Merge pull request #421 from csteegz/csteegz-add-warning

Add warning for using prediction client on azure notebooks
Merge pull request #431 from jeff-shepherd/master
2025-12-20 09:37:04 -05:00 · 2019-06-13 20:27:34 -04:00 · 2019-06-13 16:56:25 -04:00 · 2019-06-13 13:12:32 -07:00 · 2019-06-13 14:40:57 -04:00 · 2019-06-13 11:03:25 -07:00
383 changed files with 145376 additions and 16193 deletions
--- 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
@@ -1,808 +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.create(conda_packages=[\"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 = service.run(input_data = test_samples)\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()"
      ]
    }
  ],
  "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/02.train-on-local/.gitignore
+++ b/01.getting-started/02.train-on-local/.gitignore
@@ -1 +0,0 @@
 /samples/
--- a/01.getting-started/02.train-on-local/.ipynb_checkpoints/02.train-on-local-checkpoint.ipynb
+++ b/01.getting-started/02.train-on-local/.ipynb_checkpoints/02.train-on-local-checkpoint.ipynb
@@ -1,477 +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": [
    "# 02. Train locally\n",
    "* Create or load workspace.\n",
    "* Create scripts locally.\n",
    "* Create `train.py` in a folder, along with a `my.lib` file.\n",
    "* Configure & execute a local run in a user-managed Python environment.\n",
    "* Configure & execute a local run in a system-managed Python environment.\n",
    "* Configure & execute a local run in a Docker environment.\n",
    "* Query run metrics to find the best model\n",
    "* Register model for operationalization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check core SDK version number\n",
    "import azureml.core\n",
    "\n",
    "print(\"SDK version:\", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Workspace\n",
    "\n",
    "Initialize a workspace object from persisted configuration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.workspace import Workspace\n",
    "\n",
    "ws = Workspace.from_config()\n",
    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create An Experiment\n",
    "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Experiment\n",
    "experiment_name = 'train-on-local'\n",
    "exp = Experiment(workspace=ws, name=experiment_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View `train.py`\n",
    "\n",
    "`train.py` is already created for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./train.py', 'r') as f:\n",
    "    print(f.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note `train.py` also references a `mylib.py` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./mylib.py', 'r') as f:\n",
    "    print(f.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure & Run\n",
    "### User-managed environment\n",
    "Below, we use a user-managed run, which means you are responsible to ensure all the necessary packages are available in the Python environment you choose to run the script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.runconfig import RunConfiguration\n",
    "\n",
    "# Editing a run configuration property on-fly.\n",
    "run_config_user_managed = RunConfiguration()\n",
    "\n",
    "run_config_user_managed.environment.python.user_managed_dependencies = True\n",
    "\n",
    "# You can choose a specific Python environment by pointing to a Python path \n",
    "#run_config.environment.python.interpreter_path = '/home/johndoe/miniconda3/envs/sdk2/bin/python'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Submit script to run in the user-managed environment\n",
    "Note whole script folder is submitted for execution, including the `mylib.py` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import ScriptRunConfig\n",
    "\n",
    "src = ScriptRunConfig(source_directory='./', script='train.py', run_config=run_config_user_managed)\n",
    "run = exp.submit(src)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get run history details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Block to wait till run finishes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### System-managed environment\n",
    "You can also ask the system to build a new conda environment and execute your scripts in it. The environment is built once and will be reused in subsequent executions as long as the conda dependencies remain unchanged. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.runconfig import RunConfiguration\n",
    "from azureml.core.conda_dependencies import CondaDependencies\n",
    "\n",
    "run_config_system_managed = RunConfiguration()\n",
    "\n",
    "run_config_system_managed.environment.python.user_managed_dependencies = False\n",
    "run_config_system_managed.auto_prepare_environment = True\n",
    "\n",
    "# Specify conda dependencies with scikit-learn\n",
    "cd = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
    "run_config_system_managed.environment.python.conda_dependencies = cd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Submit script to run in the system-managed environment\n",
    "A new conda environment is built based on the conda dependencies object. If you are running this for the first time,  this might take up to 5 mninutes. But this conda environment is reused so long as you don't change the conda dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "src = ScriptRunConfig(source_directory=\"./\", script='train.py', run_config=run_config_system_managed)\n",
    "run = exp.submit(src)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get run history details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Block and wait till run finishes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run.wait_for_completion(show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Docker-based execution\n",
    "**IMPORTANT**: You must have Docker engine installed locally in order to use this execution mode. If your kernel is already running in a Docker container, such as **Azure Notebooks**, this mode will **NOT** work.\n",
    "\n",
    "You can also ask the system to pull down a Docker image and execute your scripts in it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_config_docker = RunConfiguration()\n",
    "run_config_docker.environment.python.user_managed_dependencies = False\n",
    "run_config_docker.auto_prepare_environment = True\n",
    "run_config_docker.environment.docker.enabled = True\n",
    "run_config_docker.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
    "\n",
    "# Specify conda dependencies with scikit-learn\n",
    "cd = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
    "run_config_docker.environment.python.conda_dependencies = cd\n",
    "\n",
    "src = ScriptRunConfig(source_directory=\"./\", script='train.py', run_config=run_config_docker)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Submit script to run in the system-managed environment\n",
    "A new conda environment is built based on the conda dependencies object. If you are running this for the first time, this might take up to 5 mninutes. But this conda environment is reused so long as you don't change the conda dependencies.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import subprocess\n",
    "\n",
    "# Check if Docker is installed and Linux containers are enables\n",
    "if subprocess.run(\"docker -v\", shell=True) == 0:\n",
    "    out = subprocess.check_output(\"docker system info\", shell=True, encoding=\"ascii\").split(\"\\n\")\n",
    "    if not \"OSType: linux\" in out:\n",
    "        print(\"Switch Docker engine to use Linux containers.\")\n",
    "    else:\n",
    "        run = exp.submit(src)\n",
    "else:\n",
    "    print(\"Docker engine not installed.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get run history details\n",
    "run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query run metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "query history",
     "get metrics"
    ]
   },
   "outputs": [],
   "source": [
    "# get all metris logged in the run\n",
    "run.get_metrics()\n",
    "metrics = run.get_metrics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's find the model that has the lowest MSE value logged."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "best_alpha = metrics['alpha'][np.argmin(metrics['mse'])]\n",
    "\n",
    "print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n",
    "    min(metrics['mse']), \n",
    "    best_alpha\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also list all the files that are associated with this run record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run.get_file_names()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We know the model `ridge_0.40.pkl` is the best performing model from the eariler queries. So let's register it with the workspace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# supply a model name, and the full path to the serialized model file.\n",
    "model = run.register_model(model_name='best_ridge_model', model_path='./outputs/ridge_0.40.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(model.name, model.version, model.url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you can deploy this model following the example in the 01 notebook."
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "roastala"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/01.getting-started/02.train-on-local/02.train-on-local.ipynb
+++ b/01.getting-started/02.train-on-local/02.train-on-local.ipynb
@@ -1,477 +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": [
        "# 02. Train locally\n",
        "* Create or load workspace.\n",
        "* Create scripts locally.\n",
        "* Create `train.py` in a folder, along with a `my.lib` file.\n",
        "* Configure & execute a local run in a user-managed Python environment.\n",
        "* Configure & execute a local run in a system-managed Python environment.\n",
        "* Configure & execute a local run in a Docker environment.\n",
        "* Query run metrics to find the best model\n",
        "* Register model for operationalization."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Prerequisites\n",
        "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Check core SDK version number\n",
        "import azureml.core\n",
        "\n",
        "print(\"SDK version:\", azureml.core.VERSION)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Initialize Workspace\n",
        "\n",
        "Initialize a workspace object from persisted configuration."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.workspace import Workspace\n",
        "\n",
        "ws = Workspace.from_config()\n",
        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create An Experiment\n",
        "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import Experiment\n",
        "experiment_name = 'train-on-local'\n",
        "exp = Experiment(workspace=ws, name=experiment_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## View `train.py`\n",
        "\n",
        "`train.py` is already created for you."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "with open('./train.py', 'r') as f:\n",
        "    print(f.read())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Note `train.py` also references a `mylib.py` file."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "with open('./mylib.py', 'r') as f:\n",
        "    print(f.read())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Configure & Run\n",
        "### User-managed environment\n",
        "Below, we use a user-managed run, which means you are responsible to ensure all the necessary packages are available in the Python environment you choose to run the script."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.runconfig import RunConfiguration\n",
        "\n",
        "# Editing a run configuration property on-fly.\n",
        "run_config_user_managed = RunConfiguration()\n",
        "\n",
        "run_config_user_managed.environment.python.user_managed_dependencies = True\n",
        "\n",
        "# You can choose a specific Python environment by pointing to a Python path \n",
        "#run_config.environment.python.interpreter_path = '/home/johndoe/miniconda3/envs/sdk2/bin/python'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Submit script to run in the user-managed environment\n",
        "Note whole script folder is submitted for execution, including the `mylib.py` file."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import ScriptRunConfig\n",
        "\n",
        "src = ScriptRunConfig(source_directory='./', script='train.py', run_config=run_config_user_managed)\n",
        "run = exp.submit(src)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Get run history details"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Block to wait till run finishes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "run.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### System-managed environment\n",
        "You can also ask the system to build a new conda environment and execute your scripts in it. The environment is built once and will be reused in subsequent executions as long as the conda dependencies remain unchanged. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.runconfig import RunConfiguration\n",
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "\n",
        "run_config_system_managed = RunConfiguration()\n",
        "\n",
        "run_config_system_managed.environment.python.user_managed_dependencies = False\n",
        "run_config_system_managed.auto_prepare_environment = True\n",
        "\n",
        "# Specify conda dependencies with scikit-learn\n",
        "cd = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
        "run_config_system_managed.environment.python.conda_dependencies = cd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Submit script to run in the system-managed environment\n",
        "A new conda environment is built based on the conda dependencies object. If you are running this for the first time,  this might take up to 5 mninutes. But this conda environment is reused so long as you don't change the conda dependencies."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "src = ScriptRunConfig(source_directory=\"./\", script='train.py', run_config=run_config_system_managed)\n",
        "run = exp.submit(src)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Get run history details"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Block and wait till run finishes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "run.wait_for_completion(show_output = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Docker-based execution\n",
        "**IMPORTANT**: You must have Docker engine installed locally in order to use this execution mode. If your kernel is already running in a Docker container, such as **Azure Notebooks**, this mode will **NOT** work.\n",
        "\n",
        "You can also ask the system to pull down a Docker image and execute your scripts in it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "run_config_docker = RunConfiguration()\n",
        "run_config_docker.environment.python.user_managed_dependencies = False\n",
        "run_config_docker.auto_prepare_environment = True\n",
        "run_config_docker.environment.docker.enabled = True\n",
        "run_config_docker.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
        "\n",
        "# Specify conda dependencies with scikit-learn\n",
        "cd = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
        "run_config_docker.environment.python.conda_dependencies = cd\n",
        "\n",
        "src = ScriptRunConfig(source_directory=\"./\", script='train.py', run_config=run_config_docker)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Submit script to run in the system-managed environment\n",
        "A new conda environment is built based on the conda dependencies object. If you are running this for the first time, this might take up to 5 mninutes. But this conda environment is reused so long as you don't change the conda dependencies.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import subprocess\n",
        "\n",
        "# Check if Docker is installed and Linux containers are enables\n",
        "if subprocess.run(\"docker -v\", shell=True) == 0:\n",
        "    out = subprocess.check_output(\"docker system info\", shell=True, encoding=\"ascii\").split(\"\\n\")\n",
        "    if not \"OSType: linux\" in out:\n",
        "        print(\"Switch Docker engine to use Linux containers.\")\n",
        "    else:\n",
        "        run = exp.submit(src)\n",
        "else:\n",
        "    print(\"Docker engine not installed.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#Get run history details\n",
        "run"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "run.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Query run metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "query history",
          "get metrics"
        ]
      },
      "outputs": [],
      "source": [
        "# get all metris logged in the run\n",
        "run.get_metrics()\n",
        "metrics = run.get_metrics()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's find the model that has the lowest MSE value logged."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "best_alpha = metrics['alpha'][np.argmin(metrics['mse'])]\n",
        "\n",
        "print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n",
        "    min(metrics['mse']), \n",
        "    best_alpha\n",
        "))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can also list all the files that are associated with this run record"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "run.get_file_names()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We know the model `ridge_0.40.pkl` is the best performing model from the eariler queries. So let's register it with the workspace."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# supply a model name, and the full path to the serialized model file.\n",
        "model = run.register_model(model_name='best_ridge_model', model_path='./outputs/ridge_0.40.pkl')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print(model.name, model.version, model.url)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now you can deploy this model following the example in the 01 notebook."
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "roastala"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/01.getting-started/03.train-on-aci/.ipynb_checkpoints/03.train-on-aci-checkpoint.ipynb
+++ b/01.getting-started/03.train-on-aci/.ipynb_checkpoints/03.train-on-aci-checkpoint.ipynb
@@ -1,325 +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 (EXPERIMENTAL)\n",
    "\n",
    "* Create Workspace\n",
    "* Create Project\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": [
    "Create a folder to store the training script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "script_folder = './samples/train-on-aci'\n",
    "os.makedirs(script_folder, exist_ok = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remote execution on ACI\n",
    "\n",
    "Use `%%writefile` magic to write training code to `train.py` file under the project folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile $script_folder/train.py\n",
    "\n",
    "import os\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 azureml.core.run import Run\n",
    "from sklearn.externals import joblib\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "os.makedirs('./outputs', exist_ok=True)\n",
    "\n",
    "X, y = load_diabetes(return_X_y = True)\n",
    "\n",
    "run = Run.get_submitted_run()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
    "data = {\"train\": {\"X\": X_train, \"y\": y_train},\n",
    "        \"test\": {\"X\": X_test, \"y\": y_test}}\n",
    "\n",
    "# list of numbers from 0.0 to 1.0 with a 0.05 interval\n",
    "alphas = np.arange(0.0, 1.0, 0.05)\n",
    "\n",
    "for alpha in alphas:\n",
    "    # Use Ridge algorithm to create a regression model\n",
    "    reg = Ridge(alpha = alpha)\n",
    "    reg.fit(data[\"train\"][\"X\"], data[\"train\"][\"y\"])\n",
    "\n",
    "    preds = reg.predict(data[\"test\"][\"X\"])\n",
    "    mse = mean_squared_error(preds, data[\"test\"][\"y\"])\n",
    "    run.log('alpha', alpha)\n",
    "    run.log('mse', mse)\n",
    "    \n",
    "    model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)\n",
    "    with open(model_file_name, \"wb\") as file:\n",
    "        joblib.dump(value = reg, filename = 'outputs/' + model_file_name)\n",
    "\n",
    "    print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure for using ACI\n",
    "Linux-based ACI is available in `westus`, `eastus`, `westeurope`, `northeurope`, `westus2` and `southeastasia` 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 = 'eastus'\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",
    "#run_config.environment.docker.base_image = 'microsoft/mmlspark:plus-0.9.9'\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 = script_folder,\n",
    "                                    script= 'train.py',\n",
    "                                    run_config = run_config)\n",
    "\n",
    "run = experiment.submit(script_run_config)\n"
   ]
  },
  {
   "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": [
     "query history"
    ]
   },
   "outputs": [],
   "source": [
    "# Show run details\n",
    "run"
   ]
  },
  {
   "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",
    "))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "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/04.train-on-remote-vm/.ipynb_checkpoints/04.train-on-remote-vm-checkpoint.ipynb
+++ b/01.getting-started/04.train-on-remote-vm/.ipynb_checkpoints/04.train-on-remote-vm-checkpoint.ipynb
@@ -1,321 +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": [
    "# 04. Train in a remote VM (MLC managed DSVM)\n",
    "* Create Workspace\n",
    "* Create Project\n",
    "* Create `train.py` file\n",
    "* Create DSVM as Machine Learning Compute (MLC) resource\n",
    "* Configure & execute a run in a conda environment in the default miniconda Docker container on DSVM"
   ]
  },
  {
   "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",
    "\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": [
    "experiment_name = 'train-on-remote-vm'\n",
    "\n",
    "from azureml.core import Experiment\n",
    "\n",
    "exp = Experiment(workspace = ws, name = experiment_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View `train.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.py` in a cell to show the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./train.py', 'r') as training_script:\n",
    "    print(training_script.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Linux DSVM as a compute target\n",
    "\n",
    "**Note**: If creation fails with a message about Marketplace purchase eligibilty, go to portal.azure.com, start creating DSVM there, and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled it, you can exit without actually creating VM.\n",
    " \n",
    "**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you switch to a different port (such as 5022), you can append the port number to the address like the example below. [Read more](../../documentation/sdk/ssh-issue.md) on this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.compute import DsvmCompute\n",
    "from azureml.core.compute_target import ComputeTargetException\n",
    "\n",
    "compute_target_name = 'mydsvm'\n",
    "\n",
    "try:\n",
    "    dsvm_compute = DsvmCompute(workspace = ws, name = compute_target_name)\n",
    "    print('found existing:', dsvm_compute.name)\n",
    "except ComputeTargetException:\n",
    "    print('creating new.')\n",
    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
    "    dsvm_compute = DsvmCompute.create(ws, name = compute_target_name, provisioning_configuration = dsvm_config)\n",
    "    dsvm_compute.wait_for_completion(show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attach an existing Linux DSVM as a compute target\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    " from azureml.core.compute import RemoteCompute \n",
    " # if you want to connect using SSH key instead of username/password you can provide parameters private_key_file and private_key_passphrase \n",
    " dsvm_compute = RemoteCompute.attach(ws,name=\"attach-from-sdk6\",username=<username>,address=<ipaddress>,ssh_port=22,password=<password>)\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure & Run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configure a Docker run with new conda environment on the VM\n",
    "You can execute in a Docker container in the VM. If you choose this route, you don't need to install anything on the VM yourself. Azure ML execution service will take care of it for you."
   ]
  },
  {
   "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",
    "# Load the \"cpu-dsvm.runconfig\" file (created by the above attach operation) in memory\n",
    "run_config = RunConfiguration(framework = \"python\")\n",
    "\n",
    "# Set compute target to the Linux DSVM\n",
    "run_config.target = compute_target_name\n",
    "\n",
    "# Use Docker in the remote VM\n",
    "run_config.environment.docker.enabled = True\n",
    "\n",
    "# Use CPU base image from DockerHub\n",
    "run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
    "print('Base Docker image is:', run_config.environment.docker.base_image)\n",
    "\n",
    "# Ask system to provision a new one based on the conda_dependencies.yml file\n",
    "run_config.environment.python.user_managed_dependencies = False\n",
    "\n",
    "# Prepare the Docker and conda environment automatically when executingfor the first time.\n",
    "run_config.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",
    "Submit script to run in the Docker image in the remote VM. If you run this for the first time, the system will download the base image, layer in packages specified in the `conda_dependencies.yml` file on top of the base image, create a container and then execute the script in the container."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Run\n",
    "from azureml.core import ScriptRunConfig\n",
    "\n",
    "src = ScriptRunConfig(source_directory = '.', script = 'train.py', run_config = run_config)\n",
    "run = exp.submit(src)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View run history details"
   ]
  },
  {
   "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": [
    "### Find the best run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Clean up compute resource"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsvm_compute.delete()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/01.getting-started/05.train-in-spark/.ipynb_checkpoints/05.train-in-spark-checkpoint.ipynb
+++ b/01.getting-started/05.train-in-spark/.ipynb_checkpoints/05.train-in-spark-checkpoint.ipynb
@@ -1,257 +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-remote-vm'\n",
    "\n",
    "from azureml.core import Experiment\n",
    "\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": [
    "### Attach an HDI cluster\n",
    "To use HDI commpute target:\n",
    " 1. Create an Spark for HDI cluster in Azure. Here is some [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.\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",
    "\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_new = HDInsightCompute.attach(ws, \n",
    "                             name=\"hdi-attach\", \n",
    "                             address=\"hdi-ignite-demo-ssh.azurehdinsight.net\", \n",
    "                             ssh_port=22, \n",
    "                             username='<username>', \n",
    "                             password='<password>')\n",
    "\n",
    "except UserErrorException as e:\n",
    "    print(\"Caught = {}\".format(e.message))\n",
    "    print(\"Compute config already attached.\")\n",
    "    \n",
    "    \n",
    "hdi_compute_new.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",
    "# Load the \"cpu-dsvm.runconfig\" file (created by the above attach operation) in memory\n",
    "run_config = RunConfiguration(framework = \"python\")\n",
    "\n",
    "# Set compute target to the Linux DSVM\n",
    "run_config.target = hdi_compute.name\n",
    "\n",
    "# Use Docker in the remote VM\n",
    "# run_config.environment.docker.enabled = True\n",
    "\n",
    "# Use CPU base image from DockerHub\n",
    "# run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
    "# print('Base Docker image is:', run_config.environment.docker.base_image)\n",
    "\n",
    "# Ask system to provision a new one based on the conda_dependencies.yml file\n",
    "run_config.environment.python.user_managed_dependencies = False\n",
    "\n",
    "# Prepare the Docker and conda environment automatically when executingfor the first time.\n",
    "# run_config.prepare_environment = True\n",
    "\n",
    "# specify CondaDependencies obj\n",
    "# run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
    "# load the runconfig object from the \"myhdi.runconfig\" file generated by the attach operaton above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submit the script to HDI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "script_run_config = ScriptRunConfig(source_directory = '.',\n",
    "                                    script= 'train-spark.py',\n",
    "                                    run_config = run_config)\n",
    "run = experiment.submit(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": [
    "run.wait_for_completion(show_output = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get all metris logged in the run\n",
    "metrics = run.get_metrics()\n",
    "print(metrics)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- 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
@@ -1,453 +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",
    "        # 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 (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/1.0.15/Dockerfile
+++ b/Dockerfiles/1.0.15/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]==1.0.15"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.15" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.17/Dockerfile
+++ b/Dockerfiles/1.0.17/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]==1.0.17"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.17" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.18/Dockerfile
+++ b/Dockerfiles/1.0.18/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]==1.0.18"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.18" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- 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/1.0.21/Dockerfile
+++ b/Dockerfiles/1.0.21/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]==1.0.21"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.21" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.23/Dockerfile
+++ b/Dockerfiles/1.0.23/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]==1.0.23"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.23" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.30/Dockerfile
+++ b/Dockerfiles/1.0.30/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]==1.0.30"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.30" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.33/Dockerfile
+++ b/Dockerfiles/1.0.33/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]==1.0.33"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.33" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.41/Dockerfile
+++ b/Dockerfiles/1.0.41/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]==1.0.41"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.41" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.43/Dockerfile
+++ b/Dockerfiles/1.0.43/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]==1.0.43"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.43" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- 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/Licenses/sdk-license/LICENSE
+++ b/Licenses/sdk-license/LICENSE
@@ -0,0 +1,14 @@
 This software is made available to you on the condition that you agree to
 [your agreement][1] governing your use of Azure.
 If you do not have an existing agreement governing your use of Azure, you agree that 
 your agreement governing use of Azure is the [Microsoft Online Subscription Agreement][2]
 (which incorporates the [Online Services Terms][3]).
 By using the software you agree to these terms. This software may collect data
 that is transmitted to Microsoft. Please see the [Microsoft Privacy Statement][4]
 to learn more about how Microsoft processes personal data.
 [1]: https://azure.microsoft.com/en-us/support/legal/
 [2]: https://azure.microsoft.com/en-us/support/legal/subscription-agreement/
 [3]: http://www.microsoftvolumelicensing.com/DocumentSearch.aspx?Mode=3&DocumentTypeId=46
 [4]: http://go.microsoft.com/fwlink/?LinkId=248681 
--- a/Licenses/sdk-preview-license/LICENSE
+++ b/Licenses/sdk-preview-license/LICENSE
--- a/NBSETUP.md
+++ b/NBSETUP.md
@@ -0,0 +1,95 @@
 # Set up your notebook environment for Azure Machine Learning
 To run the notebooks in this repository use one of following options.
 ## **Option 1: Use Azure Notebooks**
 Azure Notebooks is a hosted Jupyter-based notebook service in the Azure cloud. Azure Machine Learning Python SDK is already pre-installed in the Azure Notebooks `Python 3.6` kernel.
 1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
 [Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks
 1. Follow the instructions in the [Configuration](configuration.ipynb) notebook to create and connect to a workspace
 1. Open one of the sample notebooks
    **Make sure the Azure Notebook kernel is set to `Python 3.6`** when you open a notebook by choosing Kernel > Change Kernel > Python 3.6 from the menus.
 ## **Option 2: Use your own notebook server**
 ### Quick installation
 We recommend you create a Python virtual environment ([Miniconda](https://conda.io/miniconda.html) preferred but [virtualenv](https://virtualenv.pypa.io/en/latest/) works too) and install the SDK in it.
 ```sh
 # install just the base SDK
 pip install azureml-sdk
 # clone the sample repoistory
 git clone https://github.com/Azure/MachineLearningNotebooks.git
 # below steps are optional
 # install the base SDK, Jupyter notebook server and tensorboard
 pip install azureml-sdk[notebooks,tensorboard]
 # install model explainability component
 pip install azureml-sdk[explain]
 # install automated ml components
 pip install azureml-sdk[automl]
 # install experimental features (not ready for production use)
 pip install azureml-sdk[contrib]
 ```
 Note the _extras_ (the keywords inside the square brackets) can be combined. For example:
 ```sh
 # install base SDK, Jupyter notebook and automated ml components
 pip install azureml-sdk[notebooks,automl]
 ```
 ### Full instructions
 [Install the Azure Machine Learning SDK](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python)
 Please make sure you start with the [Configuration](configuration.ipynb) notebook to create and connect to a workspace.
 ### Video walkthrough:
 [!VIDEO https://youtu.be/VIsXeTuW3FU]
 ## **Option 3: Use Docker**
 You need to have Docker engine installed locally and running. Open a command line window and type the following command. 
 __Note:__ We use version `1.0.10` below as an exmaple, but you can replace that with any available version number you like.
 ```sh
 # clone the sample repoistory
 git clone https://github.com/Azure/MachineLearningNotebooks.git
 # change current directory to the folder 
 # where Dockerfile of the specific SDK version is located.
 cd MachineLearningNotebooks/Dockerfiles/1.0.10
 # build a Docker image with the a name (azuremlsdk for example) 
 # and a version number tag (1.0.10 for example).
 # this can take several minutes depending on your computer speed and network bandwidth.
 docker build . -t azuremlsdk:1.0.10
 # launch the built Docker container which also automatically starts
 # a Jupyter server instance listening on port 8887 of the host machine
 docker run -it -p 8887:8887 azuremlsdk:1.0.10
 ```
 Now you can point your browser to http://localhost:8887. We recommend that you start from the `configuration.ipynb` notebook at the root directory.
 If you need additional Azure ML SDK components, you can either modify the Docker files before you build the Docker images to add additional steps, or install them through command line in the live container after you build the Docker image. For example:
 ```sh
 # install the core SDK and automated ml components
 pip install azureml-sdk[automl]
 # install the core SDK and model explainability component
 pip install azureml-sdk[explain]
 # install the core SDK and experimental components
 pip install azureml-sdk[contrib]
 ```
 Drag and Drop
 The image will be downloaded by Fatkun
--- a/README.md
+++ b/README.md
@@ -1,53 +1,69 @@
-For full documentation for Azure Machine Learning service, visit **https://aka.ms/aml-docs**.
+# Azure Machine Learning service example notebooks
 # Sample Notebooks for Azure Machine Learning service
-To run the notebooks in this repository use one of these methods:
+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.
-## Use Azure Notebooks - Jupyter based notebooks in the Azure cloud
+![Azure ML workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/service/media/overview-what-is-azure-ml/aml.png)
-1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
+## Quick installation
-[Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks.
+```sh
-1. Follow the instructions in the [00.configuration](00.configuration.ipynb) notebook to create and connect to a workspace.
+pip install azureml-sdk
 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:
 ```
-     pip install --upgrade azureml-dataprep
+Read more detailed instructions on [how to set up your environment](./NBSETUP.md) using Azure Notebook service, your own Jupyter notebook server, or Docker.
 ## How to navigate and use the example notebooks?
 If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, 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. 
 If you want to...
 * ...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).
 * ...prepare your data and do automated machine learning, start with regression tutorials: [Part 1 (Data Prep)](./tutorials/regression-part1-data-prep.ipynb) and [Part 2 (Automated ML)](./tutorials/regression-part2-automated-ml.ipynb).
 * ...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).
 * ...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).
 * ...deploy models as a 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 a 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](https://aka.ms/pl-batch-scoring).
 * ...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).
 ## Tutorials
 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
 - [Manage Azure ML Service](./how-to-use-azureml/manage-azureml-service) - Examples how to perform tasks, such as authenticate against Azure ML service in different ways.
 - [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)
 * Azure ML Data Prep SDK [overview](https://aka.ms/data-prep-sdk), [Python SDK reference](https://aka.ms/aml-data-prep-apiref), and [tutorials and how-tos](https://aka.ms/aml-data-prep-notebooks).
 ---
 ## Projects using Azure Machine Learning
 Visit following repos to see projects contributed by Azure ML users:
 - [AMLSamples](https://github.com/Azure/AMLSamples) Number of end-to-end examples, including face recognition, predictive maintenance, customer churn and sentiment analysis.
 - [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)
 ## Data/Telemetry 
 This repository collects usage data and sends it to Mircosoft to help improve our products and services. Read Microsoft's [privacy statement to learn more](https://privacy.microsoft.com/en-US/privacystatement)
 To opt out of tracking, please go to the raw markdown or .ipynb files and remove the following line of code:
 ```sh
    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/README.png)"
 ```
 This URL will be slightly different depending on the file. 
-1. Start your notebook server.
+ ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/README.png)
 1. Follow the instructions in the [00.configuration](00.configuration.ipynb) notebook to create and connect to a workspace.
 1. Open one of the sample notebooks.
 > Note: **Looking for automated machine learning samples?**
 > For your convenience, you can use an installation script instead of the steps below for the automated ML notebooks. Go to the [automl folder README](automl/README.md) and follow the instructions.  The script installs all  packages needed for notebooks in that folder.
 # Contributing
 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/03.auto-ml-remote-execution.ipynb
+++ b/automl/03.auto-ml-remote-execution.ipynb
@@ -1,485 +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 03: Remote Execution using DSVM (Ubuntu)\n",
        "\n",
        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
        "\n",
        "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you wiil learn how to:\n",
        "1. Create an `Experiment` in an existing `Workspace`.\n",
        "2. Attach an existing DSVM to a workspace.\n",
        "3. Configure AutoML using `AutoMLConfig`.\n",
        "4. Train the model using the DSVM.\n",
        "5. Explore the results.\n",
        "6. Test the best fitted model.\n",
        "\n",
        "In addition, this notebook showcases the following features:\n",
        "- **Parallel** executions for iterations\n",
        "- **Asynchronous** tracking of progress\n",
        "- **Cancellation** of individual iterations or the entire run\n",
        "- Retrieving models for any iteration or logged metric\n",
        "- Specifying AutoML settings as `**kwargs`\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create an Experiment\n",
        "\n",
        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import logging\n",
        "import os\n",
        "import random\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "from matplotlib.pyplot import imshow\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import datasets\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.train.automl import AutoMLConfig\n",
        "from azureml.train.automl.run import AutoMLRun"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "\n",
        "# Choose a name for the run history container in the workspace.\n",
        "experiment_name = 'automl-remote-dsvm4'\n",
        "project_folder = './sample_projects/automl-remote-dsvm4'\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace Name'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Project Directory'] = project_folder\n",
        "output['Experiment Name'] = experiment.name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "pd.DataFrame(data = output, index = ['']).T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Diagnostics\n",
        "\n",
        "Opt-in diagnostics for better experience, quality, and security of future releases."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.telemetry import set_diagnostics_collection\n",
        "set_diagnostics_collection(send_diagnostics = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create a Remote Linux DSVM\n",
        "**Note:** If creation fails with a message about Marketplace purchase eligibilty, start creation of a DSVM through the [Azure portal](https://portal.azure.com), and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled this setting, you can exit the portal without actually creating the DSVM, and creation of the DSVM through the notebook should work.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.compute import DsvmCompute\n",
        "\n",
        "dsvm_name = 'mydsvm'\n",
        "try:\n",
        "    dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
        "    print('Found an existing DSVM.')\n",
        "except:\n",
        "    print('Creating a new DSVM.')\n",
        "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
        "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
        "    dsvm_compute.wait_for_completion(show_output = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create Get Data File\n",
        "For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
        "In this example, the `get_data()` function returns data using scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "if not os.path.exists(project_folder):\n",
        "    os.makedirs(project_folder)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%%writefile $project_folder/get_data.py\n",
        "\n",
        "from sklearn import datasets\n",
        "from scipy import sparse\n",
        "import numpy as np\n",
        "\n",
        "def get_data():\n",
        "    \n",
        "    digits = datasets.load_digits()\n",
        "    X_train = digits.data[100:,:]\n",
        "    y_train = digits.target[100:]\n",
        "\n",
        "    return { \"X\" : X_train, \"y\" : y_train }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Configure AutoML <a class=\"anchor\" id=\"Instantiate-AutoML-Remote-DSVM\"></a>\n",
        "\n",
        "You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
        "\n",
        "**Note:** When using Remote DSVM, you can't pass Numpy arrays directly to the fit method.\n",
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
        "|**max_time_sec**|Time limit in seconds for each iteration.|\n",
        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
        "|**n_cross_validations**|Number of cross validation splits.|\n",
        "|**concurrent_iterations**|Maximum number of iterations to execute in parallel. This should be less than the number of cores on the DSVM.|"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_settings = {\n",
        "    \"max_time_sec\": 600,\n",
        "    \"iterations\": 20,\n",
        "    \"n_cross_validations\": 5,\n",
        "    \"primary_metric\": 'AUC_weighted',\n",
        "    \"preprocess\": False,\n",
        "    \"concurrent_iterations\": 2,\n",
        "    \"verbosity\": logging.INFO\n",
        "}\n",
        "\n",
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             debug_log = 'automl_errors.log',\n",
        "                             path = project_folder, \n",
        "                             compute_target = dsvm_compute,\n",
        "                             data_script = project_folder + \"/get_data.py\",\n",
        "                             **automl_settings\n",
        "                            )\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Note:** The first run on a new DSVM may take several minutes to prepare the environment."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train the Models\n",
        "\n",
        "Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run.\n",
        "\n",
        "In this example, we specify `show_output = False` to suppress console output while the run is in progress."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "remote_run = experiment.submit(automl_config, show_output = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Explore the Results\n",
        "\n",
        "#### Loading Executed Runs\n",
        "In case you need to load a previously executed run, enable the cell below and replace the `run_id` value."
      ]
    },
    {
      "cell_type": "raw",
      "metadata": {},
      "source": [
        "remote_run = AutoMLRun(experiment=experiment, run_id = 'AutoML_480d3ed6-fc94-44aa-8f4e-0b945db9d3ef')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Widget for Monitoring Runs\n",
        "\n",
        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
        "\n",
        "You can click on a pipeline to see run properties and output logs.  Logs are also available on the DSVM under `/tmp/azureml_run/{iterationid}/azureml-logs`\n",
        "\n",
        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.train.widgets import RunDetails\n",
        "RunDetails(remote_run).show() "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Wait until the run finishes.\n",
        "remote_run.wait_for_completion(show_output = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n",
        "#### Retrieve All Child Runs\n",
        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "children = list(remote_run.get_children())\n",
        "metricslist = {}\n",
        "for run in children:\n",
        "    properties = run.get_properties()\n",
        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
        "    metricslist[int(properties['iteration'])] = metrics\n",
        "\n",
        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
        "rundata"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Cancelling Runs\n",
        "\n",
        "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Cancel the ongoing experiment and stop scheduling new iterations.\n",
        "# remote_run.cancel()\n",
        "\n",
        "# Cancel iteration 1 and move onto iteration 2.\n",
        "# remote_run.cancel_iteration(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Retrieve the Best Model\n",
        "\n",
        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run, fitted_model = remote_run.get_output()\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Best Model Based on Any Other Metric\n",
        "Show the run and the model which has the smallest `log_loss` value:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "lookup_metric = \"log_loss\"\n",
        "best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Model from a Specific Iteration\n",
        "Show the run and the model from the third iteration:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "iteration = 3\n",
        "third_run, third_model = remote_run.get_output(iteration = iteration)\n",
        "print(third_run)\n",
        "print(third_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Test the Best Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n",
        "\n",
        "#### Load Test Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "digits = datasets.load_digits()\n",
        "X_test = digits.data[:10, :]\n",
        "y_test = digits.target[:10]\n",
        "images = digits.images[:10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Test Our Best Fitted Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Randomly select digits and test.\n",
        "for index in np.random.choice(len(y_test), 2, replace = False):\n",
        "    print(index)\n",
        "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
        "    label = y_test[index]\n",
        "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
        "    fig = plt.figure(1, figsize=(3,3))\n",
        "    ax1 = fig.add_axes((0,0,.8,.8))\n",
        "    ax1.set_title(title)\n",
        "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
        "    plt.show()"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "savitam"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/automl/04.auto-ml-remote-execution-text-data-blob-store.ipynb
+++ b/automl/04.auto-ml-remote-execution-text-data-blob-store.ipynb
@@ -1,491 +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": [
        "# Auto ML 04: Remote Execution with Text Data from Azure Blob Storage\n",
        "\n",
        "In this example we use the [Burning Man 2016 dataset](https://innovate.burningman.org/datasets-page/) to showcase how you can use AutoML to handle text data from Azure Blob Storage.\n",
        "\n",
        "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. Create an `Experiment` in an existing `Workspace`.\n",
        "2. Attach an existing DSVM to a workspace.\n",
        "3. Configure AutoML using `AutoMLConfig`.\n",
        "4. Train the model using the DSVM.\n",
        "5. Explore the results.\n",
        "6. Test the best fitted model.\n",
        "\n",
        "In addition this notebook showcases the following features\n",
        "- **Parallel** executions for iterations\n",
        "- **Asynchronous** tracking of progress\n",
        "- **Cancellation** of individual iterations or the entire run\n",
        "- Retrieving models for any iteration or logged metric\n",
        "- Specifying AutoML settings as `**kwargs`\n",
        "- Handling **text** data using the `preprocess` flag\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create an Experiment\n",
        "\n",
        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import logging\n",
        "import os\n",
        "import random\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "from matplotlib.pyplot import imshow\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import datasets\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.train.automl import AutoMLConfig\n",
        "from azureml.train.automl.run import AutoMLRun"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "\n",
        "# Choose a name for the run history container in the workspace.\n",
        "experiment_name = 'automl-remote-dsvm-blobstore'\n",
        "project_folder = './sample_projects/automl-remote-dsvm-blobstore'\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Project Directory'] = project_folder\n",
        "output['Experiment Name'] = experiment.name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "pd.DataFrame(data=output, index=['']).T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Diagnostics\n",
        "\n",
        "Opt-in diagnostics for better experience, quality, and security of future releases."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.telemetry import set_diagnostics_collection\n",
        "set_diagnostics_collection(send_diagnostics = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Attach a Remote Linux DSVM\n",
        "To use a remote Docker compute target:\n",
        "1. Create a Linux DSVM in Azure, following these [quick instructions](https://docs.microsoft.com/en-us/azure/machine-learning/desktop-workbench/how-to-create-dsvm-hdi). Make sure you use the Ubuntu flavor (not CentOS). Make sure that disk space is available under `/tmp` because AutoML creates files under `/tmp/azureml_run`s. The DSVM should have more cores than the number of parallel runs that you plan to enable. It should also have at least 4GB per core.\n",
        "2. Enter the IP address, user name and password below.\n",
        "\n",
        "**Note:** By default, SSH runs on port 22 and you don't need to change the port number below. If you've configured SSH to use a different port, change `dsvm_ssh_port` accordinglyaddress. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on changing SSH ports for security reasons."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.compute import RemoteCompute\n",
        "import time\n",
        "\n",
        "# Add your VM information below\n",
        "# If a compute with the specified compute_name already exists, it will be used and the dsvm_ip_addr, dsvm_ssh_port, \n",
        "# dsvm_username and dsvm_password will be ignored.\n",
        "compute_name  = 'mydsvm'\n",
        "dsvm_ip_addr  = '<<ip_addr>>'\n",
        "dsvm_ssh_port = 22\n",
        "dsvm_username = '<<username>>'\n",
        "dsvm_password = '<<password>>'\n",
        "\n",
        "if compute_name in ws.compute_targets:\n",
        "    print('Using existing compute.')\n",
        "    dsvm_compute = ws.compute_targets[compute_name]\n",
        "else:\n",
        "    RemoteCompute.attach(workspace=ws, name=compute_name, address=dsvm_ip_addr, username=dsvm_username, password=dsvm_password, ssh_port=dsvm_ssh_port)\n",
        "\n",
        "    while ws.compute_targets[compute_name].provisioning_state == 'Creating':\n",
        "        time.sleep(1)\n",
        "\n",
        "    dsvm_compute = ws.compute_targets[compute_name]\n",
        "    \n",
        "    if dsvm_compute.provisioning_state == 'Failed':\n",
        "        print('Attached failed.')\n",
        "        print(dsvm_compute.provisioning_errors)\n",
        "        dsvm_compute.delete()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create Get Data File\n",
        "For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
        "In this example, the `get_data()` function returns a [dictionary](README.md#getdata)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "if not os.path.exists(project_folder):\n",
        "    os.makedirs(project_folder)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%%writefile $project_folder/get_data.py\n",
        "\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "def get_data():\n",
        "    # Load Burning Man 2016 data.\n",
        "    df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
        "                     delimiter=\"\\t\", quotechar='\"')\n",
        "    # Get integer labels.\n",
        "    le = LabelEncoder()\n",
        "    le.fit(df[\"Label\"].values)\n",
        "    y = le.transform(df[\"Label\"].values)\n",
        "    X = df.drop([\"Label\"], axis=1)\n",
        "\n",
        "    X_train, _, y_train, _ = train_test_split(X, y, test_size = 0.1, random_state = 42)\n",
        "\n",
        "    return { \"X\" : X_train, \"y\" : y_train }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### View data\n",
        "\n",
        "You can execute the `get_data()` function locally to view the training data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%run  $project_folder/get_data.py\n",
        "data_dict = get_data()\n",
        "df = data_dict[\"X\"]\n",
        "y = data_dict[\"y\"]\n",
        "pd.set_option('display.max_colwidth', 15)\n",
        "df['Label'] = pd.Series(y, index=df.index)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Configure AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
        "\n",
        "You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
        "\n",
        "**Note:** When using Remote DSVM, you can't pass Numpy arrays directly to the fit method.\n",
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
        "|**max_time_sec**|Time limit in seconds for each iteration.|\n",
        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
        "|**n_cross_validations**|Number of cross validation splits.|\n",
        "|**concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of cores on the DSVM.|\n",
        "|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.|\n",
        "|**max_cores_per_iteration**|Indicates how many cores on the compute target would be used to train a single pipeline.<br>Default is *1*; you can set it to *-1* to use all cores.|"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_settings = {\n",
        "    \"max_time_sec\": 3600,\n",
        "    \"iterations\": 10,\n",
        "    \"n_cross_validations\": 5,\n",
        "    \"primary_metric\": 'AUC_weighted',\n",
        "    \"preprocess\": True,\n",
        "    \"max_cores_per_iteration\": 2\n",
        "}\n",
        "\n",
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             path = project_folder,\n",
        "                             compute_target = dsvm_compute,\n",
        "                             data_script = project_folder + \"/get_data.py\",\n",
        "                             **automl_settings\n",
        "                            )\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train the Models <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
        "\n",
        "Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "remote_run = experiment.submit(automl_config)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
        "#### Widget for Monitoring Runs\n",
        "\n",
        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
        "\n",
        "You can click on a pipeline to see run properties and output logs.  Logs are also available on the DSVM under `/tmp/azureml_run/{iterationid}/azureml-logs`\n",
        "\n",
        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.train.widgets import RunDetails\n",
        "RunDetails(remote_run).show() "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n",
        "#### Retrieve All Child Runs\n",
        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "children = list(remote_run.get_children())\n",
        "metricslist = {}\n",
        "for run in children:\n",
        "    properties = run.get_properties()\n",
        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
        "    metricslist[int(properties['iteration'])] = metrics\n",
        "\n",
        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
        "rundata"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Cancelling Runs\n",
        "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Cancel the ongoing experiment and stop scheduling new iterations.\n",
        "remote_run.cancel()\n",
        "\n",
        "# Cancel iteration 1 and move onto iteration 2.\n",
        "# remote_run.cancel_iteration(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Retrieve the Best Model\n",
        "\n",
        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run, fitted_model = remote_run.get_output()\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Best Model Based on Any Other Metric\n",
        "Show the run and the model which has the smallest `accuracy` value:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# lookup_metric = \"accuracy\"\n",
        "# best_run, fitted_model = remote_run.get_output(metric = lookup_metric)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Model from a Specific Iteration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "iteration = 0\n",
        "zero_run, zero_model = remote_run.get_output(iteration = iteration)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from pandas_ml import ConfusionMatrix\n",
        "\n",
        "df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
        "                     delimiter=\"\\t\", quotechar='\"')\n",
        "\n",
        "# get integer labels\n",
        "le = LabelEncoder()\n",
        "le.fit(df[\"Label\"].values)\n",
        "y = le.transform(df[\"Label\"].values)\n",
        "X = df.drop([\"Label\"], axis=1)\n",
        "\n",
        "_, X_test, _, y_test = train_test_split(X, y, test_size=0.1, random_state=42)\n",
        "\n",
        "\n",
        "ypred = fitted_model.predict(X_test.values)\n",
        "\n",
        "\n",
        "ypred_strings = le.inverse_transform(ypred)\n",
        "ytest_strings = le.inverse_transform(y_test)\n",
        "\n",
        "cm = ConfusionMatrix(ytest_strings, ypred_strings)\n",
        "\n",
        "print(cm)\n",
        "\n",
        "cm.plot()"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "savitam"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/automl/08.auto-ml-remote-execution-with-DataStore.ipynb
+++ b/automl/08.auto-ml-remote-execution-with-DataStore.ipynb
@@ -1,552 +0,0 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# AutoML 08: Remote Execution with DataStore\n",
        "\n",
        "This sample accesses a data file on a remote DSVM through DataStore. Advantages of using data store are:\n",
        "1. DataStore secures the access details.\n",
        "2. DataStore supports read, write to blob and file store\n",
        "3. AutoML natively supports copying data from DataStore to DSVM\n",
        "\n",
        "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you would see\n",
        "1. Storing data in DataStore.\n",
        "2. get_data returning data from DataStore.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create Experiment\n",
        "\n",
        "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import logging\n",
        "import os\n",
        "import random\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "from matplotlib.pyplot import imshow\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import datasets\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.train.automl import AutoMLConfig\n",
        "from azureml.train.automl.run import AutoMLRun"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "\n",
        "# choose a name for experiment\n",
        "experiment_name = 'automl-remote-datastore-file'\n",
        "# project folder\n",
        "project_folder = './sample_projects/automl-remote-dsvm-file'\n",
        "\n",
        "experiment=Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Project Directory'] = project_folder\n",
        "output['Experiment Name'] = experiment.name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "pd.DataFrame(data=output, index=['']).T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Diagnostics\n",
        "\n",
        "Opt-in diagnostics for better experience, quality, and security of future releases"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.telemetry import set_diagnostics_collection\n",
        "set_diagnostics_collection(send_diagnostics=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create a Remote Linux DSVM\n",
        "Note: If creation fails with a message about Marketplace purchase eligibilty, go to portal.azure.com, start creating DSVM there, and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled it, you can exit without actually creating VM.\n",
        "\n",
        "**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you can switch to a different port (such as 5022), you can append the port number to the address. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on this."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.compute import DsvmCompute\n",
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "compute_target_name = 'mydsvm'\n",
        "\n",
        "try:\n",
        "    dsvm_compute = DsvmCompute(workspace=ws, name=compute_target_name)\n",
        "    print('found existing:', dsvm_compute.name)\n",
        "except ComputeTargetException:\n",
        "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size=\"Standard_D2_v2\")\n",
        "    dsvm_compute = DsvmCompute.create(ws, name=compute_target_name, provisioning_configuration=dsvm_config)\n",
        "    dsvm_compute.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Copy data file to local\n",
        "\n",
        "Download the data file.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "mkdir data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
        "                     delimiter=\"\\t\", quotechar='\"')\n",
        "df.to_csv(\"data/data.tsv\", sep=\"\\t\", quotechar='\"', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Upload data to the cloud"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now make the data accessible remotely by uploading that data from your local machine into Azure so it can be accessed for remote training. The datastore is a convenient construct associated with your workspace for you to upload/download data, and interact with it from your remote compute targets. It is backed by Azure blob storage account.\n",
        "\n",
        "The data.tsv files are uploaded into a directory named data at the root of the datastore."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import Workspace, Datastore\n",
        "#blob_datastore = Datastore(ws, blob_datastore_name)\n",
        "ds = ws.get_default_datastore()\n",
        "print(ds.datastore_type, ds.account_name, ds.container_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# ds.upload_files(\"data.tsv\")\n",
        "ds.upload(src_dir='./data', target_path='data', overwrite=True, show_progress=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Configure & Run\n",
        "\n",
        "First let's create a DataReferenceConfigruation object to inform the system what data folder to download to the compute target.\n",
        "The path_on_compute should be an absolute path to ensure that the data files are downloaded only once.   The get_data method should use this same path to access the data files."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.runconfig import DataReferenceConfiguration\n",
        "dr = DataReferenceConfiguration(datastore_name=ds.name, \n",
        "                   path_on_datastore='data', \n",
        "                   path_on_compute='/tmp/azureml_runs',\n",
        "                   mode='download', # download files from datastore to compute target\n",
        "                   overwrite=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.runconfig import RunConfiguration\n",
        "\n",
        "# create a new RunConfig object\n",
        "conda_run_config = RunConfiguration(framework=\"python\")\n",
        "\n",
        "# Set compute target to the Linux DSVM\n",
        "conda_run_config.target = dsvm_compute\n",
        "# set the data reference of the run coonfiguration\n",
        "conda_run_config.data_references = {ds.name: dr}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create Get Data File\n",
        "For remote executions you should author a get_data.py file containing a get_data() function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
        "\n",
        "The *get_data()* function returns a [dictionary](README.md#getdata).\n",
        "\n",
        "The read_csv uses the path_on_compute value specified in the DataReferenceConfiguration call plus the path_on_datastore folder and then the actual file name."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "if not os.path.exists(project_folder):\n",
        "    os.makedirs(project_folder)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%%writefile $project_folder/get_data.py\n",
        "\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "import os\n",
        "from os.path import expanduser, join, dirname\n",
        "\n",
        "def get_data():\n",
        "    # Burning man 2016 data\n",
        "    df = pd.read_csv(\"/tmp/azureml_runs/data/data.tsv\", delimiter=\"\\t\", quotechar='\"')\n",
        "    # get integer labels\n",
        "    le = LabelEncoder()\n",
        "    le.fit(df[\"Label\"].values)\n",
        "    y = le.transform(df[\"Label\"].values)\n",
        "    X = df.drop([\"Label\"], axis=1)\n",
        "\n",
        "    X_train, _, y_train, _ = train_test_split(X, y, test_size=0.1, random_state=42)\n",
        "\n",
        "    return { \"X\" : X_train.values, \"y\" : y_train }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
        "\n",
        "You can specify automl_settings as **kwargs** as well. Also note that you can use the get_data() symantic for local excutions too. \n",
        "\n",
        "<i>Note: For Remote DSVM and Batch AI you cannot pass Numpy arrays directly to AutoMLConfig.</i>\n",
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
        "|**primary_metric**|This is the metric that you want to optimize.<br> Classification supports the following primary metrics <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
        "|**max_time_sec**|Time limit in seconds for each iteration|\n",
        "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data|\n",
        "|**n_cross_validations**|Number of cross validation splits|\n",
        "|**concurrent_iterations**|Max number of iterations that would be executed in parallel.  This should be less than the number of cores on the DSVM\n",
        "|**preprocess**| *True/False* <br>Setting this to *True* enables Auto ML to perform preprocessing <br>on the input to handle *missing data*, and perform some common *feature extraction*|\n",
        "|**max_cores_per_iteration**| Indicates how many cores on the compute target would be used to train a single pipeline.<br> Default is *1*, you can set it to *-1* to use all cores|"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_settings = {\n",
        "    \"max_time_sec\": 3600,\n",
        "    \"iterations\": 10,\n",
        "    \"n_cross_validations\": 5,\n",
        "    \"primary_metric\": 'AUC_weighted',\n",
        "    \"preprocess\": True,\n",
        "    \"max_cores_per_iteration\": 2,\n",
        "    \"verbosity\": logging.INFO\n",
        "}\n",
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             debug_log = 'automl_errors.log',\n",
        "                             path=project_folder,\n",
        "                             run_configuration=conda_run_config,\n",
        "                             #compute_target = dsvm_compute,\n",
        "                             data_script = project_folder + \"/get_data.py\",\n",
        "                             **automl_settings\n",
        "                            )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Training the Models <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
        "\n",
        "For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets/models even when the experiment is running to retreive the best model up to that point. Once you are satisfied with the model you can cancel a particular iteration or the whole run."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "remote_run = experiment.submit(automl_config, show_output=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
        "#### Widget for monitoring runs\n",
        "\n",
        "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
        "\n",
        "You can click on a pipeline to see run properties and output logs. Logs are also available on the DSVM under /tmp/azureml_run/{iterationid}/azureml-logs\n",
        "\n",
        "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.train.widgets import RunDetails\n",
        "RunDetails(remote_run).show() "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n",
        "#### Retrieve All Child Runs\n",
        "You can also use sdk methods to fetch all the child runs and see individual metrics that we log. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "children = list(remote_run.get_children())\n",
        "metricslist = {}\n",
        "for run in children:\n",
        "    properties = run.get_properties()\n",
        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
        "    metricslist[int(properties['iteration'])] = metrics\n",
        "\n",
        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
        "rundata"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Canceling Runs\n",
        "You can cancel ongoing remote runs using the *cancel()* and *cancel_iteration()* functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Cancel the ongoing experiment and stop scheduling new iterations\n",
        "remote_run.cancel()\n",
        "\n",
        "# Cancel iteration 1 and move onto iteration 2\n",
        "# remote_run.cancel_iteration(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Retrieve the Best Model\n",
        "\n",
        "Below we select the best pipeline from our iterations. The *get_output* method returns the best run and the fitted model. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run, fitted_model = remote_run.get_output()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Best Model based on any other metric"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# lookup_metric = \"accuracy\"\n",
        "# best_run, fitted_model = remote_run.get_output(metric=lookup_metric)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Model from a specific iteration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# iteration = 1\n",
        "# best_run, fitted_model = remote_run.get_output(iteration=iteration)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Testing the Best Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from pandas_ml import ConfusionMatrix\n",
        "\n",
        "df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
        "                     delimiter=\"\\t\", quotechar='\"')\n",
        "\n",
        "# get integer labels\n",
        "le = LabelEncoder()\n",
        "le.fit(df[\"Label\"].values)\n",
        "y = le.transform(df[\"Label\"].values)\n",
        "X = df.drop([\"Label\"], axis=1)\n",
        "\n",
        "_, X_test, _, y_test = train_test_split(X, y, test_size=0.1, random_state=42)\n",
        "\n",
        "ypred = fitted_model.predict(X_test.values)\n",
        "\n",
        "ypred_strings = le.inverse_transform(ypred)\n",
        "ytest_strings = le.inverse_transform(y_test)\n",
        "\n",
        "cm = ConfusionMatrix(ytest_strings, ypred_strings)\n",
        "\n",
        "print(cm)\n",
        "\n",
        "cm.plot()"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "savitam"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- 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/15b.auto-ml-regression-ensemble.ipynb
+++ b/automl/15b.auto-ml-regression-ensemble.ipynb
@@ -1,442 +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 15b: Regression with ensembling on remote compute\n",
        "\n",
        "In this example we use the scikit-learn's [diabetes dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html) to showcase how you can use AutoML for a simple regression problem.\n",
        "\n",
        "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. Create an `Experiment` in an existing `Workspace`.\n",
        "2. Configure AutoML using `AutoMLConfig`which enables an extra ensembling iteration.\n",
        "3. Train the model using remote compute.\n",
        "4. Explore the results.\n",
        "5. Test the best fitted model.\n",
        "\n",
        "<b>Disclaimers / Limitations </b>\n",
        "- Currently only Train/Validation split is supported; support for cross-validation will be coming soon.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create an Experiment\n",
        "\n",
        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import logging\n",
        "import os\n",
        "import random\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "from matplotlib.pyplot import imshow\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import datasets\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.train.automl import AutoMLConfig\n",
        "from azureml.train.automl.run import AutoMLRun"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "\n",
        "# Choose a name for the experiment and specify the project folder.\n",
        "experiment_name = 'automl-local-regression'\n",
        "project_folder = './sample_projects/automl-local-regression'\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace Name'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Project Directory'] = project_folder\n",
        "output['Experiment Name'] = experiment.name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "pd.DataFrame(data = output, index = ['']).T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Diagnostics\n",
        "\n",
        "Opt-in diagnostics for better experience, quality, and security of future releases."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.telemetry import set_diagnostics_collection\n",
        "set_diagnostics_collection(send_diagnostics = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create a Remote Linux DSVM\n",
        "**Note:** If creation fails with a message about Marketplace purchase eligibilty, start creation of a DSVM through the [Azure portal](https://portal.azure.com), and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled this setting, you can exit the portal without actually creating the DSVM, and creation of the DSVM through the notebook should work."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.compute import DsvmCompute\n",
        "\n",
        "dsvm_name = 'mydsvm'\n",
        "try:\n",
        "    dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
        "    print('Found an existing DSVM.')\n",
        "except:\n",
        "    print('Creating a new DSVM.')\n",
        "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
        "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
        "    dsvm_compute.wait_for_completion(show_output = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create Get Data File\n",
        "For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
        "In this example, the `get_data()` function returns data using scikit-learn's `diabetes` dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%%writefile $project_folder/get_data.py\n",
        "\n",
        "# Load the diabetes dataset, a well-known built-in small dataset that comes with scikit-learn.\n",
        "from sklearn.datasets import load_diabetes\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "def get_data():\n",
        "    X, y = load_diabetes(return_X_y = True)\n",
        "\n",
        "    columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
        "\n",
        "    X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
        "    X_valid, X_test, y_valid, y_test = train_test_split(X_temp, y_temp, test_size = 0.5, random_state = 0)\n",
        "    return { \"X\" : X_train, \"y\" : y_train, \"X_valid\": X_valid, \"y_valid\": y_valid }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Configure AutoML\n",
        "\n",
        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
        "|**task**|classification or regression|\n",
        "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
        "|**max_time_sec**|Time limit in seconds for each iteration.|\n",
        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
        "|**enable_ensembling**|Flag to enable an ensembling iteration after all the other iterations complete.|\n",
        "|**ensemble_iterations**|Number of iterations during which we choose a fitted pipeline to be part of the final ensemble.|\n",
        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_config = AutoMLConfig(task = 'regression',\n",
        "                             max_time_sec = 600,\n",
        "                             iterations = 20,\n",
        "                             primary_metric = 'spearman_correlation',\n",
        "                             debug_log = 'regression.log',\n",
        "                             verbosity = logging.INFO,\n",
        "                             compute_target = dsvm_compute,\n",
        "                             data_script = project_folder + \"/get_data.py\",\n",
        "                             enable_ensembling = True,\n",
        "                             ensemble_iterations = 5,\n",
        "                             path = project_folder)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train the Model\n",
        "\n",
        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
        "In this example, we specify `show_output = True` to print currently running iterations to the console."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run = experiment.submit(automl_config, show_output = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Explore the Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Widget for Monitoring Runs\n",
        "\n",
        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
        "\n",
        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.train.widgets import RunDetails\n",
        "RunDetails(local_run).show() "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n",
        "#### Retrieve All Child Runs\n",
        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "children = list(local_run.get_children())\n",
        "metricslist = {}\n",
        "for run in children:\n",
        "    properties = run.get_properties()\n",
        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
        "    metricslist[int(properties['iteration'])] = metrics\n",
        "\n",
        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
        "rundata"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Retrieve the Best Model\n",
        "\n",
        "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run, fitted_model = local_run.get_output()\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Best Model Based on Any Other Metric\n",
        "Show the run and the model that has the smallest `root_mean_squared_error` value."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "lookup_metric = \"root_mean_squared_error\"\n",
        "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Test the Best Model (Ensemble)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Predict on training and test set, and calculate residual values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_diabetes\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X, y = load_diabetes(return_X_y = True)\n",
        "\n",
        "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
        "X_valid, X_test, y_valid, y_test = train_test_split(X_temp, y_temp, test_size = 0.5, random_state = 0)\n",
        "\n",
        "\n",
        "y_pred_train = fitted_model.predict(X_train)\n",
        "y_residual_train = y_train - y_pred_train\n",
        "\n",
        "y_pred_test = fitted_model.predict(X_test)\n",
        "y_residual_test = y_test - y_pred_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn import datasets\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "# Set up a multi-plot chart.\n",
        "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[1, 1], 'wspace':0, 'hspace': 0})\n",
        "f.suptitle('Regression Residual Values', fontsize = 18)\n",
        "f.set_figheight(6)\n",
        "f.set_figwidth(16)\n",
        "\n",
        "# Plot residual values of training set.\n",
        "a0.axis([0, 360, -200, 200])\n",
        "a0.plot(y_residual_train, 'bo', alpha = 0.5)\n",
        "a0.plot([-10,360],[0,0], 'r-', lw = 3)\n",
        "a0.text(16,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_train, y_pred_train))), fontsize = 12)\n",
        "a0.text(16,140,'R2 score = {0:.2f}'.format(r2_score(y_train, y_pred_train)), fontsize = 12)\n",
        "a0.set_xlabel('Training samples', fontsize = 12)\n",
        "a0.set_ylabel('Residual Values', fontsize = 12)\n",
        "\n",
        "# Plot a histogram.\n",
        "a0.hist(y_residual_train, orientation = 'horizontal', color = 'b', bins = 10, histtype = 'step');\n",
        "a0.hist(y_residual_train, orientation = 'horizontal', color = 'b', alpha = 0.2, bins = 10);\n",
        "\n",
        "# Plot residual values of test set.\n",
        "a1.axis([0, 90, -200, 200])\n",
        "a1.plot(y_residual_test, 'bo', alpha = 0.5)\n",
        "a1.plot([-10,360],[0,0], 'r-', lw = 3)\n",
        "a1.text(5,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_test, y_pred_test))), fontsize = 12)\n",
        "a1.text(5,140,'R2 score = {0:.2f}'.format(r2_score(y_test, y_pred_test)), fontsize = 12)\n",
        "a1.set_xlabel('Test samples', fontsize = 12)\n",
        "a1.set_yticklabels([])\n",
        "\n",
        "# Plot a histogram.\n",
        "a1.hist(y_residual_test, orientation = 'horizontal', color = 'b', bins = 10, histtype = 'step')\n",
        "a1.hist(y_residual_test, orientation = 'horizontal', color = 'b', alpha = 0.2, bins = 10)\n",
        "\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "ratanase"
      }
    ],
    "kernelspec": {
      "display_name": "Python 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
@@ -1,277 +0,0 @@
 # Table of Contents
 1. [Automated ML Introduction](#introduction)
 1. [Running samples in Azure Notebooks](#jupyter)
 1. [Running samples in a Local Conda environment](#localconda)
 1. [Automated ML SDK Sample Notebooks](#samples)
 1. [Documentation](#documentation)
 1. [Running using python command](#pythoncommand)
 1. [Troubleshooting](#troubleshooting)
 <a name="introduction"></a>
 # Automated ML introduction 
 Automated machine learning (automated ML) builds high quality machine learning models for you by automating model and hyperparameter selection. Bring a labelled dataset that you want to build a model for, automated ML will give you a high quality machine learning model that you can use for predictions.
 If you are new to Data Science, AutoML will help you get jumpstarted by simplifying machine learning model building. It abstracts you from needing to perform model selection, hyperparameter selection and in one step creates a high quality trained model for you to use.
 If you are an experienced data scientist, AutoML will help increase your productivity by intelligently performing the model and hyperparameter selection for your training and generates high quality models much quicker than manually specifying several combinations of the parameters and running training jobs. AutoML provides visibility and access to all the training jobs and the performance characteristics of the models to help you further tune the pipeline if you desire.
 <a name="jupyter"></a>
 ## Running samples in Azure Notebooks - Jupyter based notebooks in the Azure cloud 
 1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
 [Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks.
 1. Follow the instructions in the [../00.configuration](00.configuration.ipynb) notebook to create and connect to a workspace.
 1. Open one of the sample notebooks.
    **Make sure the Azure Notebook kernel is set to `Python 3.6`** when you open a notebook. 
    ![set kernel to Python 3.6](../images/python36.png)
 <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 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 10 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: 
 ```
 bash automl_setup_linux.sh
 ```
 ### 4. Running configuration.ipynb
 - Before running any samples you next need to run the configuration notebook. Click on 00.configuration.ipynb notebook
 - 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 
 - [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 automated ML for classification using a remote Batch AI compute for training
    - Parallel execution of iterations
    - Async tracking of progress
    - Cancelling individual iterations or entire run
    - 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-train-test-split.ipynb](06.auto-ml-sparse-data-train-test-split.ipynb)
    - Dataset: Scikit learn's [20newsgroup](http://scikit-learn.org/stable/datasets/twenty_newsgroups.html) 
    - Handle sparse datasets
    - Specify custom train and validation set
 - [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)
    - Download fitted pipeline for any iteration
 - [08.auto-ml-remote-execution-with-DataStore.ipynb](08.auto-ml-remote-execution-with-DataStore.ipynb)
    - Dataset: scikit learn's [digit dataset](https://innovate.burningman.org/datasets-page/)
    - Download the data and store it in DataStore.
 - [09.auto-ml-classification-with-deployment.ipynb](09.auto-ml-classification-with-deployment.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
    - 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
 - [14.auto-ml-model-explanation.ipynb](14.auto-ml-model-explanation.ipynb)
    - Dataset: seaborn's [iris dataset](https://seaborn.pydata.org/generated/seaborn.load_dataset.html)
    - Explaining the AutoML classification pipeline
    - Visualizing feature importance in widget
 - [15a.auto-ml-classification-ensemble.ipynb](15a.auto-ml-classification-ensemble.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
    - Enables an extra iteration for generating an Ensemble of models
    - Uses local compute for training
 - [15b.auto-ml-regression-ensemble.ipynb](15b.auto-ml-regression-ensemble.ipynb)
    - Dataset: scikit learn's [diabetes dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html)
    - Enables an extra iteration for generating an Ensemble of models
    - Uses remote Linux DSVM for training
 <a name="documentation"></a>
 # Documentation 
 ## Table of Contents
 1. [Automated ML Settings ](#automlsettings)
 1. [Cross validation split options](#cvsplits)
 1. [Get Data Syntax](#getdata)
 1. [Data pre-processing and featurization](#preprocessing)
 <a name="automlsettings"></a>
 ## Automated ML Settings 
 |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>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|
 <a name="cvsplits"></a>
 ## Cross validation split options
 ### K-Folds Cross Validation
 Use *n_cross_validations* setting to specify the number of cross validations. The training data set will be randomly split into *n_cross_validations* folds of equal size. During each cross validation round, one of the folds will be used for validation of the model trained on the remaining folds. This process repeats for *n_cross_validations* rounds until each fold is used once as validation set. Finally, the average scores accross all *n_cross_validations* rounds will be reported, and the corresponding model will be retrained on the whole training data set.
 ### 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.
 <a name="getdata"></a>
 ## get_data() syntax 
 The *get_data()* function can be used to return a dictionary with these values:
 |Key|Type|Dependency|Mutually Exclusive with|Description|
 |:-|:-|:-|:-|:-|
 |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|
 <a name="preprocessing"></a>
 ## Data pre-processing and featurization 
 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.
 <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 
 ## 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,<1.15.0
 - cython
 - urllib3<1.24
 - scipy>=0.19.0,<0.20.0
 - scikit-learn>=0.18.0,<=0.19.1
 - pandas>=0.22.0,<0.23.0
 - pip:
    # Required packages for AzureML execution, history, and data preparation.
  - azureml-sdk[automl,notebooks]
  - pandas_ml
--- a/configuration.ipynb
+++ b/configuration.ipynb
@@ -0,0 +1,383 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/configuration.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Configuration\n",
        "\n",
        "_**Setting up your Azure Machine Learning services workspace and configuring your notebook library**_\n",
        "\n",
        "---\n",
        "---\n",
        "\n",
        "## Table of Contents\n",
        "\n",
        "1. [Introduction](#Introduction)\n",
        "    1. What is an Azure Machine Learning workspace\n",
        "1. [Setup](#Setup)\n",
        "    1. Azure subscription\n",
        "    1. Azure ML SDK and other library installation\n",
        "    1. Azure Container Instance registration\n",
        "1. [Configure your Azure ML Workspace](#Configure%20your%20Azure%20ML%20workspace)\n",
        "    1. Workspace parameters\n",
        "    1. Access your workspace\n",
        "    1. Create a new workspace\n",
        "    1. Create compute resources\n",
        "1. [Next steps](#Next%20steps)\n",
        "\n",
        "---\n",
        "\n",
        "## Introduction\n",
        "\n",
        "This notebook configures your library of notebooks to connect to an Azure Machine Learning (ML) workspace.  In this case, a library contains all of the notebooks in the current folder and any nested folders.  You can configure this notebook library to use an existing workspace or create a new workspace.\n",
        "\n",
        "Typically you will need to run this notebook only once per notebook library as all other notebooks will use connection information that is written here.  If you want to redirect your notebook library to work with a different workspace, then you should re-run this notebook.\n",
        "\n",
        "In this notebook you will\n",
        "* Learn about getting an Azure subscription\n",
        "* Specify your workspace parameters\n",
        "* Access or create your workspace\n",
        "* Add a default compute cluster for your workspace\n",
        "\n",
        "### What is an Azure Machine Learning workspace\n",
        "\n",
        "An Azure ML Workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an Azure ML Workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, deployment, 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 1.0.43 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 = \"cpu-cluster\"\n",
        "\n",
        "# Verify that cluster does not exist already\n",
        "try:\n",
        "    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
        "    print(\"Found existing cpu-cluster\")\n",
        "except ComputeTargetException:\n",
        "    print(\"Creating new cpu-cluster\")\n",
        "    \n",
        "    # Specify the configuration for the new cluster\n",
        "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_D2_V2\",\n",
        "                                                           min_nodes=0,\n",
        "                                                           max_nodes=4)\n",
        "\n",
        "    # Create the cluster with the specified name and configuration\n",
        "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
        "    \n",
        "    # Wait for the cluster to complete, show the output log\n",
        "    cpu_cluster.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To create a **GPU** cluster, run the cell below. Note that your subscription must have sufficient quota for GPU VMs or the command will fail. To increase quota, see [these instructions](https://docs.microsoft.com/en-us/azure/azure-supportability/resource-manager-core-quotas-request). "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "# Choose a name for your GPU cluster\n",
        "gpu_cluster_name = \"gpu-cluster\"\n",
        "\n",
        "# Verify that cluster does not exist already\n",
        "try:\n",
        "    gpu_cluster = ComputeTarget(workspace=ws, name=gpu_cluster_name)\n",
        "    print(\"Found existing gpu cluster\")\n",
        "except ComputeTargetException:\n",
        "    print(\"Creating new gpu-cluster\")\n",
        "    \n",
        "    # Specify the configuration for the new cluster\n",
        "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_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 within notebook](./how-to-use-azureml/training/train-within-notebook) example that walks through a simplified but complete end to end machine learning process."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "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/README.md
+++ b/contrib/RAPIDS/README.md
@@ -0,0 +1,307 @@
 ## How to use the RAPIDS on AzureML materials
 ### Setting up requirements
 The material requires the use of the Azure ML SDK and of the Jupyter Notebook Server to run the interactive execution. Please refer to instructions to [setup the environment.](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#local "Local Computer Set Up") Follow the instructions under **Local Computer**, make sure to run the last step: <span style="font-family: Courier New;">pip install \<new package\></span> with <span style="font-family: Courier New;">new package = progressbar2  (pip install progressbar2)</span>
 After following the directions, the user should end up setting a conda environment (<span style="font-family: Courier New;">myenv</span>)that can be activated in an Anaconda prompt
 The user would also require an Azure Subscription with a Machine Learning Services quota on the desired region for 24 nodes or more (to be able to select a vmSize with 4 GPUs as it is used on the Notebook) on the desired VM family ([NC\_v3](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv3-series),  [NC\_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv2-series), [ND](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#nd-series) or [ND_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ndv2-series-preview)), the specific vmSize to be used within the chosen family would also need to be whitelisted for Machine Learning Services usage.  
 &nbsp;  
 ### Getting and running the material 
 Clone the AzureML Notebooks repository in GitHub by running the following command on a local_directory: 
 * C:\local_directory>git clone https://github.com/Azure/MachineLearningNotebooks.git
 On a conda prompt navigate to the local directory, activate the conda environment (<span style="font-family: Courier New;">myenv</span>), where the Azure ML SDK was installed and launch Jupyter Notebook. 
 * (<span style="font-family: Courier New;">myenv</span>) C:\local_directory>jupyter notebook
 From the resulting browser at http://localhost:8888/tree, navigate to the master notebook: 
 * http://localhost:8888/tree/MachineLearningNotebooks/contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb
 &nbsp;  
 The following notebook will appear:  
 ![](imgs/NotebookHome.png)
 &nbsp;  
 ### Master Jupyter Notebook
 The notebook can be executed interactively step by step, by pressing the Run button (In a red circle in the above image.)
 The first couple of functional steps import the necessary AzureML libraries.  If you experience any errors please refer back to the [setup the environment.](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#local "Local Computer Set Up") instructions.
 &nbsp;  
 #### Setting up a Workspace
 The following step gathers the information necessary to set up a workspace to execute the RAPIDS script. This needs to be done only once, or not at all if you already have a workspace you can use set up on the Azure Portal:
 ![](imgs/WorkSpaceSetUp.png)
 It is important to be sure to set the correct values for the subscription\_id, resource\_group, workspace\_name, and region before executing the step. An example is:
    subscription_id = os.environ.get("SUBSCRIPTION_ID", "1358e503-xxxx-4043-xxxx-65b83xxxx32d")
    resource_group = os.environ.get("RESOURCE_GROUP", "AML-Rapids-Testing")
    workspace_name = os.environ.get("WORKSPACE_NAME", "AML_Rapids_Tester")
    workspace_region = os.environ.get("WORKSPACE_REGION", "West US 2")
 &nbsp;  
 The resource\_group and workspace_name could take any value, the region should match the region for which the subscription has the required Machine Learning Services node quota.
 The first time the code is executed it will redirect to the Azure Portal to validate subscription credentials. After the workspace is created, its related information is stored on a local file so that this step can be subsequently skipped. The immediate step will just load the saved workspace
 ![](imgs/saved_workspace.png)
 Once a workspace has been created the user could skip its creation and just jump to this step. The configuration file resides in:
 * C:\local_directory\\MachineLearningNotebooks\contrib\RAPIDS\aml_config\config.json
 &nbsp;  
 #### Creating an AML Compute Target 
 Following step, creates an AML Compute Target 
 ![](imgs/target_creation.png)
 Parameter vm\_size on function call AmlCompute.provisioning\_configuration() has to be a member of the VM families ([NC\_v3](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv3-series),  [NC\_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv2-series), [ND](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#nd-series) or [ND_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ndv2-series-preview)) that are the ones provided with P40 or V100 GPUs, that are the ones supported by RAPIDS. In this particular case an Standard\_NC24s\_V2 was used.
 &nbsp;  
 If the output of running the step has an error of the form:
 ![](imgs/targeterror1.png)
 It is an indication that even though the subscription has a node quota for VMs for that family, it does not have a node quota for Machine Learning Services for that family. 
 You will need to request an increase node quota for that family in that region for **Machine Learning Services**.
 &nbsp;  
 Another possible error is the following: 
 ![](imgs/targeterror2.png)
 Which indicates that specified vmSize has not been whitelisted for usage on Machine Learning Services and a request to do so should be filled.
 The successful creation of the compute target would have an output like the following:
 ![](imgs/targetsuccess.png)
 &nbsp;  
 #### RAPIDS script uploading and viewing
 The next step copies the RAPIDS script process_data.py, which is a slightly modified implementation of the [RAPIDS E2E example](https://github.com/rapidsai/notebooks/blob/master/mortgage/E2E.ipynb), into a script processing folder and it presents its contents to the user. (The script is discussed in the next section in detail). 
 If the user wants to use a different RAPIDS script, the references to the  <span style="font-family: Courier New;">process_data.py</span> script have to be changed
 ![](imgs/scriptuploading.png)
 &nbsp;  
 #### Data Uploading
 The RAPIDS script loads and extracts features from the Fannie Mae’s Mortgage Dataset to train an XGBoost prediction model. The script uses two years of data
 The next few steps download and decompress the data and is made available to the  script as an [Azure Machine Learning Datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data).
 &nbsp;  
 The following functions are used to download and decompress the input data
 ![](imgs/dcf1.png)
 ![](imgs/dcf2.png)
 ![](imgs/dcf3.png)
 ![](imgs/dcf4.png)
 &nbsp;  
 The next step uses those functions to download locally file: 
 http://rapidsai-data.s3-website.us-east-2.amazonaws.com/notebook-mortgage-data/mortgage_2000-2001.tgz'
 And to decompress it, into local folder path = .\mortgage_2000-2001
 The step takes several minutes, the intermediate outputs provide progress indicators.
 ![](imgs/downamddecom.png)
 &nbsp;  
 The decompressed data should have the following structure:
 * .\mortgage_2000-2001\acq\Acquisition_<year>Q<num>.txt 
 * .\mortgage_2000-2001\perf\Performance_<year>Q<num>.txt 
 * .\mortgage_2000-2001\names.csv
 The data is divided in partitions that roughly correspond to yearly quarters. RAPIDS includes support for multi-node, multi-GPU deployments, enabling scaling up and out on much larger dataset sizes. The user will be able to verify that the number of partitions that the script is able to process increases with the number of GPUs used. The RAPIDS script is implemented for single-machine scenarios. An example supporting multiple nodes will be published later. 
 &nbsp;  
 The next step upload the data into the [Azure Machine Learning Datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data) under reference <span style="font-family: Courier New;">fileroot = mortgage_2000-2001</span>
 The step takes several minutes to load the data, the output provides a progress indicator.
 ![](imgs/datastore.png)
 Once the data has been loaded into the Azure Machine LEarning Data Store, in subsequent run, the user can comment out the ds.upload line and just make reference to the <span style="font-family: Courier New;">mortgage_2000-2001</blog> data store reference  
 &nbsp;  
 #### Setting up required libraries and environment to run RAPIDS code
 There are two options to setup the environment to run RAPIDS code. The following steps shows how to ues a prebuilt conda environment. A recommended alternative is to specify a base Docker image and package dependencies. You can find sample code for that in the notebook.
 ![](imgs/install2.png)
 &nbsp;  
 #### Wrapper function to submit the RAPIDS script as an Azure Machine Learning experiment
 The next step consists of the definition of a wrapper function to be used when the user attempts to run the RAPIDS script with different arguments. It takes as arguments: <span style="font-family: Times New Roman;">*cpu\_training*</span>;  a flag that indicates if the run is meant to be processed with CPU-only, <span style="font-family: Times New Roman;">*gpu\_count*</span>; the number of GPUs to be used if they are meant to be used and part_count: the number of data partitions to be used
 ![](imgs/wrapper.png)
 &nbsp;  
 The core of the function resides in configuring the run by the instantiation of a ScriptRunConfig object, which defines the source_directory for the script to be executed, the name of the script and the arguments to be passed to the script.
 In addition to the wrapper function arguments, two other arguments are passed: <span style="font-family: Times New Roman;">*data\_dir*</span>, the directory where the data is stored and <span style="font-family: Times New Roman;">*end_year*</span> is the largest year to use partition from.
 As mentioned earlier the size of the data that can be processed increases with the number of gpus, in the function, dictionary <span style="font-family: Times New Roman;">*max\_gpu\_count\_data\_partition_mapping*</span> maps the maximum number of partitions that we empirically found that the system can handle given the number of GPUs used. The function throws a warning when the number of partitions for a given number of gpus exceeds the maximum but the script is still executed, however the user should expect an error as an out of memory situation would be encountered
 If the user wants to use a different RAPIDS script, the reference to the process_data.py script has to be changed
 &nbsp;  
 #### Submitting Experiments
 We are ready to submit experiments: launching the RAPIDS script with different sets of parameters.
 &nbsp;  
 The following couple of steps submit experiments under different conditions. 
 ![](imgs/submission1.png)
 &nbsp;  
 The user can change variable num\_gpu between one and the number of GPUs supported by the chosen vmSize. Variable part\_count can take any value between 1 and 11, but if it exceeds the maximum for num_gpu, the run would result in an error
 &nbsp;  
 If the experiment is successfully submitted, it would be placed on a queue for processing, its status would appeared as Queued and an output like the following would appear 
 ![](imgs/queue.png)
 &nbsp;  
 When the experiment starts running, its status would appeared as Running and the output would change to something like this:
 ![](imgs/running.png)
 &nbsp;  
 #### Reproducing the performance gains plot results on the Blog Post
 When the run has finished successfully, its status would appeared as Completed and the output would change to something like this:
 &nbsp; 
 ![](imgs/completed.png)
 Which is the output for an experiment run with three partitions and one GPU, notice that the reported processing time is 49.16 seconds just as depicted on the performance gains plot on the blog post
 &nbsp;  
 ![](imgs/2GPUs.png)
 This output corresponds to a run with three partitions and two GPUs, notice that the reported processing time is 37.50 seconds just as depicted on the performance gains plot on the blog post
 &nbsp;  
 ![](imgs/3GPUs.png)
 This output corresponds to an experiment run with three partitions and three GPUs, notice that the reported processing time is 24.40 seconds just as depicted on the performance gains plot on the blog post
 &nbsp;  
 ![](imgs/4gpus.png)
 This output corresponds to an experiment run with three partitions and four GPUs, notice that the reported processing time is 23.33 seconds just as depicted on the performance gains plot on the blogpost
 &nbsp;  
 ![](imgs/CPUBase.png)
 This output corresponds to an experiment run with three partitions and using only CPU, notice that the reported processing time is 9 minutes and 1.21 seconds or 541.21 second just as depicted on the performance gains plot on the blog post
 &nbsp;  
 ![](imgs/OOM.png)
 This output corresponds to an experiment run with nine partitions and four GPUs, notice that the notebook throws a warning signaling that the number of partitions exceed the maximum that the system can handle with those many GPUs and the run ends up failing, hence having and status of Failed. 
 &nbsp;  
 ##### Freeing Resources
 In the last step the notebook deletes the compute target. (This step is optional especially if the min_nodes in the cluster is set to 0 with which the cluster will scale down to 0 nodes when there is no usage.)
 ![](imgs/clusterdelete.png)
 &nbsp;  
 ### RAPIDS Script
 The Master Notebook runs experiments by launching a RAPIDS script with different sets of parameters. In this section, the RAPIDS script, process_data.py in the material, is analyzed
 The script first imports all the necessary libraries and parses the arguments passed by the Master Notebook.
 The all internal functions to be used by the script are defined.
 &nbsp;  
 #### Wrapper Auxiliary Functions:
 The below functions are wrappers for a configuration module for librmm, the RAPIDS Memory Manager python interface:
 ![](imgs/wap1.png)![](imgs/wap2.png)
 &nbsp;  
 A couple of other functions are wrappers for the submission of jobs to the DASK client:
 ![](imgs/wap3.png)
 ![](imgs/wap4.png)
 &nbsp;  
 #### Data Loading Functions:
 The data is loaded through the use of the following three functions 
 ![](imgs/DLF1.png)![](imgs/DLF2.png)![](imgs/DLF3.png)
 All three functions use library function cudf.read_csv(), cuDF version for the well known counterpart on Pandas.
 &nbsp;  
 #### Data Transformation and Feature Extraction Functions:
 The raw data is transformed and processed to extract features by joining, slicing, grouping, aggregating, factoring, etc, the original dataframes just as is done with Pandas. The following functions in the script are used for that purpose:
 ![](imgs/fef1.png)![](imgs/fef2.png)![](imgs/fef3.png)![](imgs/fef4.png)![](imgs/fef5.png)
 ![](imgs/fef6.png)![](imgs/fef7.png)![](imgs/fef8.png)![](imgs/fef9.png)
 &nbsp;  
 #### Main() Function
 The previous functions are used in the Main function to accomplish several steps: Set up the Dask client, do all ETL operations, set up and train an XGBoost model, the function also assigns which data needs to be processed by each Dask client
 &nbsp;  
 ##### Setting Up DASK client:
 The following lines:
 ![](imgs/daskini.png)
 &nbsp;  
 Initialize and set up a DASK client with a number of workers corresponding to the number of GPUs to be used on the run. A successful execution of the set up will result on the following output:
 ![](imgs/daskoutput.png)
 ##### All ETL functions are used on single calls to process\_quarter_gpu, one per data partition
 ![](imgs/ETL.png)
 &nbsp;  
 ##### Concentrating the data assigned to each DASK worker
 The partitions assigned to each worker are concatenated and set up for training.
 ![](imgs/Dask2.png)
 &nbsp;  
 ##### Setting Training Parameters
 The parameters used for the training of a gradient boosted decision tree model are set up in the following code block:
 ![](imgs/PArameters.png)
 Notice how the parameters are modified when using the CPU-only mode.
 &nbsp;  
 ##### Launching the training of a gradient boosted decision tree model using XGBoost.
 ![](imgs/training.png)
 The outputs of the script can be observed in the master notebook as the script is executed
 ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/RAPIDS/README.png)
--- a/contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb
+++ b/contrib/RAPIDS/azure-ml-with-nvidia-rapids.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": [
    "# 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 ETLÂ 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.conda_dependencies import CondaDependencies\n",
    "from azureml.core.compute import AmlCompute, ComputeTarget\n",
    "from azureml.data.data_reference import DataReference\n",
    "from azureml.core.runconfig import RunConfiguration\n",
    "from azureml.core import ScriptRunConfig\n",
    "from azureml.widgets import RunDetails"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Azure ML Workspace"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following step is optional if you already have a workspace. If you want to use an existing workspace, then\n",
    "skip this workspace creation step and move on to the next step to load the workspace.\n",
    " \n",
    "<font color='red'>Important</font>: in the code cell below, be sure to set the correct values for the subscription_id, \n",
    "resource_group, workspace_name, region before executing this code cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "subscription_id = os.environ.get(\"SUBSCRIPTION_ID\", \"<subscription_id>\")\n",
    "resource_group = os.environ.get(\"RESOURCE_GROUP\", \"<resource_group>\")\n",
    "workspace_name = os.environ.get(\"WORKSPACE_NAME\", \"<workspace_name>\")\n",
    "workspace_region = os.environ.get(\"WORKSPACE_REGION\", \"<region>\")\n",
    "\n",
    "ws = Workspace.create(workspace_name, subscription_id=subscription_id, resource_group=resource_group, location=workspace_region)\n",
    "\n",
    "# write config to a local directory for future use\n",
    "ws.write_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load existing Workspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "# 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's Single-Family Loan Performance Data](http://www.fanniemae.com/portal/funding-the-market/data/loan-performance-data.html). Once you obtain access to the data, you will need to make this data available in an [Azure Machine Learning Datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data), for use in this sample. The following code shows how to do that."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Downloading Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red'>Important</font>: Python package progressbar2 is necessary to run the following cell. If it is not available in your environment where this notebook is running, please install it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tarfile\n",
    "import hashlib\n",
    "from urllib.request import urlretrieve\n",
    "from progressbar import ProgressBar\n",
    "\n",
    "def validate_downloaded_data(path):\n",
    "    if(os.path.isdir(path) and os.path.exists(path + '//names.csv')) :\n",
    "        if(os.path.isdir(path + '//acq' ) and len(os.listdir(path + '//acq')) == 8):\n",
    "            if(os.path.isdir(path + '//perf' ) and len(os.listdir(path + '//perf')) == 11):\n",
    "                print(\"Data has been downloaded and decompressed at: {0}\".format(path))\n",
    "                return True\n",
    "    print(\"Data has not been downloaded and decompressed\")\n",
    "    return False\n",
    "\n",
    "def show_progress(count, block_size, total_size):\n",
    "    global pbar\n",
    "    global processed\n",
    "    \n",
    "    if count == 0:\n",
    "        pbar = ProgressBar(maxval=total_size)\n",
    "        processed = 0\n",
    "    \n",
    "    processed += block_size\n",
    "    processed = min(processed,total_size)\n",
    "    pbar.update(processed)\n",
    "\n",
    "        \n",
    "def download_file(fileroot):\n",
    "    filename = fileroot + '.tgz'\n",
    "    if(not os.path.exists(filename) or hashlib.md5(open(filename, 'rb').read()).hexdigest() != '82dd47135053303e9526c2d5c43befd5' ):\n",
    "        url_format = 'http://rapidsai-data.s3-website.us-east-2.amazonaws.com/notebook-mortgage-data/{0}.tgz'\n",
    "        url = url_format.format(fileroot)\n",
    "        print(\"...Downloading file :{0}\".format(filename))\n",
    "        urlretrieve(url, filename,show_progress)\n",
    "        pbar.finish()\n",
    "        print(\"...File :{0} finished downloading\".format(filename))\n",
    "    else:\n",
    "        print(\"...File :{0} has been downloaded already\".format(filename))\n",
    "    return filename\n",
    "\n",
    "def decompress_file(filename,path):\n",
    "    tar = tarfile.open(filename)\n",
    "    print(\"...Getting information from {0} about files to decompress\".format(filename))\n",
    "    members = tar.getmembers()\n",
    "    numFiles = len(members)\n",
    "    so_far = 0\n",
    "    for member_info in members:\n",
    "        tar.extract(member_info,path=path)\n",
    "        show_progress(so_far, 1, numFiles)\n",
    "        so_far += 1\n",
    "    pbar.finish()\n",
    "    print(\"...All {0} files have been decompressed\".format(numFiles))\n",
    "    tar.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fileroot = 'mortgage_2000-2001'\n",
    "path = '.\\\\{0}'.format(fileroot)\n",
    "pbar = None\n",
    "processed = 0\n",
    "\n",
    "if(not validate_downloaded_data(path)):\n",
    "    print(\"Downloading and Decompressing Input Data\")\n",
    "    filename = download_file(fileroot)\n",
    "    decompress_file(filename,path)\n",
    "    print(\"Input Data has been Downloaded and Decompressed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uploading Data to Workspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = ws.get_default_datastore()\n",
    "\n",
    "# download and uncompress data in a local directory before uploading to data store\n",
    "# directory specified in src_dir parameter below should have the acq, perf directories with data and names.csv file\n",
    "ds.upload(src_dir=path, target_path=fileroot, overwrite=True, show_progress=True)\n",
    "\n",
    "# data already uploaded to the datastore\n",
    "data_ref = DataReference(data_reference_name='data', datastore=ds, path_on_datastore=fileroot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create AML run configuration to launch a machine learning job"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RunConfiguration is used to submit jobs to Azure Machine Learning service. When creating RunConfiguration for a job, users can either \n",
    "1. specify a Docker image with prebuilt conda environment and use it without any modifications to run the job, or \n",
    "2. specify a Docker image as the base image and conda or pip packages as dependnecies to let AML build a new Docker image with a conda environment containing specified dependencies to use in the job\n",
    "\n",
    "The second option is the recommended option in AML. \n",
    "The following steps have code for both options. You can pick the one that is more appropriate for your requirements. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Specify prebuilt conda environment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code shows how to use an existing image from [Docker Hub](https://hub.docker.com/r/rapidsai/rapidsai/) that has a prebuilt conda environment named 'rapids' when creating a RunConfiguration. Note that this conda environment does not include azureml-defaults package that is required for using AML functionality like metrics tracking, model management etc. This package is automatically installed when you use 'Specify package dependencies' option and that is why it is the recommended option to create RunConfiguraiton in AML."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_config = RunConfiguration()\n",
    "run_config.framework = 'python'\n",
    "run_config.environment.python.user_managed_dependencies = True\n",
    "run_config.environment.python.interpreter_path = '/conda/envs/rapids/bin/python'\n",
    "run_config.target = gpu_cluster_name\n",
    "run_config.environment.docker.enabled = True\n",
    "run_config.environment.docker.gpu_support = True\n",
    "run_config.environment.docker.base_image = \"rapidsai/rapidsai:cuda9.2-runtime-ubuntu18.04\"\n",
    "# run_config.environment.docker.base_image_registry.address = '<registry_url>' # not required if the base_image is in Docker hub\n",
    "# run_config.environment.docker.base_image_registry.username = '<user_name>' # needed only for private images\n",
    "# run_config.environment.docker.base_image_registry.password = '<password>' # needed only for private images\n",
    "run_config.environment.spark.precache_packages = False\n",
    "run_config.data_references={'data':data_ref.to_config()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Specify package dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code shows how to list package dependencies in a conda environment definition file (rapids.yml) when creating a RunConfiguration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cd = CondaDependencies(conda_dependencies_file_path='rapids.yml')\n",
    "# run_config = RunConfiguration(conda_dependencies=cd)\n",
    "# run_config.framework = 'python'\n",
    "# run_config.target = gpu_cluster_name\n",
    "# run_config.environment.docker.enabled = True\n",
    "# run_config.environment.docker.gpu_support = True\n",
    "# run_config.environment.docker.base_image = \"<image>\"\n",
    "# run_config.environment.docker.base_image_registry.address = '<registry_url>' # not required if the base_image is in Docker hub\n",
    "# run_config.environment.docker.base_image_registry.username = '<user_name>' # needed only for private images\n",
    "# run_config.environment.docker.base_image_registry.password = '<password>' # needed only for private images\n",
    "# run_config.environment.spark.precache_packages = False\n",
    "# run_config.data_references={'data':data_ref.to_config()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wrapper function to submit Azure Machine Learning experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameter cpu_predictor indicates if training should be done on CPU. If set to true, GPUs are used *only* for ETL and *not* for training\n",
    "# parameter num_gpu indicates number of GPUs to use among the GPUs available in the VM for ETL and if cpu_predictor is false, for training as well \n",
    "def run_rapids_experiment(cpu_training, gpu_count, part_count):\n",
    "    # any value between 1-4 is allowed here depending the type of VMs available in gpu_cluster\n",
    "    if gpu_count not in [1, 2, 3, 4]:\n",
    "        raise Exception('Value specified for the number of GPUs to use {0} is invalid'.format(gpu_count))\n",
    "\n",
    "    # following data partition mapping is empirical (specific to GPUs used and current data partitioning scheme) and may need to be tweaked\n",
    "    max_gpu_count_data_partition_mapping = {1: 3, 2: 4, 3: 6, 4: 8}\n",
    "    \n",
    "    if part_count > max_gpu_count_data_partition_mapping[gpu_count]:\n",
    "        print(\"Too many partitions for the number of GPUs, exceeding memory threshold\")\n",
    "        \n",
    "    if part_count > 11:\n",
    "        print(\"Warning: Maximum number of partitions available is 11\")\n",
    "        part_count = 11\n",
    "        \n",
    "    end_year = 2000\n",
    "    \n",
    "    if part_count > 4:\n",
    "        end_year = 2001 # use more data with more GPUs\n",
    "\n",
    "    src = ScriptRunConfig(source_directory=scripts_folder, \n",
    "                          script='process_data.py', \n",
    "                          arguments = ['--num_gpu', gpu_count, '--data_dir', str(data_ref),\n",
    "                                      '--part_count', part_count, '--end_year', end_year,\n",
    "                                      '--cpu_predictor', cpu_training\n",
    "                                      ],\n",
    "                          run_config=run_config\n",
    "                         )\n",
    "\n",
    "    exp = Experiment(ws, 'rapidstest')\n",
    "    run = exp.submit(config=src)\n",
    "    RunDetails(run).show()\n",
    "    return run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submit experiment (ETL & training on GPU)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cpu_predictor = False\n",
    "# the value for num_gpu should be less than or equal to the number of GPUs available in the VM\n",
    "num_gpu = 1\n",
    "data_part_count = 1\n",
    "# train using CPU, use GPU for both ETL and training\n",
    "run = run_rapids_experiment(cpu_predictor, num_gpu, data_part_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submit experiment (ETL on GPU, training on CPU)\n",
    "\n",
    "To observe performance difference between GPU-accelerated RAPIDS based training with CPU-only training, set 'cpu_predictor' predictor to 'True' and rerun the experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cpu_predictor = True\n",
    "# the value for num_gpu should be less than or equal to the number of GPUs available in the VM\n",
    "num_gpu = 1\n",
    "data_part_count = 1\n",
    "# train using CPU, use GPU for ETL\n",
    "run = run_rapids_experiment(cpu_predictor, num_gpu, data_part_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Delete cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete the cluster\n",
    "# gpu_cluster.delete()"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "ksivas"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3.6",
   "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/imgs/2GPUs.png
+++ b/contrib/RAPIDS/imgs/2GPUs.png
--- a/contrib/RAPIDS/imgs/3GPUs.png
+++ b/contrib/RAPIDS/imgs/3GPUs.png
--- a/contrib/RAPIDS/imgs/4gpus.png
+++ b/contrib/RAPIDS/imgs/4gpus.png
--- a/contrib/RAPIDS/imgs/CPUBase.png
+++ b/contrib/RAPIDS/imgs/CPUBase.png
--- a/contrib/RAPIDS/imgs/DLF1.png
+++ b/contrib/RAPIDS/imgs/DLF1.png
--- a/contrib/RAPIDS/imgs/DLF2.png
+++ b/contrib/RAPIDS/imgs/DLF2.png
--- a/contrib/RAPIDS/imgs/DLF3.png
+++ b/contrib/RAPIDS/imgs/DLF3.png
--- a/contrib/RAPIDS/imgs/Dask2.png
+++ b/contrib/RAPIDS/imgs/Dask2.png
--- a/contrib/RAPIDS/imgs/ETL.png
+++ b/contrib/RAPIDS/imgs/ETL.png
--- a/contrib/RAPIDS/imgs/NotebookHome.png
+++ b/contrib/RAPIDS/imgs/NotebookHome.png
--- a/contrib/RAPIDS/imgs/OOM.png
+++ b/contrib/RAPIDS/imgs/OOM.png
--- a/contrib/RAPIDS/imgs/PArameters.png
+++ b/contrib/RAPIDS/imgs/PArameters.png
--- a/contrib/RAPIDS/imgs/WorkSpaceSetUp.png
+++ b/contrib/RAPIDS/imgs/WorkSpaceSetUp.png
--- a/contrib/RAPIDS/imgs/clusterdelete.png
+++ b/contrib/RAPIDS/imgs/clusterdelete.png
--- a/contrib/RAPIDS/imgs/completed.png
+++ b/contrib/RAPIDS/imgs/completed.png
--- a/contrib/RAPIDS/imgs/daskini.png
+++ b/contrib/RAPIDS/imgs/daskini.png
--- a/contrib/RAPIDS/imgs/daskoutput.png
+++ b/contrib/RAPIDS/imgs/daskoutput.png
--- a/contrib/RAPIDS/imgs/datastore.png
+++ b/contrib/RAPIDS/imgs/datastore.png
--- a/contrib/RAPIDS/imgs/dcf1.png
+++ b/contrib/RAPIDS/imgs/dcf1.png
--- a/contrib/RAPIDS/imgs/dcf2.png
+++ b/contrib/RAPIDS/imgs/dcf2.png
--- a/contrib/RAPIDS/imgs/dcf3.png
+++ b/contrib/RAPIDS/imgs/dcf3.png
--- a/contrib/RAPIDS/imgs/dcf4.png
+++ b/contrib/RAPIDS/imgs/dcf4.png
--- a/contrib/RAPIDS/imgs/downamddecom.png
+++ b/contrib/RAPIDS/imgs/downamddecom.png
--- a/contrib/RAPIDS/imgs/fef1.png
+++ b/contrib/RAPIDS/imgs/fef1.png
--- a/contrib/RAPIDS/imgs/fef2.png
+++ b/contrib/RAPIDS/imgs/fef2.png
--- a/contrib/RAPIDS/imgs/fef3.png
+++ b/contrib/RAPIDS/imgs/fef3.png
--- a/contrib/RAPIDS/imgs/fef4.png
+++ b/contrib/RAPIDS/imgs/fef4.png
--- a/contrib/RAPIDS/imgs/fef5.png
+++ b/contrib/RAPIDS/imgs/fef5.png
--- a/contrib/RAPIDS/imgs/fef6.png
+++ b/contrib/RAPIDS/imgs/fef6.png
--- a/contrib/RAPIDS/imgs/fef7.png
+++ b/contrib/RAPIDS/imgs/fef7.png
--- a/contrib/RAPIDS/imgs/fef8.png
+++ b/contrib/RAPIDS/imgs/fef8.png
--- a/contrib/RAPIDS/imgs/fef9.png
+++ b/contrib/RAPIDS/imgs/fef9.png
--- a/contrib/RAPIDS/imgs/install2.png
+++ b/contrib/RAPIDS/imgs/install2.png
--- a/contrib/RAPIDS/imgs/installation.png
+++ b/contrib/RAPIDS/imgs/installation.png
--- a/contrib/RAPIDS/imgs/queue.png
+++ b/contrib/RAPIDS/imgs/queue.png
--- a/contrib/RAPIDS/imgs/running.png
+++ b/contrib/RAPIDS/imgs/running.png
--- a/contrib/RAPIDS/imgs/saved_workspace.png
+++ b/contrib/RAPIDS/imgs/saved_workspace.png
--- a/contrib/RAPIDS/imgs/scriptuploading.png
+++ b/contrib/RAPIDS/imgs/scriptuploading.png
--- a/contrib/RAPIDS/imgs/submission1.png
+++ b/contrib/RAPIDS/imgs/submission1.png
--- a/contrib/RAPIDS/imgs/target_creation.png
+++ b/contrib/RAPIDS/imgs/target_creation.png
--- a/contrib/RAPIDS/imgs/targeterror1.png
+++ b/contrib/RAPIDS/imgs/targeterror1.png
--- a/contrib/RAPIDS/imgs/targeterror2.png
+++ b/contrib/RAPIDS/imgs/targeterror2.png
--- a/contrib/RAPIDS/imgs/targetsuccess.png
+++ b/contrib/RAPIDS/imgs/targetsuccess.png
--- a/contrib/RAPIDS/imgs/training.png
+++ b/contrib/RAPIDS/imgs/training.png
--- a/contrib/RAPIDS/imgs/wap1.png
+++ b/contrib/RAPIDS/imgs/wap1.png
--- a/contrib/RAPIDS/imgs/wap2.png
+++ b/contrib/RAPIDS/imgs/wap2.png
--- a/contrib/RAPIDS/imgs/wap3.png
+++ b/contrib/RAPIDS/imgs/wap3.png
--- a/contrib/RAPIDS/imgs/wap4.png
+++ b/contrib/RAPIDS/imgs/wap4.png
--- a/contrib/RAPIDS/imgs/wrapper.png
+++ b/contrib/RAPIDS/imgs/wrapper.png
--- a/contrib/RAPIDS/process_data.py
+++ b/contrib/RAPIDS/process_data.py
@@ -0,0 +1,495 @@
 import numpy as np
 import datetime
 import dask_xgboost as dxgb_gpu
 import dask
 import dask_cudf
 from dask_cuda import LocalCUDACluster
 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
 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(client, col_names_path, acq_data_path, year=2000, quarter=1, perf_file=""):
    dask_client = client
    ml_arrays = run_dask_task(delayed(run_gpu_workflow),
                                          col_path=col_names_path,
                                          acq_path=acq_data_path,
                                          quarter=quarter,
                                          year=year,
                                          perf_file=perf_file)
    return dask_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(col_path, acq_path, quarter=1, year=2000, perf_file="", **kwargs):
    names = gpu_load_names(col_path=col_path)
    acq_gdf = gpu_load_acquisition_csv(acquisition_path= acq_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(col_path):
    """ 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_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(preserve_index=False)
 def main():
    #print('XGBOOST_BUILD_DOC is ' + os.environ['XGBOOST_BUILD_DOC'])
    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')
    if cpu_predictor:
        print('Training with CPUs require num gpu = 1')
        num_gpu = 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]
    cluster = LocalCUDACluster(ip=IPADDR,n_workers=num_gpu)
    client = Client(cluster)
    client
    print(client.ncores())
 # 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)
    client
    print(client.ncores())
 # 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(client, col_names_path, acq_data_path, 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)
    client
    print(client.ncores())
    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()
    wait(gpu_dfs)
    labels = None
    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')
 if __name__ == '__main__':
    main()
--- a/contrib/RAPIDS/rapids.yml
+++ b/contrib/RAPIDS/rapids.yml
@@ -0,0 +1,35 @@
 name: rapids
 channels:
 - nvidia
 - numba
 - conda-forge
 - rapidsai
 - defaults
 - pytorch
 dependencies:
 - arrow-cpp=0.12.0
 - bokeh
 - cffi=1.11.5
 - cmake=3.12
 - cuda92
 - cython==0.29
 - dask=1.1.1
 - distributed=1.25.3
 - faiss-gpu=1.5.0
 - numba=0.42
 - numpy=1.15.4
 - nvstrings
 - pandas=0.23.4
 - pyarrow=0.12.0
 - scikit-learn
 - scipy
 - cudf
 - cuml
 - python=3.6.2
 - jupyterlab
 - pip:
  - file:/rapids/xgboost/python-package/dist/xgboost-0.81-py3-none-any.whl
  - git+https://github.com/rapidsai/dask-xgboost@dask-cudf
  - git+https://github.com/rapidsai/dask-cudf@master
  - git+https://github.com/rapidsai/dask-cuda@master
--- a/databricks/01.Installation_and_Configuration.ipynb
+++ b/databricks/01.Installation_and_Configuration.ipynb
@@ -1 +0,0 @@
 {"cells":[{"cell_type":"markdown","source":["Azure ML & Azure Databricks notebooks by Parashar Shah.\n\nCopyright (c) Microsoft Corporation. All rights reserved.\n\nLicensed under the MIT License."],"metadata":{}},{"cell_type":"markdown","source":["Please ensure you have run this notebook before proceeding."],"metadata":{}},{"cell_type":"markdown","source":["Now we support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package (during private preview). You can select the option to attach the library to all clusters or just one cluster.\n\nProvide this full string to install the SDK:\n\nazureml-sdk[databricks]"],"metadata":{}},{"cell_type":"code","source":["import azureml.core\n\n# Check core SDK version number - based on build number of preview/master.\nprint(\"SDK version:\", azureml.core.VERSION)"],"metadata":{},"outputs":[],"execution_count":4},{"cell_type":"code","source":["subscription_id = \"<your-subscription-id>\"\nresource_group = \"<your-existing-resource-group>\"\nworkspace_name = \"<a-new-or-existing-workspace; it is unrelated to Databricks workspace>\"\nworkspace_region = \"<your-resource group-region>\""],"metadata":{},"outputs":[],"execution_count":5},{"cell_type":"code","source":["# import the Workspace class and check the azureml SDK version\n# exist_ok checks if workspace exists or not.\n\nfrom azureml.core import Workspace\n\nws = Workspace.create(name = workspace_name,\n                      subscription_id = subscription_id,\n                      resource_group = resource_group, \n                      location = workspace_region,\n                      exist_ok=True)\n\nws.get_details()"],"metadata":{},"outputs":[],"execution_count":6},{"cell_type":"code","source":["ws = Workspace(workspace_name = workspace_name,\n               subscription_id = subscription_id,\n               resource_group = resource_group)\n\n# persist the subscription id, resource group name, and workspace name in aml_config/config.json.\nws.write_config()"],"metadata":{},"outputs":[],"execution_count":7},{"cell_type":"code","source":["%sh\ncat /databricks/driver/aml_config/config.json"],"metadata":{},"outputs":[],"execution_count":8},{"cell_type":"code","source":["# import the Workspace class and check the azureml SDK version\nfrom azureml.core import Workspace\n\nws = Workspace.from_config()\nprint('Workspace name: ' + ws.name, \n      'Azure region: ' + ws.location, \n      'Subscription id: ' + ws.subscription_id, \n      'Resource group: ' + ws.resource_group, sep = '\\n')"],"metadata":{},"outputs":[],"execution_count":9},{"cell_type":"code","source":["dbutils.notebook.exit(\"success\")"],"metadata":{},"outputs":[],"execution_count":10},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":11}],"metadata":{"name":"01.Installation_and_Configuration","notebookId":3874566296719377},"nbformat":4,"nbformat_minor":0}
--- a/databricks/02.Ingest_data.ipynb
+++ b/databricks/02.Ingest_data.ipynb
@@ -1 +0,0 @@
 {"cells":[{"cell_type":"markdown","source":["Azure ML & Azure Databricks notebooks by Parashar Shah.\n\nCopyright (c) Microsoft Corporation. All rights reserved.\n\nLicensed under the MIT License."],"metadata":{}},{"cell_type":"markdown","source":["Please ensure you have run all previous notebooks in sequence before running this."],"metadata":{}},{"cell_type":"markdown","source":["#Data Ingestion"],"metadata":{}},{"cell_type":"code","source":["import os\nimport urllib"],"metadata":{},"outputs":[],"execution_count":4},{"cell_type":"code","source":["# Download AdultCensusIncome.csv from Azure CDN. This file has 32,561 rows.\nbasedataurl = \"https://amldockerdatasets.azureedge.net\"\ndatafile = \"AdultCensusIncome.csv\"\ndatafile_dbfs = os.path.join(\"/dbfs\", datafile)\n\nif os.path.isfile(datafile_dbfs):\n    print(\"found {} at {}\".format(datafile, datafile_dbfs))\nelse:\n    print(\"downloading {} to {}\".format(datafile, datafile_dbfs))\n    urllib.request.urlretrieve(os.path.join(basedataurl, datafile), datafile_dbfs)"],"metadata":{},"outputs":[],"execution_count":5},{"cell_type":"code","source":["# Create a Spark dataframe out of the csv file.\ndata_all = sqlContext.read.format('csv').options(header='true', inferSchema='true', ignoreLeadingWhiteSpace='true', ignoreTrailingWhiteSpace='true').load(datafile)\nprint(\"({}, {})\".format(data_all.count(), len(data_all.columns)))\ndata_all.printSchema()"],"metadata":{},"outputs":[],"execution_count":6},{"cell_type":"code","source":["#renaming columns\ncolumns_new = [col.replace(\"-\", \"_\") for col in data_all.columns]\ndata_all = data_all.toDF(*columns_new)\ndata_all.printSchema()"],"metadata":{},"outputs":[],"execution_count":7},{"cell_type":"code","source":["display(data_all.limit(5))"],"metadata":{},"outputs":[],"execution_count":8},{"cell_type":"markdown","source":["#Data Preparation"],"metadata":{}},{"cell_type":"code","source":["# Choose feature columns and the label column.\nlabel = \"income\"\nxvals_all = set(data_all.columns) - {label}\n\n#dbutils.widgets.remove(\"xvars_multiselect\")\ndbutils.widgets.removeAll()\n\ndbutils.widgets.multiselect('xvars_multiselect', 'hours_per_week', xvals_all)\nxvars_multiselect = dbutils.widgets.get(\"xvars_multiselect\")\nxvars = xvars_multiselect.split(\",\")\n\nprint(\"label = {}\".format(label))\nprint(\"features = {}\".format(xvars))\n\ndata = data_all.select([*xvars, label])\n\n# Split data into train and test.\ntrain, test = data.randomSplit([0.75, 0.25], seed=123)\n\nprint(\"train ({}, {})\".format(train.count(), len(train.columns)))\nprint(\"test ({}, {})\".format(test.count(), len(test.columns)))"],"metadata":{},"outputs":[],"execution_count":10},{"cell_type":"markdown","source":["#Data Persistence"],"metadata":{}},{"cell_type":"code","source":["# Write the train and test data sets to intermediate storage\ntrain_data_path = \"AdultCensusIncomeTrain\"\ntest_data_path = \"AdultCensusIncomeTest\"\n\ntrain_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTrain\")\ntest_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTest\")\n\ntrain.write.mode('overwrite').parquet(train_data_path)\ntest.write.mode('overwrite').parquet(test_data_path)\nprint(\"train and test datasets saved to {} and {}\".format(train_data_path_dbfs, test_data_path_dbfs))"],"metadata":{},"outputs":[],"execution_count":12},{"cell_type":"code","source":["dbutils.notebook.exit(\"success\")"],"metadata":{},"outputs":[],"execution_count":13},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":14}],"metadata":{"name":"02.Ingest_data","notebookId":3874566296719393},"nbformat":4,"nbformat_minor":0}
--- a/databricks/03a.Build_model.ipynb
+++ b/databricks/03a.Build_model.ipynb
--- a/databricks/03b.Build_model_runHistory.ipynb
+++ b/databricks/03b.Build_model_runHistory.ipynb
--- a/databricks/04.Deploy_to_ACI.ipynb
+++ b/databricks/04.Deploy_to_ACI.ipynb
--- a/databricks/05.Deploy_to_AKS_existingImage.ipynb
+++ b/databricks/05.Deploy_to_AKS_existingImage.ipynb
@@ -1 +0,0 @@
 {"cells":[{"cell_type":"markdown","source":["Azure ML & Azure Databricks notebooks by Parashar Shah.\n\nCopyright (c) Microsoft Corporation. All rights reserved.\n\nLicensed under the MIT License."],"metadata":{}},{"cell_type":"markdown","source":["Please ensure you have run all previous notebooks in sequence before running this. This notebook uses image from ACI notebook for deploying to AKS."],"metadata":{}},{"cell_type":"code","source":["from azureml.core import Workspace\nimport azureml.core\n\n# Check core SDK version number\nprint(\"SDK version:\", azureml.core.VERSION)\n\n#'''\nws = Workspace.from_config()\nprint('Workspace name: ' + ws.name, \n      'Azure region: ' + ws.location, \n      'Subscription id: ' + ws.subscription_id, \n      'Resource group: ' + ws.resource_group, sep = '\\n')\n#'''"],"metadata":{},"outputs":[],"execution_count":3},{"cell_type":"code","source":["# List images by ws\n\nfrom azureml.core.image import ContainerImage\nfor i in ContainerImage.list(workspace = ws):\n    print('{}(v.{} [{}]) stored at {} with build log {}'.format(i.name, i.version, i.creation_state, i.image_location, i.image_build_log_uri))"],"metadata":{},"outputs":[],"execution_count":4},{"cell_type":"code","source":["from azureml.core.image import Image\nmyimage = Image(workspace=ws, id=\"aciws:25\")"],"metadata":{},"outputs":[],"execution_count":5},{"cell_type":"code","source":["#create AKS compute\n#it may take 20-25 minutes to create a new cluster\n\nfrom azureml.core.compute import AksCompute, ComputeTarget\n\n# Use the default configuration (can also provide parameters to customize)\nprov_config = AksCompute.provisioning_configuration()\n\naks_name = 'ps-aks-clus2' \n\n# Create the cluster\naks_target = ComputeTarget.create(workspace = ws, \n                                  name = aks_name, \n                                  provisioning_configuration = prov_config)\n\naks_target.wait_for_completion(show_output = True)\n\nprint(aks_target.provisioning_state)\nprint(aks_target.provisioning_errors)"],"metadata":{},"outputs":[],"execution_count":6},{"cell_type":"code","source":["from azureml.core.webservice import Webservice\nhelp( Webservice.deploy_from_image)"],"metadata":{},"outputs":[],"execution_count":7},{"cell_type":"code","source":["from azureml.core.webservice import Webservice, AksWebservice\nfrom azureml.core.image import ContainerImage\n\n#Set the web service configuration (using default here)\naks_config = AksWebservice.deploy_configuration()\n\n#unique service name\nservice_name ='ps-aks-service'\n\n# Webservice creation using single command, there is a variant to use image directly as well.\naks_service = Webservice.deploy_from_image(\n  workspace=ws, \n  name=service_name,\n  deployment_config = aks_config,\n  image = myimage,\n  deployment_target = aks_target\n    )\n\naks_service.wait_for_deployment(show_output=True)"],"metadata":{},"outputs":[],"execution_count":8},{"cell_type":"code","source":["#for using the Web HTTP API \nprint(aks_service.scoring_uri)\nprint(aks_service.get_keys())"],"metadata":{},"outputs":[],"execution_count":9},{"cell_type":"code","source":["import json\n\n#get the some sample data\ntest_data_path = \"AdultCensusIncomeTest\"\ntest = spark.read.parquet(test_data_path).limit(5)\n\ntest_json = json.dumps(test.toJSON().collect())\n\nprint(test_json)"],"metadata":{},"outputs":[],"execution_count":10},{"cell_type":"code","source":["#using data defined above predict if income is >50K (1) or <=50K (0)\naks_service.run(input_data=test_json)"],"metadata":{},"outputs":[],"execution_count":11},{"cell_type":"code","source":["#comment to not delete the web service\naks_service.delete()\n#image.delete()\n#model.delete()\n#aks_target.delete()"],"metadata":{},"outputs":[],"execution_count":12},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":13}],"metadata":{"name":"04.DeploytoACI","notebookId":3874566296719318},"nbformat":4,"nbformat_minor":0}
--- a/databricks/Databricks_AMLSDK_github.dbc
+++ b/databricks/Databricks_AMLSDK_github.dbc
--- a/databricks/readme.md
+++ b/databricks/readme.md
@@ -1,29 +0,0 @@
 # Azure Databricks - Azure Machine Learning SDK Sample Notebooks
 **NOTE**: With the latest version of Azure Machine Learning SDK, there are some API changes due to which previous version of notebooks will not work.
 Please remove the previous SDK version and install the latest SDK by installing **azureml-sdk[databricks]** as a PyPi library in Azure Databricks workspace. 
 **NOTE**: Please create your Azure Databricks cluster as v4.x (high concurrency preferred) with **Python 3** (dropdown).
 **NOTE**: Some packages like psutil upgrade libs that can cause a conflict, please install such packages by freezing lib version. Eg. "pstuil **cryptography==1.5 pyopenssl==16.0.0 ipython=2.2.0**" to avoid install error. This issue is related to Databricks and not related to AML SDK.
 **NOTE**: You should at least have contributor access to your Azure subcription to run some of the notebooks.
 The iPython Notebooks have to be run sequentially after making changes based on your subscription. The corresponding DBC archive contains all the notebooks and can be imported into your Databricks workspace. You can the run notebooks after importing .dbc instead of downloading individually.  
 This set of notebooks are related to Income prediction experiment based on this [dataset](https://archive.ics.uci.edu/ml/datasets/adult) and demonstrate how to data prep, train and operationalize a Spark ML model with Azure ML Python SDK from within Azure Databricks. For details on SDK concepts, please refer to [notebooks](https://github.com/Azure/MachineLearningNotebooks)
 (Recommended) [Azure Databricks AML SDK notebooks](Databricks_AMLSDK_github.dbc) A single DBC package to import all notebooks in your Azure Databricks workspace.
 01. [Installation and Configuration](01.Installation_and_Configuration.ipynb): Install the Azure ML Python SDK and Initialize an Azure ML Workspace and save the Workspace configuration file.
 02. [Ingest data](02.Ingest_data.ipynb): Download the Adult Census Income dataset and split it into train and test sets.
 03. [Build model](03a.Build_model.ipynb): Train a binary classification model in Azure Databricks with a Spark ML Pipeline.
 04. [Build model with Run History](03b.Build_model_runHistory.ipynb): Train model and also capture run history (tracking) with Azure ML Python SDK.
 05. [Deploy to ACI](04.Deploy_to_ACI.ipynb): Deploy model to Azure Container Instance (ACI) with Azure ML Python SDK.
 06. [Deploy to AKS](04.Deploy_to_AKS_existingImage.ipynb): Deploy model to Azure Kubernetis Service (AKS) with Azure ML Python SDK from an existing Image with model, conda and score file.
 Copyright (c) Microsoft Corporation. All rights reserved.
 All notebooks in this folder are licensed under the MIT License. 
 Apache®, Apache Spark, and Spark® are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries.
--- a/dataprep/getting-started.ipynb
+++ b/dataprep/getting-started.ipynb
--- a/how-to-use-azureml/README.md
+++ b/how-to-use-azureml/README.md
@@ -0,0 +1,17 @@
 ## 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 to local computer and use Azure ML managed run configuration.
 * [train-on-amlcompute](./training/train-on-amlcompute): Use a 1-n node Azure ML managed compute cluster for remote runs on Azure CPU or GPU infrastructure.
 * [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 +1,270 @@
 # Table of Contents
 1. [Automated ML Introduction](#introduction)
 1. [Setup using Azure Notebooks](#jupyter)
 1. [Setup using Azure Databricks](#databricks)
 1. [Setup using 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, automated ML 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, automated ML 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. Automated ML 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 automated ML.
 <a name="jupyter"></a>
 ## Setup using 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>
 ## Setup using 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>
 ## Setup using 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.
 ### 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 automated ML sample notebooks are in the "automated-machine-learning" folder.
 ### 3. Setup a new conda environment
 The **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.
 Packages installed by the **automl_setup** script:
    <ul><li>python</li><li>nb_conda</li><li>matplotlib</li><li>numpy</li><li>cython</li><li>urllib3</li><li>scipy</li><li>scikit-learn</li><li>pandas</li><li>tensorflow</li><li>py-xgboost</li><li>azureml-sdk</li><li>azureml-widgets</li><li>pandas-ml</li></ul>
 For more details refer to the [automl_env.yml](./automl_env.yml)
 ## 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](../../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 automated ML.
 ### 6. Starting jupyter notebook manually
 To start your Jupyter notebook manually, use:
 ```
 conda activate azure_automl
 jupyter notebook
 ```
 or on Mac or Linux:
 ```
 source activate azure_automl
 jupyter notebook
 ```
 <a name="samples"></a>
 # Automated ML SDK Sample Notebooks
 - [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 automated 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 automated ML for regression
    - Uses local compute for training
 - [auto-ml-remote-amlcompute.ipynb](remote-amlcompute/auto-ml-remote-amlcompute.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 automated ML settings as kwargs
 - [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 automated ML Runs for a given project
    - Get details for a automated ML Run. (automated ML settings, run widget & all metrics)
    - Download fitted pipeline for any iteration
 - [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 automated 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-subsampling-local.ipynb](subsampling/auto-ml-subsampling-local.ipynb)
    - How to enable subsampling
 - [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 automated 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 automated ML 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 automated ML forecasting model on multiple time-series
 - [auto-ml-classification-with-onnx.ipynb](classification-with-onnx/auto-ml-classification-with-onnx.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 automated ML for classification with ONNX models
    - Uses local compute for training
 <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. On Linux, if the error is `gcc: error trying to exec 'cc1plus': execvp: No such file or directory`, install build essentials using the command `sudo apt-get install build-essential`.
 5. 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>`. 
 ## automl_setup_linux.sh fails
 If automl_setup_linux.sh fails on Ubuntu Linux with the error: `unable to execute 'gcc': No such file or directory`
 1. Make sure that outbound ports 53 and 80 are enabled.  On an Azure VM, you can do this from the Azure Portal by selecting the VM and clicking on Networking.
 2. Run the command: `sudo apt-get update`
 3. Run the command: `sudo apt-get install build-essential --fix-missing`
 4. Run `automl_setup_linux.sh` again.
 ## 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.
 ## Numpy import fails on Windows
 Some Windows environments see an error loading numpy with the latest Python version 3.6.8.  If you see this issue, try with Python version 3.6.7.
 ## Numpy import fails
 Check the tensorflow version in the automated ml conda environment. Supported versions are < 1.13. Uninstall tensorflow from the environment if version is >= 1.13
 You may check the version of tensorflow and uninstall as follows
 1) start a command shell, activate conda environment where automated ml packages are installed
 2) enter `pip freeze` and look for `tensorflow` , if found, the version listed should be < 1.13 
 3) If the listed version is a not a supported version,  `pip uninstall tensorflow` in the command shell and enter y for confirmation. 
 ## 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
 Automated ML 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
 Automated ML 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.  Automated ML 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/Show More
+++ b/Show More
		`@@ -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}