer\"], run_id)\n",
        "\n",
        "RunDetails(published_pipeline_run_candy).show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# run the pipeline using PipelineParameter values style='rain_princess' and nodecount=3\n",
        "response = requests.post(rest_endpoint, \n",
        "                         headers=aad_token,\n",
        "                         json={\"ExperimentName\": \"style_transfer\",\n",
        "                               \"ParameterAssignments\": {\"style\": \"rain_princess\", \"nodecount\": 3}})    \n",
        "run_id = response.json()[\"Id\"]\n",
        "\n",
        "published_pipeline_run_rain = PipelineRun(ws.experiments[\"style_transfer\"], run_id)\n",
        "\n",
        "RunDetails(published_pipeline_run_rain).show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# run the pipeline using PipelineParameter values style='udnie' and nodecount=4\n",
        "response = requests.post(rest_endpoint, \n",
        "                         headers=aad_token,\n",
        "                         json={\"ExperimentName\": \"style_transfer\",\n",
        "                               \"ParameterAssignments\": {\"style\": \"udnie\", \"nodecount\": 4}})   \n",
        "run_id = response.json()[\"Id\"]\n",
        "\n",
        "published_pipeline_run_udnie = PipelineRun(ws.experiments[\"style_transfer\"], run_id)\n",
        "\n",
        "RunDetails(published_pipeline_run_udnie).show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Download output from re-run"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "published_pipeline_run_candy.wait_for_completion()\n",
        "published_pipeline_run_rain.wait_for_completion()\n",
        "published_pipeline_run_udnie.wait_for_completion()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "download_video(published_pipeline_run_candy, target_dir=\"output_video_candy\")\n",
        "download_video(published_pipeline_run_rain, target_dir=\"output_video_rain_princess\")\n",
        "download_video(published_pipeline_run_udnie, target_dir=\"output_video_udnie\")"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "hichando"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/training-with-deep-learning/README.md
+++ b/how-to-use-azureml/training-with-deep-learning/README.md
@@ -0,0 +1,16 @@
 ## Azure Machine Learning service training examples
 These examples show you:
 1. [How to use the Estimator pattern in Azure ML](how-to-use-estimator)
 2. [Train using TensorFlow Estimator and tune hyperparameters using Hyperdrive](train-hyperparameter-tune-deploy-with-tensorflow)
 3. [Train using Keras and tune hyperparameters using Hyperdrive](train-hyperparameter-tune-deploy-with-keras)
 4. [Train using Pytorch Estimator and tune hyperparameters using Hyperdrive](train-hyperparameter-tune-deploy-with-pytorch)
 5. [Distributed training using TensorFlow and Parameter Server](distributed-tensorflow-with-parameter-server)
 6. [Distributed training using TensorFlow and Horovod](distributed-tensorflow-with-horovod)
 7. [Distributed training using Pytorch and Horovod](distributed-pytorch-with-horovod)
 8. [Distributed training using CNTK and custom Docker image](distributed-cntk-with-custom-docker)
 9. [Export run history records to Tensorboard](export-run-history-to-tensorboard)
 10. [Use TensorBoard to monitor training execution](tensorboard)
 Learn more about how to use `Estimator` class to  [train deep neural networks with Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-ml-models).
--- a/how-to-use-azureml/training-with-deep-learning/distributed-cntk-with-custom-docker/cntk_distr_mnist.py
+++ b/how-to-use-azureml/training-with-deep-learning/distributed-cntk-with-custom-docker/cntk_distr_mnist.py
--- a/how-to-use-azureml/training-with-deep-learning/distributed-cntk-with-custom-docker/distributed-cntk-with-custom-docker.ipynb
+++ b/how-to-use-azureml/training-with-deep-learning/distributed-cntk-with-custom-docker/distributed-cntk-with-custom-docker.ipynb
@@ -13,7 +13,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "# 06. Distributed CNTK using custom docker images\n",
+        "# Distributed CNTK using custom docker images\n",
        "In this tutorial, you will train a CNTK model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using a custom docker image and distributed training."
      ]
    },
@@ -23,7 +23,7 @@
      "source": [
        "## Prerequisites\n",
        "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
-        "* Go through the [00.configuration.ipynb]() notebook to:\n",
+        "* Go through the [configuration notebook](../../../configuration.ipynb) to:\n",
        "    * install the AML SDK\n",
        "    * create a workspace and its configuration file (`config.json`)"
      ]
@@ -59,6 +59,7 @@
      "outputs": [],
      "source": [
        "from azureml.telemetry import set_diagnostics_collection\n",
        "\n",
        "set_diagnostics_collection(send_diagnostics=True)"
      ]
    },
@@ -68,7 +69,7 @@
      "source": [
        "## Initialize workspace\n",
        "\n",
-        "Initialize a [Workspace](https://review.docs.microsoft.com/en-us/azure/machine-learning/service/concept-azure-machine-learning-architecture?branch=release-ignite-aml#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
+        "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
      ]
    },
    {
@@ -90,10 +91,12 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "## Create a remote compute target\n",
+        "## Create or Attach existing AmlCompute\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an [Azure Batch AI](https://docs.microsoft.com/azure/batch-ai/overview) cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
        "\n",
-        "**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process."
+        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
        "\n",
        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) 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."
      ]
    },
    {
@@ -102,7 +105,7 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "from azureml.core.compute import ComputeTarget, BatchAiCompute\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "# choose a name for your cluster\n",
@@ -113,18 +116,16 @@
        "    print('Found existing compute target.')\n",
        "except ComputeTargetException:\n",
        "    print('Creating a new compute target...')\n",
-        "    compute_config = BatchAiCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6',\n",
-        "                                                                autoscale_enabled=True,\n",
+        "                                                           max_nodes=4)\n",
        "                                                                cluster_min_nodes=0, \n",
        "                                                                cluster_max_nodes=4)\n",
        "\n",
        "    # create the cluster\n",
        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
        "\n",
        "    compute_target.wait_for_completion(show_output=True)\n",
        "\n",
-        "    # Use the 'status' property to get a detailed status for the current cluster. \n",
+        "# use get_status() to get a detailed status for the current AmlCompute\n",
-        "    print(compute_target.status.serialize())"
+        "print(compute_target.get_status().serialize())"
      ]
    },
    {
@@ -243,6 +244,7 @@
      "outputs": [],
      "source": [
        "import shutil\n",
        "\n",
        "shutil.copy('cntk_distr_mnist.py', project_folder)"
      ]
    },
@@ -280,10 +282,10 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "from azureml.train.estimator import *\n",
+        "from azureml.train.estimator import Estimator\n",
        "\n",
        "script_params = {\n",
-        "    '--num_epochs': 50,\n",
+        "    '--num_epochs': 20,\n",
        "    '--data_dir': ds_data.as_mount(),\n",
        "    '--output_dir': './outputs'\n",
        "}\n",
@@ -324,7 +326,7 @@
      "outputs": [],
      "source": [
        "run = experiment.submit(estimator)\n",
-        "print(run.get_details())"
+        "print(run)"
      ]
    },
    {
@@ -341,7 +343,8 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "from azureml.train.widgets import RunDetails\n",
+        "from azureml.widgets import RunDetails\n",
        "\n",
        "RunDetails(run).show()"
      ]
    },
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
hning86	f8d4fec978	Keras Sample	2019-02-14 16:55:43 -05:00
hning86	0fdab91b97	dockefile reorg	2019-01-31 09:21:06 -05:00
hning86	b54be912d8	dockerfiles added	2019-01-30 17:04:18 -05:00
Roope Astala	0514eee64b	Merge pull request #182 from rastala/master version 1.0.10	2019-01-28 18:10:20 -05:00
Roope Astala	4b6e34fdc0	Update train-within-notebook.ipynb	2019-01-28 18:09:36 -05:00
Roope Astala	e01216d85b	Update configuration.ipynb	2019-01-28 18:08:41 -05:00
Roope Astala	b00f75edd8	version 1.0.10	2019-01-28 15:30:17 -05:00
Hai Ning	06aba388c6	Update azure-ml-with-nvidia-rapids.ipynb	2019-01-24 10:09:31 -05:00
Roope Astala	3018461dfc	Merge pull request #176 from rastala/master update tutorials	2019-01-22 14:25:28 -05:00
Roope Astala	0d91f2d697	update tutorials	2019-01-22 14:24:31 -05:00
Roope Astala	a14cb635f0	Merge pull request #175 from rastala/master RAPIDS sample	2019-01-22 13:44:55 -05:00
Roope Astala	88f6a966cc	RAPIDS sample	2019-01-22 13:32:59 -05:00
Hai Ning	4f76a844c6	Update README.md	2019-01-18 01:18:44 -05:00
Hai Ning	c1573ff949	Update NBSETUP.md	2019-01-18 01:15:53 -05:00
Hai Ning	d1b18b3771	Update NBSETUP.md	2019-01-18 01:09:13 -05:00
Roope Astala	e1a948f4cd	Merge pull request #168 from rastala/master version 1.0.8	2019-01-14 12:14:02 -08:00
Roope Astala	3ca40c0817	version 1.0.8	2019-01-14 15:13:30 -05:00
Roope Astala	f724cb4d9b	Merge pull request #166 from jeff-shepherd/master Fixed broken links in tutorials	2019-01-08 12:01:50 -08:00
Jeff Shepherd	094b4b3b13	Fixed broken links in tutorials	2019-01-08 11:58:03 -08:00
Roope Astala	d09942f521	Merge pull request #165 from rastala/master databricks update	2019-01-08 09:24:11 -08:00
Roope Astala	0c9e527174	databricks update	2019-01-08 12:23:15 -05:00
Roope Astala	e2640e54da	Merge pull request #160 from rastala/master Create aml-pipelines-concept.png	2019-01-02 12:03:13 -08:00
Roope Astala	d348baf8a1	Create aml-pipelines-concept.png	2019-01-02 15:02:25 -05:00
Roope Astala	b41e11e30d	Merge pull request #159 from jeff-shepherd/master Removed databricks notebook link	2019-01-02 11:56:15 -08:00
Jeff Shepherd	c1aa951867	Removed databricks notebook link	2019-01-02 11:45:52 -08:00
Roope Astala	5fe5f06e07	Merge pull request #158 from rastala/master Create Databricks_AMLSDK_1-4_6.dbc	2019-01-02 10:52:24 -08:00
Roope Astala	e8a09c49b1	Create Databricks_AMLSDK_1-4_6.dbc	2019-01-02 13:51:29 -05:00
Roope Astala	fb6a73a790	Merge pull request #145 from rastala/master fix databricks	2018-12-20 13:11:17 -08:00
Roope Astala	c2968b6526	fix databricks	2018-12-20 16:10:27 -05:00
Roope Astala	2ac62ae1ed	Merge pull request #144 from rastala/master Version 1.0.6	2018-12-20 12:43:17 -08:00
Roope Astala	cad5d5c97c	more files	2018-12-20 15:42:03 -05:00
Roope Astala	d8cf73503e	update to version 1.0.6	2018-12-20 15:40:20 -05:00
Hai Ning	4a2d6d637a	Update README.md	2018-12-13 11:28:02 -05:00
Hai Ning	0348e54e21	Update README.md	2018-12-13 11:27:41 -05:00
Hai Ning	a97d147d01	Create README.md	2018-12-13 11:26:58 -05:00
Hai Ning	c4ceac032b	Update README.md	2018-12-08 10:11:11 -05:00
Hai Ning	1d3dff5634	Update README.md	2018-12-08 10:10:37 -05:00
Roope Astala	096dd424db	Merge pull request #125 from jeff-shepherd/master Added to troubleshooting section and fixed paths	2018-12-07 11:03:59 -08:00
Jeff Shepherd	fdefea5e82	Added to troubleshooting section	2018-12-07 10:55:45 -08:00
Roope Astala	15fb283b78	Merge pull request #124 from rastala/master add NBSETUP	2018-12-07 10:29:46 -08:00
Roope Astala	142514a255	add NBSETUP	2018-12-07 13:29:08 -05:00
Roope Astala	26678677b3	Merge pull request #122 from rastala/master Update distributed pytorch notebook	2018-12-07 07:00:04 -08:00
Roope Astala	2e2a943f5b	Update distributed pytorch notebook	2018-12-07 09:40:29 -05:00
Roope Astala	94ba3f3665	Merge pull request #117 from rastala/master update automl notebooks	2018-12-04 16:12:49 -08:00
rastala	da02f93fc2	update automl notebooks	2018-12-04 19:11:42 -05:00
Roope Astala	d39af379f8	Merge pull request #116 from rastala/master add adb readme	2018-12-04 15:34:49 -08:00
rastala	6304fb1eb1	add adb readme	2018-12-04 18:34:10 -05:00
Hai Ning	b35d14ab72	Update img-classification-part1-training.ipynb	2018-12-04 13:27:19 -05:00
Roope Astala	e791456f34	Merge pull request #115 from rastala/master Fix broken link	2018-12-04 08:52:17 -08:00
rastala	9b93e13426	another patch 3	2018-12-04 11:51:05 -05:00
rastala	3d640393aa	another patch 2	2018-12-04 11:47:06 -05:00
Roope Astala	34d50b0427	Merge pull request #114 from rastala/master another patch	2018-12-04 08:43:29 -08:00
rastala	060a53d256	another patch	2018-12-04 11:42:51 -05:00
Roope Astala	78fba3ceea	Merge pull request #113 from rastala/master notebook patches	2018-12-04 08:26:00 -08:00
rastala	01dc3d0a5b	notebook patches	2018-12-04 11:24:50 -05:00
Heather Spetalnick (Shapiro)	ce7ca94a9a	fix aks link	2018-12-04 10:33:07 -05:00
Hai Ning	b8877f1f92	Merge pull request #111 from Azure/jamiemaclennan-patch-1 Fix link	2018-12-04 10:30:22 -05:00
Hai Ning	b19ec15601	Merge pull request #112 from Azure/jamiemaclennan-patch-2 fix links	2018-12-04 10:29:50 -05:00
Jamie MacLennan	e99de11c25	fix links	2018-12-04 10:28:19 -05:00
Jamie MacLennan	212c2e8bf0	Fix link	2018-12-04 10:24:10 -05:00
Hai Ning	2aed0a32bf	Update README.md	2018-12-04 08:55:46 -05:00
Hai Ning	25456b84f0	Update train-within-notebook.ipynb	2018-12-04 08:52:23 -05:00
Hai Ning	897ae13de1	Update train-on-remote-vm.ipynb	2018-12-04 08:52:06 -05:00
Hai Ning	e329729e0f	Update train-on-local.ipynb	2018-12-04 08:51:45 -05:00
Hai Ning	aacdede890	Update logging-api.ipynb	2018-12-04 08:51:00 -05:00
Hai Ning	0471f2f8db	Update logging-api.ipynb	2018-12-04 08:49:46 -05:00
Hai Ning	a8a525e704	Update logging-api.ipynb	2018-12-04 08:48:56 -05:00
Hai Ning	e37f3fa206	Update README.md	2018-12-04 08:18:27 -05:00
Roope Astala	2c4d6b5188	Merge pull request #110 from rastala/master fix azure-databricks	2018-12-03 19:21:31 -08:00
rastala	008befcce2	fix azure-databricks	2018-12-03 22:20:06 -05:00
Roope Astala	b4895bf1f8	Merge pull request #109 from jeff-shepherd/master Added setup and configuration files	2018-12-03 16:46:42 -08:00
Jeff Shepherd	a27cdbd478	Added setup and configuration files	2018-12-03 16:36:14 -08:00
Roope Astala	a408f3f2cb	Merge pull request #108 from rastala/master big update	2018-12-03 17:47:29 -05:00
rastala	ab2de17978	one more file	2018-12-03 17:38:46 -05:00
rastala	a63f5084e0	big update 2	2018-12-03 17:38:20 -05:00
rastala	d26a4b0323	big update	2018-12-02 21:50:53 -05:00
rastala	3c49d861df	license updates	2018-12-02 21:47:13 -05:00
Roope Astala	613db3158d	Merge pull request #93 from yanrez/master Make pipeline notebooks links in readme	2018-11-28 12:33:12 -05:00
yanrez	c3a8c36297	Make pipeline notebooks links in readme	2018-11-22 17:13:31 -08:00
Roope Astala	e7ce245674	Merge pull request #92 from dipankar-ray/master updated pipeline notebooks with expanded tutorial	2018-11-22 10:15:55 -05:00
Dipankar Ray	ef5844fffd	updated pipeline notebooks with expanded tutorial	2018-11-21 20:00:07 -08:00
Roope Astala	e039b98ee6	Merge pull request #91 from rastala/master automl notebook update	2018-11-21 20:46:21 -05:00
rastala	05713689e0	automl notebook update	2018-11-21 20:45:17 -05:00
Roope Astala	7bb906b53c	Merge pull request #87 from rastala/master Update to version 0.1.80	2018-11-20 11:02:28 -05:00
rastala	5726fe3ddb	Version 0.1.80	2018-11-20 11:00:48 -05:00
rastala	d10b1fa796	Revert "Updated notebook folders" This reverts commit `06728004b6`.	2018-11-20 10:39:48 -05:00
rastala	d7127de03c	Revert "Update tutorials/README.md" This reverts commit `50787f4ccc`.	2018-11-20 10:39:34 -05:00
Roope Astala	50787f4ccc	Update tutorials/README.md	2018-11-19 13:35:11 -05:00
Roope Astala	06728004b6	Updated notebook folders	2018-11-19 13:28:49 -05:00
Roope Astala	f5bcc55fe3	Merge pull request #74 from yueguoguo/master Typo in README	2018-11-09 09:51:01 -05:00
Roope Astala	f23fb58200	Merge pull request #77 from rastala/master Fix autoscale	2018-11-09 09:47:46 -05:00
Roope Astala	dbce7b8db2	Fix autoscase	2018-11-09 09:47:01 -05:00
Roope Astala	303090adf6	Merge pull request #76 from rastala/master Update 00.configuration.ipynb	2018-11-09 09:33:07 -05:00
Roope Astala	b091d1f5f1	Update 00.configuration.ipynb Create computes in 00.configuration, and link to tutorial	2018-11-09 09:31:25 -05:00
Hai Ning	803d69c539	Update 03.train-hyperparameter-tune-deploy-with-tensorflow.ipynb	2018-11-07 13:54:11 -05:00
Zhang Le	37848e9686	Merge pull request #1 from yueguoguo/yueguoguo-patch-1 Typo in README	2018-11-07 13:18:31 +08:00
Zhang Le	7d9227441e	Typo in README Typo of `psutil`.	2018-11-07 13:17:53 +08:00
Roope Astala	21c454b0f2	Merge pull request #72 from rastala/master Add logging API notebook	2018-11-06 12:46:39 -05:00
Roope Astala	c7b0960ae4	Add logging API notebook	2018-11-06 12:46:05 -05:00
Roope Astala	14e11fefd6	Delete .gitignore	2018-11-06 12:31:53 -05:00
Roope Astala	4deaeb04cf	Delete 05.train-in-spark-checkpoint.ipynb	2018-11-06 12:31:32 -05:00
Roope Astala	ee78323df2	Delete 03.train-on-aci-checkpoint.ipynb	2018-11-06 12:31:18 -05:00
Roope Astala	89c2622938	Delete 02.train-on-local-checkpoint.ipynb	2018-11-06 12:31:03 -05:00
Roope Astala	96b352e3be	Delete 04.train-on-remote-vm-checkpoint.ipynb	2018-11-06 12:30:43 -05:00
Roope Astala	5280201f93	Merge pull request #70 from wchill/fix_macos_sigsegv Fix segfault under certain conditions when running AutoML pipelines on MacOS	2018-11-05 19:04:14 -05:00
Eric Ahn	3825fd2c10	Fix segfault under certain conditions on MacOS	2018-11-05 15:06:38 -08:00
Roope Astala	b936dd3505	Merge pull request #69 from rastala/master New SDK version 0.1.74	2018-11-05 15:28:40 -05:00
Roope Astala	7339c95ea0	New SDK version	2018-11-05 15:27:36 -05:00
Hai Ning	32102e2aac	Update pipeline-batch-scoring.ipynb	2018-11-02 14:18:38 -04:00
Hai Ning	a043769197	Update pr.md	2018-10-29 22:23:49 -04:00
Hai Ning	a0f3727cf4	Update pr.md	2018-10-29 22:23:39 -04:00
Roope Astala	0e8b42f8c7	Delete snowleopardgaze.jpg	2018-10-26 16:53:47 -04:00
hning86	2daafdbca1	logging api sample	2018-10-26 14:02:05 -04:00
Roope Astala	fec2e97310	Merge pull request #62 from rastala/master Fix link in 01 getting started	2018-10-26 10:27:42 -04:00
Roope Astala	1a79e53935	Fix link in 01 getting started	2018-10-26 10:26:38 -04:00
Hai Ning	900cc7a76b	remove json.loads	2018-10-25 13:03:10 -04:00
Roope Astala	3148e52258	Merge pull request #60 from rastala/master fix json output	2018-10-25 12:48:28 -04:00
Roope Astala	dda402db83	fix json output	2018-10-25 12:47:38 -04:00
Roope Astala	603f4a6434	Merge pull request #58 from rastala/master Tutorial fixes	2018-10-24 13:47:05 -04:00
Roope Astala	114449dd9b	Tutorial fixes	2018-10-24 13:45:15 -04:00
Roope Astala	de20b6c40e	Merge pull request #55 from Azure/sdgilley-patch-1 Update 03.auto-train-models.ipynb	2018-10-22 12:43:20 -04:00
Hai Ning	886ece1089	Update pr.md	2018-10-22 11:23:49 -04:00
Sheri Gilley	0dfe00d05a	Update 03.auto-train-models.ipynb fix link	2018-10-22 10:04:46 -05:00
hning86	7a6fb8067f	auto updated from HaiGPU	2018-10-22 01:50:11 -04:00
hning86	bb439ab2fd	removed empty folder	2018-10-22 01:41:05 -04:00
hning86	ea3abdde4f	auto updated from HaiGPU	2018-10-22 01:39:38 -04:00
Hai Ning	2e4eb8785c	Update pr.md	2018-10-18 15:29:26 -04:00
Hai Ning	bfccb07dae	Update pr.md	2018-10-18 15:27:36 -04:00
Hai Ning	94cd37e9fb	Update README.md	2018-10-18 14:49:28 -04:00
Hai Ning	cdeb4dddab	Update README.md	2018-10-18 14:47:44 -04:00
Hai Ning	e12637098a	Update README.md	2018-10-18 14:47:19 -04:00
Hai Ning	d5f8811f4f	YT cover	2018-10-18 14:46:08 -04:00
Hai Ning	92d36a2db4	Delete ytimg_png.PNG	2018-10-18 14:45:53 -04:00
Hai Ning	c5c76e8187	Update pr.md	2018-10-18 14:45:12 -04:00
Hai Ning	833d1d0f4e	Update pr.md	2018-10-18 14:44:59 -04:00
Hai Ning	dd0c0264a2	Update README.md	2018-10-18 14:43:15 -04:00
Hai Ning	52368bad81	Update README.md	2018-10-18 14:42:48 -04:00
Hai Ning	604f6c18be	Update README.md	2018-10-18 14:42:23 -04:00
Hai Ning	829bc297f2	Update README.md	2018-10-18 14:41:45 -04:00
Hai Ning	9e5101ea8c	Update README.md	2018-10-18 14:41:34 -04:00
Hai Ning	37e96f2ad6	youtube cover	2018-10-18 14:40:17 -04:00
Roope Astala	d0c9bb330a	Merge pull request #39 from cforbe/master Adding dataprep notebook	2018-10-18 12:39:01 -04:00
Colleen Forbes	b4c7932640	Update README.md	2018-10-17 15:44:30 -07:00
Roope Astala	8fed628390	Merge pull request #53 from rastala/master Update automl setup	2018-10-17 17:38:28 -04:00
rastala	d940aca06d	Update automl setup	2018-10-17 17:37:01 -04:00
Hai Ning	beb97b1d9f	Update README.md	2018-10-17 12:00:37 -04:00
Colleen	e7e9923cfb	updating README.md	2018-10-03 16:46:51 -07:00
Colleen	b5482fcd4b	Adding dataprep notebook	2018-10-03 09:58:55 -07:00
		`@@ -1 +0,0 @@`
			{"cells":[{"cell_type":"markdown","source":["Azure ML & Azure Databricks notebooks by Parashar Shah.\n\nCopyright (c) Microsoft Corporation. All rights reserved.\n\nLicensed under the MIT License."],"metadata":{}},{"cell_type":"markdown","source":["Please ensure you have run this notebook before proceeding."],"metadata":{}},{"cell_type":"markdown","source":["Now we support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package (during private preview). You can select the option to attach the library to all clusters or just one cluster.\n\nProvide this full string to install the SDK:\n\nazureml-sdk[databricks]"],"metadata":{}},{"cell_type":"code","source":["import azureml.core\n\n# Check core SDK version number - based on build number of preview/master.\nprint(\"SDK version:\", azureml.core.VERSION)"],"metadata":{},"outputs":[],"execution_count":4},{"cell_type":"code","source":["subscription_id = \"<your-subscription-id>\"\nresource_group = \"<your-existing-resource-group>\"\nworkspace_name = \"<a-new-or-existing-workspace; it is unrelated to Databricks workspace>\"\nworkspace_region = \"<your-resource group-region>\""],"metadata":{},"outputs":[],"execution_count":5},{"cell_type":"code","source":["# import the Workspace class and check the azureml SDK version\n# exist_ok checks if workspace exists or not.\n\nfrom azureml.core import Workspace\n\nws = Workspace.create(name = workspace_name,\n subscription_id = subscription_id,\n resource_group = resource_group, \n location = workspace_region,\n exist_ok=True)\n\nws.get_details()"],"metadata":{},"outputs":[],"execution_count":6},{"cell_type":"code","source":["ws = Workspace(workspace_name = workspace_name,\n subscription_id = subscription_id,\n resource_group = resource_group)\n\n# persist the subscription id, resource group name, and workspace name in aml_config/config.json.\nws.write_config()"],"metadata":{},"outputs":[],"execution_count":7},{"cell_type":"code","source":["%sh\ncat /databricks/driver/aml_config/config.json"],"metadata":{},"outputs":[],"execution_count":8},{"cell_type":"code","source":["# import the Workspace class and check the azureml SDK version\nfrom azureml.core import Workspace\n\nws = Workspace.from_config()\nprint('Workspace name: ' + ws.name, \n 'Azure region: ' + ws.location, \n 'Subscription id: ' + ws.subscription_id, \n 'Resource group: ' + ws.resource_group, sep = '\\n')"],"metadata":{},"outputs":[],"execution_count":9},{"cell_type":"code","source":["dbutils.notebook.exit(\"success\")"],"metadata":{},"outputs":[],"execution_count":10},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":11}],"metadata":{"name":"01.Installation_and_Configuration","notebookId":3874566296719377},"nbformat":4,"nbformat_minor":0}