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Update 12/2
Update 12-1-19
2025-12-21 10:05:09 -05:00 · 2018-12-02 19:21:15 -05:00 · 2018-12-02 14:46:52 -05:00 · 2018-12-01 20:37:45 -05:00 · 2018-11-29 11:32:16 -05:00
59 changed files with 87071 additions and 2634 deletions
--- a/NBSETUP.md
+++ b/NBSETUP.md
@@ -1,34 +0,0 @@
 # Notebook setup
 ---
 To run the notebooks in this repository use one of these methods:
 ## Use Azure Notebooks - Jupyter based notebooks in the Azure cloud
 1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
 [Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks
 1. Follow the instructions in the [Configuration](configuration.ipynb) notebook to create and connect to a workspace
 1. Open one of the sample notebooks
    **Make sure the Azure Notebook kernel is set to `Python 3.6`** when you open a notebook
    ![set kernel to Python 3.6](images/python36.png)
 ## **Use your own notebook server**
 Video walkthrough:
 [![Get Started video](images/yt_cover.png)](https://youtu.be/VIsXeTuW3FU)
 1. Setup a Jupyter Notebook server and [install the Azure Machine Learning SDK](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python)
 1. Clone [this repository](https://aka.ms/aml-notebooks)
 1. You may need to install other packages for specific notebook
    - For example, to run the Azure Machine Learning Data Prep notebooks, install the extra dataprep SDK:
    ```bash
     pip install azureml-dataprep
    ```
 1. Start your notebook server
 1. Follow the instructions in the [Configuration](configuration.ipynb) notebook to create and connect to a workspace
 1. Open one of the sample notebooks
--- a/README.md
+++ b/README.md
@@ -1,40 +1,53 @@
-# Azure Machine Learning service sample notebooks
+For full documentation for Azure Machine Learning service, visit **https://aka.ms/aml-docs**.
 # Sample Notebooks for Azure Machine Learning service
---
+To run the notebooks in this repository use one of these methods:
-This repository contains example notebooks demonstrating the [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning-service/) Python SDK
+## Use Azure Notebooks - Jupyter based notebooks in the Azure cloud
 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.
-You can find instructions on setting up notebooks [here](./NBSETUP.md)
+1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
 [Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks.
 1. Follow the instructions in the [00.configuration](00.configuration.ipynb) notebook to create and connect to a workspace.
 1. Open one of the sample notebooks.
    **Make sure the Azure Notebook kernel is set to `Python 3.6`** when you open a notebook.  
    ![set kernel to Python 3.6](images/python36.png)
 You can find full documentation for Azure Machine Learning [here](https://aka.ms/aml-docs)
-## Getting Started
+## **Use your own notebook server**
-These examples will provide you with an effective way to get started using AML.  Once you're familiar with
+Video walkthrough:
 some of the capabilities, explore the repository for specific topics.
- [Configuration](./configuration.ipynb) configures your notebook library to easily connect to an
+[![get started video](images/yt_cover.png)](https://youtu.be/VIsXeTuW3FU)
    Azure Machine Learning workspace, and sets up your workspace to be used by many of the other examples.  You should
    always run this first when setting up a notebook library on a new machine or in a new environment
 - [Train in notebook](./how-to-use-azureml/training/train-within-notebook) shows how to create a model directly in a notebook while recording
    metrics and deploy that model to a test service
 - [Train on remote](./how-to-use-azureml/training/train-on-remote-vm) takes the previous example and shows how to create the model on a cloud compute target
 - [Production deploy to AKS](./how-to-use-azureml/deployment/production-deploy-to-aks) shows how to create a production grade inferencing webservice
-## Tutorials
+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
    ```
-The [Tutorials](./tutorials) folder contains notebooks for the tutorials described in the [Azure Machine Learning documentation](https://aka.ms/aml-docs)
+1. Start your notebook server.
-  
+1. Follow the instructions in the [00.configuration](00.configuration.ipynb) notebook to create and connect to a workspace.
-## How to use AML
+1. Open one of the sample notebooks.
 The [How to use AML](./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
+> Note: **Looking for automated machine learning samples?**
- [Automated Machine Learning](./how-to-use-azureml/automated-machine-learning) - Examples using Automated Machine Learning to automatically generate optimal machine learning pipelines and models
+> 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.
- [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
+# Contributing
- [Azure Databricks](./how-to-use-azureml/azure-databricks) - Examples showing how to use Azure ML with Azure Databricks
+
 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/how-to-use-azureml/README.md
+++ b/how-to-use-azureml/README.md
@@ -4,12 +4,13 @@ Learn how to use Azure Machine Learning services for experimentation and model m
 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-within-notebook](train-within-notebook/train-within-notebook.ipynb): Train a model hile tracking run history, and learn how to deploy the model as web service to Azure Container Instance.
- * [train-on-local](./training/train-on-local): Learn how to submit a run and use Azure ML managed run configuration.
+ * [train-on-local](train-on-local/train-on-local.ipynb): Learn how to submit a run and use Azure ML managed run configuration.
-* [train-on-remote-vm](./training/train-on-remote-vm): Use Data Science Virtual Machine as a target for remote runs.
+* [train-on-aci](train-on-aci/train-on-aci.ipynb): Submit a remote run on serverless Docker-based compute.
-* [logging-api](./training/logging-api): Learn about the details of logging metrics to run history.
+* [train-on-remote-vm](train-on-remote-vm/train-on-remote-vm.ipynb): Use Data Science Virtual Machine as a target for remote runs.
-* [register-model-create-image-deploy-service](./deployment/register-model-create-image-deploy-service): Learn about the details of model management.
+* [logging-api](logging-api/logging-api.ipynb): Learn about the details of logging metrics to run history.
-* [production-deploy-to-aks](./deployment/production-deploy-to-aks) Deploy a model to production at scale on Azure Kubernetes Service.
+* [register-model-create-image-deploy-service](register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb): Learn about the details of model management.
-* [enable-data-collection-for-models-in-aks](./deployment/enable-data-collection-for-models-in-aks) Learn about data collection APIs for deployed model.
+* [production-deploy-to-aks](production-deploy-to-aks/production-deploy-to-aks.ipynb) Deploy a model to production at scale on Azure Kubernetes Service.
-* [enable-app-insights-in-production-service](./deployment/enable-app-insights-in-production-service) Learn how to use App Insights with production web service.
+* [enable-data-collection-for-models-in-aks](enable-data-collection-for-models-in-aks/enable-data-collection-for-models-in-aks.ipynb) Learn about data collection APIs for deployed model.
- 
+* [enable-app-insights-in-production-service](enable-app-insights-in-production-serviceenable-app-insights-in-production-service.ipynb) Learn how to use App Insights with production web service.
--- a/how-to-use-azureml/automated-machine-learning/classification-local-azuredatabricks/auto-ml-classification-local-azuredatabricks.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-local-azuredatabricks/auto-ml-classification-local-azuredatabricks.ipynb
@@ -13,164 +13,18 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# Automated Machine Learning: Classification local on Azure DataBricks\n",
+    "# AutoML 01: Classification with Local Compute\n",
    "\n",
    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you will learn how to:\n",
-    "1. Create Azure Machine Learning Workspace object and initialize your notebook directory to easily reload this object from a configuration file.\n",
+    "1. Create an `Experiment` in an existing `Workspace`.\n",
-    "2. Create an `Experiment` in an existing `Workspace`.\n",
+    "2. Configure AutoML using `AutoMLConfig`.\n",
-    "3. Configure AutoML using `AutoMLConfig`.\n",
+    "3. Train the model using local compute.\n",
-    "4. Train the model using AzureDataBricks.\n",
+    "4. Explore the results.\n",
-    "5. Explore the results.\n",
+    "5. Test the best fitted model.\n"
    "6. Test the best fitted model.\n",
    "\n",
    "Prerequisites:\n",
    "Before running this notebook, run the install instructions described in README.md."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Register Machine Learning Services Resource Provider\n",
    "Microsoft.MachineLearningServices only needs to be registed once in the subscription. To register it:\n",
    "Start the Azure portal.\n",
    "Select your All services and then Subscription.\n",
    "Select the subscription that you want to use.\n",
    "Click on Resource providers\n",
    "Click the Register link next to Microsoft.MachineLearningServices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the Azure ML Core SDK Version to Validate Your Installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.core\n",
    "\n",
    "print(\"SDK Version:\", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize an Azure ML Workspace\n",
    "### What is an Azure ML Workspace and Why Do I Need One?\n",
    "\n",
    "An Azure ML workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an Azure ML workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, operationalization, and the monitoring of operationalized models.\n",
    "\n",
    "\n",
    "### What do I Need?\n",
    "\n",
    "To create or access an Azure ML workspace, you will need to import the Azure ML library and specify following information:\n",
    "* A name for your workspace. You can choose one.\n",
    "* Your subscription id. Use the `id` value from the `az account show` command output above.\n",
    "* The resource group name. The resource group organizes Azure resources and provides a default region for the resources in the group. The resource group will be created if it doesn't exist. Resource groups can be created and viewed in the [Azure portal](https://portal.azure.com)\n",
    "* Supported regions include `eastus2`, `eastus`,`westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "subscription_id = \"<SubscriptionId>\"\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",
    "                      exist_ok=True)\n",
    "ws.get_details()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuring Your Local Environment\n",
    "You can validate that you have access to the specified workspace and write a configuration file to the default configuration location, `./aml_config/config.json`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Workspace\n",
    "\n",
    "ws = Workspace(workspace_name = workspace_name,\n",
    "               subscription_id = subscription_id,\n",
    "               resource_group = resource_group)\n",
    "\n",
    "# Persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
    "ws.write_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Folder to Host Sample Projects\n",
    "Finally, create a folder where all the sample projects will be hosted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "sample_projects_folder = './sample_projects'\n",
    "\n",
    "if not os.path.isdir(sample_projects_folder):\n",
    "    os.mkdir(sample_projects_folder)\n",
    "    \n",
    "print('Sample projects will be created in {}.'.format(sample_projects_folder))"
   ]
  },
  {
@@ -191,12 +45,12 @@
    "import logging\n",
    "import os\n",
    "import random\n",
    "import time\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
@@ -254,7 +108,9 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "## Load Training Data Using DataPrep\n"
+    "## Load Training Data\n",
    "\n",
    "This uses scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
   ]
  },
  {
@@ -263,34 +119,13 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "import azureml.dataprep as dprep\n",
+    "from sklearn import datasets\n",
    "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
    "# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
    "simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
    "X = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1)  # Remove the header row.\n",
    "\n",
-    "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
+    "digits = datasets.load_digits()\n",
    "# and convert column types manually.\n",
    "# Here we read a comma delimited file and convert all columns to integers.\n",
    "y = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
-    "## Review the Data Preparation Result\n",
+    "# Exclude the first 100 rows from training so that they can be used for test.\n",
-    "You can peek the result of a Dataflow at any range using skip(i) and head(j). Doing so evaluates only j records for all the steps in the Dataflow, which makes it fast even against large datasets."
+    "X_train = digits.data[100:,:]\n",
-   ]
+    "y_train = digits.target[100:]"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.skip(1).head(5)"
   ]
  },
  {
@@ -308,8 +143,6 @@
    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
    "|**n_cross_validations**|Number of cross validation splits.|\n",
    "|**spark_context**|Spark Context object.|\n",
    "|**max_cuncurrent_iterations**|Maximum number of iterations to execute in parallel. This should be less than the number of cores on the ADB..|\n",
    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
@@ -321,25 +154,16 @@
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "automl_settings = {\n",
    "    \"iteration_timeout_minutes\": 10,\n",
    "    \"iterations\": 10,\n",
    "    \"n_cross_validations\": 5,\n",
    "    \"primary_metric\": 'AUC_weighted',\n",
    "    \"preprocess\": False,\n",
    "    \"max_concurrent_iterations\": 2,\n",
    "    \"verbosity\": logging.INFO,\n",
    "    \"spark_context\": sc\n",
    "}\n",
    " \n",
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
-    "                             path = project_folder, \n",
+    "                             primary_metric = 'AUC_weighted',\n",
-    "                             X = X, \n",
+    "                             iteration_timeout_minutes = 60,\n",
-    "                             y = y,\n",
+    "                             iterations = 25,\n",
-    "                             **automl_settings\n",
+    "                             n_cross_validations = 3,\n",
-    "                            )\n"
+    "                             verbosity = logging.INFO,\n",
    "                             X = X_train, \n",
    "                             y = y_train,\n",
    "                             path = project_folder)"
   ]
  },
  {
@@ -358,7 +182,44 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "local_run = experiment.submit(automl_config, show_output = False)"
+    "local_run = experiment.submit(automl_config, show_output = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optionally, you can continue an interrupted local run by calling `continue_experiment` without the `iterations` parameter, or run more iterations for a completed run by specifying the `iterations` parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = local_run.continue_experiment(X = X_train, \n",
    "                                          y = y_train, \n",
    "                                          show_output = True,\n",
    "                                          iterations = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run"
   ]
  },
  {
@@ -372,9 +233,11 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "#### Portal URL for Monitoring Runs\n",
+    "#### Widget for Monitoring Runs\n",
    "\n",
-    "The following will provide a link to the web interface to explore individual run details and status."
+    "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."
   ]
  },
  {
@@ -383,7 +246,8 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "print(local_run.get_portal_url())"
+    "from azureml.widgets import RunDetails\n",
    "RunDetails(local_run).show() "
   ]
  },
  {
@@ -391,22 +255,6 @@
   "metadata": {},
   "source": [
    "\n",
    "The following will show the child runs and waits for the parent run to complete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run.wait_for_completion(show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retrieve All Child Runs\n",
    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
   ]
@@ -421,7 +269,7 @@
    "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",
+    "    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",
@@ -533,7 +381,7 @@
    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
    "    ax1.set_title(title)\n",
    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
-    "    display(fig)"
+    "    plt.show()"
   ]
  }
 ],
@@ -544,9 +392,9 @@
   }
  ],
  "kernelspec": {
-   "display_name": "Python [conda env:AutoML_ADB]",
+   "display_name": "Python 3.6",
   "language": "python",
-   "name": "conda-env-AutoML_ADB-py"
+   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
@@ -559,10 +407,8 @@
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
-  },
+  }
  "name": "auto-ml-classification-local-adb",
  "notebookId": 3742842704905931
 },
 "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/02.auto-ml-regression.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/02.auto-ml-regression.ipynb
@@ -0,0 +1,415 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 02: Regression with Local Compute\n",
    "\n",
    "In this example we use the scikit-learn's [diabetes dataset](http://scikit-learn.org/stable/datasets/index.html#diabetes-dataset) to showcase how you can use AutoML for a simple regression problem.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you will learn how to:\n",
    "1. Create an `Experiment` in an existing `Workspace`.\n",
    "2. Configure AutoML using `AutoMLConfig`.\n",
    "3. Train the model using local compute.\n",
    "4. Explore the results.\n",
    "5. Test the best fitted model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an Experiment\n",
    "\n",
    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# Choose a name for the experiment and specify the project folder.\n",
    "experiment_name = 'automl-local-regression'\n",
    "project_folder = './sample_projects/automl-local-regression'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace Name'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Training Data\n",
    "This uses scikit-learn's [load_diabetes](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html) method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the diabetes dataset, a well-known built-in small dataset that comes with scikit-learn.\n",
    "from sklearn.datasets import load_diabetes\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X, y = load_diabetes(return_X_y = True)\n",
    "\n",
    "columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure AutoML\n",
    "\n",
    "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**task**|classification or regression|\n",
    "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
    "|**n_cross_validations**|Number of cross validation splits.|\n",
    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_config = AutoMLConfig(task = 'regression',\n",
    "                             iteration_timeout_minutes = 10,\n",
    "                             iterations = 10,\n",
    "                             primary_metric = 'spearman_correlation',\n",
    "                             n_cross_validations = 5,\n",
    "                             debug_log = 'automl.log',\n",
    "                             verbosity = logging.INFO,\n",
    "                             X = X_train, \n",
    "                             y = y_train,\n",
    "                             path = project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Models\n",
    "\n",
    "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
    "In this example, we specify `show_output = True` to print currently running iterations to the console."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = experiment.submit(automl_config, show_output = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Widget for Monitoring Runs\n",
    "\n",
    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
    "\n",
    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "RunDetails(local_run).show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(local_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = local_run.get_output()\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model Based on Any Other Metric\n",
    "Show the run and the model that has the smallest `root_mean_squared_error` value (which turned out to be the same as the one with largest `spearman_correlation` value):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_metric = \"root_mean_squared_error\"\n",
    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a Specific Iteration\n",
    "Show the run and the model from the third iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 3\n",
    "third_run, third_model = local_run.get_output(iteration = iteration)\n",
    "print(third_run)\n",
    "print(third_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Best Fitted Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict on training and test set, and calculate residual values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_train = fitted_model.predict(X_train)\n",
    "y_residual_train = y_train - y_pred_train\n",
    "\n",
    "y_pred_test = fitted_model.predict(X_test)\n",
    "y_residual_test = y_test - y_pred_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# Set up a multi-plot chart.\n",
    "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[1, 1], 'wspace':0, 'hspace': 0})\n",
    "f.suptitle('Regression Residual Values', fontsize = 18)\n",
    "f.set_figheight(6)\n",
    "f.set_figwidth(16)\n",
    "\n",
    "# Plot residual values of training set.\n",
    "a0.axis([0, 360, -200, 200])\n",
    "a0.plot(y_residual_train, 'bo', alpha = 0.5)\n",
    "a0.plot([-10,360],[0,0], 'r-', lw = 3)\n",
    "a0.text(16,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_train, y_pred_train))), fontsize = 12)\n",
    "a0.text(16,140,'R2 score = {0:.2f}'.format(r2_score(y_train, y_pred_train)), fontsize = 12)\n",
    "a0.set_xlabel('Training samples', fontsize = 12)\n",
    "a0.set_ylabel('Residual Values', fontsize = 12)\n",
    "\n",
    "# Plot a histogram.\n",
    "a0.hist(y_residual_train, orientation = 'horizontal', color = 'b', bins = 10, histtype = 'step');\n",
    "a0.hist(y_residual_train, orientation = 'horizontal', color = 'b', alpha = 0.2, bins = 10);\n",
    "\n",
    "# Plot residual values of test set.\n",
    "a1.axis([0, 90, -200, 200])\n",
    "a1.plot(y_residual_test, 'bo', alpha = 0.5)\n",
    "a1.plot([-10,360],[0,0], 'r-', lw = 3)\n",
    "a1.text(5,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_test, y_pred_test))), fontsize = 12)\n",
    "a1.text(5,140,'R2 score = {0:.2f}'.format(r2_score(y_test, y_pred_test)), fontsize = 12)\n",
    "a1.set_xlabel('Test samples', fontsize = 12)\n",
    "a1.set_yticklabels([])\n",
    "\n",
    "# Plot a histogram.\n",
    "a1.hist(y_residual_test, orientation = 'horizontal', color = 'b', bins = 10, histtype = 'step')\n",
    "a1.hist(y_residual_test, orientation = 'horizontal', color = 'b', alpha = 0.2, bins = 10)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "savitam"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/03.auto-ml-remote-execution.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/03.auto-ml-remote-execution.ipynb
@@ -0,0 +1,507 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 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",
    "import time\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# Choose a name for the run history container in the workspace.\n",
    "experiment_name = 'automl-remote-dsvm4'\n",
    "project_folder = './sample_projects/automl-remote-dsvm4'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace Name'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Remote Linux DSVM\n",
    "**Note:** If creation fails with a message about Marketplace purchase eligibilty, start creation of a DSVM through the [Azure portal](https://portal.azure.com), and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled this setting, you can exit the portal without actually creating the DSVM, and creation of the DSVM through the notebook should work.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.compute import DsvmCompute\n",
    "\n",
    "dsvm_name = 'mydsvma'\n",
    "try:\n",
    "    dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
    "    print('Found an existing DSVM.')\n",
    "except:\n",
    "    print('Creating a new DSVM.')\n",
    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
    "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
    "    dsvm_compute.wait_for_completion(show_output = True)\n",
    "    print(\"Waiting one minute for ssh to be accessible\")\n",
    "    time.sleep(60) # Wait for ssh to be accessible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.runconfig import RunConfiguration\n",
    "from azureml.core.conda_dependencies import CondaDependencies\n",
    "\n",
    "# create a new RunConfig object\n",
    "conda_run_config = RunConfiguration(framework=\"python\")\n",
    "\n",
    "# Set compute target to the Linux DSVM\n",
    "conda_run_config.target = dsvm_compute\n",
    "\n",
    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
    "conda_run_config.environment.python.conda_dependencies = cd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Get Data File\n",
    "For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
    "In this example, the `get_data()` function returns data using scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(project_folder):\n",
    "    os.makedirs(project_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile $project_folder/get_data.py\n",
    "\n",
    "from sklearn import datasets\n",
    "from scipy import sparse\n",
    "import numpy as np\n",
    "\n",
    "def get_data():\n",
    "    \n",
    "    digits = datasets.load_digits()\n",
    "    X_train = digits.data[100:,:]\n",
    "    y_train = digits.target[100:]\n",
    "\n",
    "    return { \"X\" : X_train, \"y\" : y_train }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure AutoML <a class=\"anchor\" id=\"Instantiate-AutoML-Remote-DSVM\"></a>\n",
    "\n",
    "You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
    "\n",
    "**Note:** When using Remote DSVM, you can't pass Numpy arrays directly to the fit method.\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
    "|**n_cross_validations**|Number of cross validation splits.|\n",
    "|**max_concurrent_iterations**|Maximum number of iterations to execute in parallel. This should be less than the number of cores on the DSVM.|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_settings = {\n",
    "    \"iteration_timeout_minutes\": 10,\n",
    "    \"iterations\": 20,\n",
    "    \"n_cross_validations\": 5,\n",
    "    \"primary_metric\": 'AUC_weighted',\n",
    "    \"preprocess\": False,\n",
    "    \"max_concurrent_iterations\": 2,\n",
    "    \"verbosity\": logging.INFO\n",
    "}\n",
    "\n",
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             path = project_folder, \n",
    "                             run_configuration=conda_run_config,\n",
    "                             data_script = project_folder + \"/get_data.py\",\n",
    "                             **automl_settings\n",
    "                            )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** The first run on a new DSVM may take several minutes to prepare the environment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Models\n",
    "\n",
    "Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run.\n",
    "\n",
    "In this example, we specify `show_output = False` to suppress console output while the run is in progress."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run = experiment.submit(automl_config, show_output = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the Results\n",
    "\n",
    "#### Loading Executed Runs\n",
    "In case you need to load a previously executed run, enable the cell below and replace the `run_id` value."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "remote_run = AutoMLRun(experiment=experiment, run_id = 'AutoML_480d3ed6-fc94-44aa-8f4e-0b945db9d3ef')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Widget for Monitoring Runs\n",
    "\n",
    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
    "\n",
    "You can click on a pipeline to see run properties and output logs.  Logs are also available on the DSVM under `/tmp/azureml_run/{iterationid}/azureml-logs`\n",
    "\n",
    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "RunDetails(remote_run).show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wait until the run finishes.\n",
    "remote_run.wait_for_completion(show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(remote_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cancelling Runs\n",
    "\n",
    "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cancel the ongoing experiment and stop scheduling new iterations.\n",
    "# remote_run.cancel()\n",
    "\n",
    "# Cancel iteration 1 and move onto iteration 2.\n",
    "# remote_run.cancel_iteration(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = remote_run.get_output()\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model Based on Any Other Metric\n",
    "Show the run and the model which has the smallest `log_loss` value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_metric = \"log_loss\"\n",
    "best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a Specific Iteration\n",
    "Show the run and the model from the third iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 3\n",
    "third_run, third_model = remote_run.get_output(iteration = iteration)\n",
    "print(third_run)\n",
    "print(third_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Best Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n",
    "\n",
    "#### Load Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_test = digits.data[:10, :]\n",
    "y_test = digits.target[:10]\n",
    "images = digits.images[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test Our Best Fitted Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Randomly select digits and test.\n",
    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
    "    print(index)\n",
    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
    "    label = y_test[index]\n",
    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
    "    fig = plt.figure(1, figsize=(3,3))\n",
    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
    "    ax1.set_title(title)\n",
    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "savitam"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/03b.auto-ml-remote-batchai.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/03b.auto-ml-remote-batchai.ipynb
@@ -0,0 +1,528 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 03: Remote Execution using Batch AI\n",
    "\n",
    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you would see\n",
    "1. Create an `Experiment` in an existing `Workspace`.\n",
    "2. Attach an existing Batch AI compute to a workspace.\n",
    "3. Configure AutoML using `AutoMLConfig`.\n",
    "4. Train the model using Batch AI.\n",
    "5. Explore the results.\n",
    "6. Test the best fitted model.\n",
    "\n",
    "In addition this notebook showcases the following features\n",
    "- **Parallel** executions for iterations\n",
    "- **Asynchronous** tracking of progress\n",
    "- **Cancellation** of individual iterations or the entire run\n",
    "- Retrieving models for any iteration or logged metric\n",
    "- Specifying AutoML settings as `**kwargs`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an Experiment\n",
    "\n",
    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# Choose a name for the run history container in the workspace.\n",
    "experiment_name = 'automl-remote-batchai'\n",
    "project_folder = './sample_projects/automl-remote-batchai'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace Name'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Batch AI Cluster\n",
    "The cluster is created as Machine Learning Compute and will appear under your workspace.\n",
    "\n",
    "**Note:** The creation of the Batch AI cluster can take over 10 minutes, please be patient.\n",
    "\n",
    "As with other Azure services, there are limits on certain resources (e.g. Batch AI cluster size) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.compute import AmlCompute\n",
    "from azureml.core.compute import ComputeTarget\n",
    "\n",
    "# Choose a name for your cluster.\n",
    "batchai_cluster_name = \"automlcl\"\n",
    "\n",
    "found = False\n",
    "# Check if this compute target already exists in the workspace.\n",
    "cts = ws.compute_targets\n",
    "if batchai_cluster_name in cts and cts[batchai_cluster_name].type == 'BatchAI':\n",
    "    found = True\n",
    "    print('Found existing compute target.')\n",
    "    compute_target = cts[batchai_cluster_name]\n",
    "    \n",
    "if not found:\n",
    "    print('Creating a new compute target...')\n",
    "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
    "                                                                #vm_priority = 'lowpriority', # optional\n",
    "                                                                max_nodes = 6)\n",
    "\n",
    "    # Create the cluster.\n",
    "    compute_target = ComputeTarget.create(ws, batchai_cluster_name, provisioning_config)\n",
    "    \n",
    "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
    "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
    "    compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
    "    \n",
    "     # For a more detailed view of current Batch AI cluster status, use the 'status' property."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.runconfig import RunConfiguration\n",
    "from azureml.core.conda_dependencies import CondaDependencies\n",
    "\n",
    "# create a new RunConfig object\n",
    "conda_run_config = RunConfiguration(framework=\"python\")\n",
    "\n",
    "# Set compute target to the Batch AI cluster\n",
    "conda_run_config.target = compute_target\n",
    "conda_run_config.environment.docker.enabled = True\n",
    "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
    "\n",
    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
    "conda_run_config.environment.python.conda_dependencies = cd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Get Data File\n",
    "For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
    "In this example, the `get_data()` function returns data using scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(project_folder):\n",
    "    os.makedirs(project_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile $project_folder/get_data.py\n",
    "\n",
    "from sklearn import datasets\n",
    "from scipy import sparse\n",
    "import numpy as np\n",
    "\n",
    "def get_data():\n",
    "    \n",
    "    digits = datasets.load_digits()\n",
    "    X_train = digits.data\n",
    "    y_train = digits.target\n",
    "\n",
    "    return { \"X\" : X_train, \"y\" : y_train }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
    "\n",
    "You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
    "\n",
    "**Note:** When using Batch AI, you can't pass Numpy arrays directly to the fit method.\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
    "|**n_cross_validations**|Number of cross validation splits.|\n",
    "|**max_concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of cores on the DSVM.|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_settings = {\n",
    "    \"iteration_timeout_minutes\": 2,\n",
    "    \"iterations\": 20,\n",
    "    \"n_cross_validations\": 5,\n",
    "    \"primary_metric\": 'AUC_weighted',\n",
    "    \"preprocess\": False,\n",
    "    \"max_concurrent_iterations\": 5,\n",
    "    \"verbosity\": logging.INFO\n",
    "}\n",
    "\n",
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             path = project_folder,\n",
    "                             run_configuration=conda_run_config,\n",
    "                             data_script = project_folder + \"/get_data.py\",\n",
    "                             **automl_settings\n",
    "                            )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Models\n",
    "\n",
    "Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run.\n",
    "In this example, we specify `show_output = False` to suppress console output while the run is in progress."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run = experiment.submit(automl_config, show_output = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the Results\n",
    "\n",
    "#### Loading executed runs\n",
    "In case you need to load a previously executed run, enable the cell below and replace the `run_id` value."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "remote_run = AutoMLRun(experiment = experiment, run_id = 'AutoML_5db13491-c92a-4f1d-b622-8ab8d973a058')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Widget for Monitoring Runs\n",
    "\n",
    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
    "\n",
    "You can click on a pipeline to see run properties and output logs.  Logs are also available on the DSVM under `/tmp/azureml_run/{iterationid}/azureml-logs`\n",
    "\n",
    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "RunDetails(remote_run).show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wait until the run finishes.\n",
    "remote_run.wait_for_completion(show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(remote_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cancelling Runs\n",
    "\n",
    "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cancel the ongoing experiment and stop scheduling new iterations.\n",
    "# remote_run.cancel()\n",
    "\n",
    "# Cancel iteration 1 and move onto iteration 2.\n",
    "# remote_run.cancel_iteration(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = remote_run.get_output()\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model Based on Any Other Metric\n",
    "Show the run and the model which has the smallest `log_loss` value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_metric = \"log_loss\"\n",
    "best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a Specific Iteration\n",
    "Show the run and the model from the third iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 3\n",
    "third_run, third_model = remote_run.get_output(iteration=iteration)\n",
    "print(third_run)\n",
    "print(third_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n",
    "\n",
    "#### Load Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_test = digits.data[:10, :]\n",
    "y_test = digits.target[:10]\n",
    "images = digits.images[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing Our Best Fitted Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Randomly select digits and test.\n",
    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
    "    print(index)\n",
    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
    "    label = y_test[index]\n",
    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
    "    fig = plt.figure(1, figsize=(3,3))\n",
    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
    "    ax1.set_title(title)\n",
    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "savitam"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3.6",
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   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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--- a/how-to-use-azureml/automated-machine-learning/04.auto-ml-remote-execution-text-data-blob-store.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/04.auto-ml-remote-execution-text-data-blob-store.ipynb
@@ -0,0 +1,539 @@
 {
 "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 ComputeTarget, RemoteCompute\n",
    "import time\n",
    "\n",
    "# Add your VM information below\n",
    "# If a compute with the specified compute_name already exists, it will be used and the dsvm_ip_addr, dsvm_ssh_port, \n",
    "# dsvm_username and dsvm_password will be ignored.\n",
    "compute_name  = 'mydsvmb'\n",
    "dsvm_ip_addr  = '<<ip_addr>>'\n",
    "dsvm_ssh_port = 22\n",
    "dsvm_username = '<<username>>'\n",
    "dsvm_password = '<<password>>'\n",
    "\n",
    "if compute_name in ws.compute_targets:\n",
    "    print('Using existing compute.')\n",
    "    dsvm_compute = ws.compute_targets[compute_name]\n",
    "else:\n",
    "    attach_config = RemoteCompute.attach_configuration(address=dsvm_ip_addr, username=dsvm_username, password=dsvm_password, ssh_port=dsvm_ssh_port)\n",
    "    ComputeTarget.attach(workspace=ws, name=compute_name, attach_configuration=attach_config)\n",
    "\n",
    "    while ws.compute_targets[compute_name].provisioning_state == 'Creating':\n",
    "        time.sleep(1)\n",
    "\n",
    "    dsvm_compute = ws.compute_targets[compute_name]\n",
    "    \n",
    "    if dsvm_compute.provisioning_state == 'Failed':\n",
    "        print('Attached failed.')\n",
    "        print(dsvm_compute.provisioning_errors)\n",
    "        dsvm_compute.detach()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.runconfig import RunConfiguration\n",
    "from azureml.core.conda_dependencies import CondaDependencies\n",
    "\n",
    "# create a new RunConfig object\n",
    "conda_run_config = RunConfiguration(framework=\"python\")\n",
    "\n",
    "# Set compute target to the Linux DSVM\n",
    "conda_run_config.target = dsvm_compute\n",
    "\n",
    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
    "conda_run_config.environment.python.conda_dependencies = cd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Get Data File\n",
    "For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
    "In this example, the `get_data()` function returns a [dictionary](README.md#getdata)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(project_folder):\n",
    "    os.makedirs(project_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile $project_folder/get_data.py\n",
    "\n",
    "import 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>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
    "|**n_cross_validations**|Number of cross validation splits.|\n",
    "|**max_concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of cores on the DSVM.|\n",
    "|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.|\n",
    "|**enable_cache**|Setting this to *True* enables preprocess done once and reuse the same preprocessed data for all the iterations. Default value is True.\n",
    "|**max_cores_per_iteration**|Indicates how many cores on the compute target would be used to train a single pipeline.<br>Default is *1*; you can set it to *-1* to use all cores.|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_settings = {\n",
    "    \"iteration_timeout_minutes\": 60,\n",
    "    \"iterations\": 4,\n",
    "    \"n_cross_validations\": 5,\n",
    "    \"primary_metric\": 'AUC_weighted',\n",
    "    \"preprocess\": True,\n",
    "    \"max_cores_per_iteration\": 2\n",
    "}\n",
    "\n",
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             path = project_folder,\n",
    "                             run_configuration=conda_run_config,\n",
    "                             data_script = project_folder + \"/get_data.py\",\n",
    "                             **automl_settings\n",
    "                            )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Models <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
    "\n",
    "Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run = experiment.submit(automl_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
    "#### Widget for Monitoring Runs\n",
    "\n",
    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
    "\n",
    "You can click on a pipeline to see run properties and output logs.  Logs are also available on the DSVM under `/tmp/azureml_run/{iterationid}/azureml-logs`\n",
    "\n",
    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "RunDetails(remote_run).show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wait until the run finishes.\n",
    "remote_run.wait_for_completion(show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-process cache cleanup\n",
    "The preprocess data gets cache at user default file store. When the run is completed the cache can be cleaned by running below cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run.clean_preprocessor_cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(remote_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cancelling Runs\n",
    "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cancel the ongoing experiment and stop scheduling new iterations.\n",
    "# remote_run.cancel()\n",
    "\n",
    "# Cancel iteration 1 and move onto iteration 2.\n",
    "# remote_run.cancel_iteration(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = remote_run.get_output()\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model Based on Any Other Metric\n",
    "Show the run and the model which has the smallest `accuracy` value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lookup_metric = \"accuracy\"\n",
    "# best_run, fitted_model = remote_run.get_output(metric = lookup_metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a Specific Iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 0\n",
    "zero_run, zero_model = remote_run.get_output(iteration = iteration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "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"
  }
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 "nbformat": 4,
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--- a/how-to-use-azureml/automated-machine-learning/05.auto-ml-missing-data-Blacklist-Early-Termination.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/05.auto-ml-missing-data-Blacklist-Early-Termination.ipynb
@@ -0,0 +1,381 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 05: Blacklisting Models, Early Termination, and Handling Missing Data\n",
    "\n",
    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for handling missing values in data. We also provide a stopping metric indicating a target for the primary metrics so that AutoML can terminate the run without necessarly going through all the iterations. Finally, if you want to avoid a certain pipeline, we allow you to specify a blacklist of algorithms that AutoML will ignore for this run.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you will learn how to:\n",
    "1. Create an `Experiment` in an existing `Workspace`.\n",
    "2. Configure AutoML using `AutoMLConfig`.\n",
    "4. Train the model.\n",
    "5. Explore the results.\n",
    "6. Test the best fitted model.\n",
    "\n",
    "In addition this notebook showcases the following features\n",
    "- **Blacklisting** certain pipelines\n",
    "- Specifying **target metrics** to indicate stopping criteria\n",
    "- Handling **missing data** in the input\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an Experiment\n",
    "\n",
    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# Choose a name for the experiment.\n",
    "experiment_name = 'automl-local-missing-data'\n",
    "project_folder = './sample_projects/automl-local-missing-data'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import sparse\n",
    "\n",
    "digits = datasets.load_digits()\n",
    "X_train = digits.data[10:,:]\n",
    "y_train = digits.target[10:]\n",
    "\n",
    "# Add missing values in 75% of the lines.\n",
    "missing_rate = 0.75\n",
    "n_missing_samples = int(np.floor(X_train.shape[0] * missing_rate))\n",
    "missing_samples = np.hstack((np.zeros(X_train.shape[0] - n_missing_samples, dtype=np.bool), np.ones(n_missing_samples, dtype=np.bool)))\n",
    "rng = np.random.RandomState(0)\n",
    "rng.shuffle(missing_samples)\n",
    "missing_features = rng.randint(0, X_train.shape[1], n_missing_samples)\n",
    "X_train[np.where(missing_samples)[0], missing_features] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data = X_train)\n",
    "df['Label'] = pd.Series(y_train, index=df.index)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure AutoML\n",
    "\n",
    "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.  This includes setting `experiment_exit_score`, which should cause the run to complete before the `iterations` count is reached.\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**task**|classification or regression|\n",
    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
    "|**n_cross_validations**|Number of cross validation splits.|\n",
    "|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.|\n",
    "|**experiment_exit_score**|*double* value indicating the target for *primary_metric*. <br>Once the target is surpassed the run terminates.|\n",
    "|**blacklist_models**|*List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run.<br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGD</i><br><i>MultinomialNaiveBayes</i><br><i>BernoulliNaiveBayes</i><br><i>SVM</i><br><i>LinearSVM</i><br><i>KNN</i><br><i>DecisionTree</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>GradientBoosting</i><br><i>TensorFlowDNN</i><br><i>TensorFlowLinearClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i>|\n",
    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers.|\n",
    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             primary_metric = 'AUC_weighted',\n",
    "                             iteration_timeout_minutes = 60,\n",
    "                             iterations = 20,\n",
    "                             n_cross_validations = 5,\n",
    "                             preprocess = True,\n",
    "                             experiment_exit_score = 0.9984,\n",
    "                             blacklist_models = ['KNN','LinearSVM'],\n",
    "                             verbosity = logging.INFO,\n",
    "                             X = X_train, \n",
    "                             y = y_train,\n",
    "                             path = project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Models\n",
    "\n",
    "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
    "In this example, we specify `show_output = True` to print currently running iterations to the console."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = experiment.submit(automl_config, show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Widget for Monitoring Runs\n",
    "\n",
    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
    "\n",
    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "RunDetails(local_run).show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(local_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = local_run.get_output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model Based on Any Other Metric\n",
    "Show the run and the model which has the smallest `accuracy` value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lookup_metric = \"accuracy\"\n",
    "# best_run, fitted_model = local_run.get_output(metric = lookup_metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a Specific Iteration\n",
    "Show the run and the model from the third iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# iteration = 3\n",
    "# best_run, fitted_model = local_run.get_output(iteration = iteration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the best Fitted Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_test = digits.data[:10, :]\n",
    "y_test = digits.target[:10]\n",
    "images = digits.images[:10]\n",
    "\n",
    "# Randomly select digits and test.\n",
    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
    "    print(index)\n",
    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
    "    label = y_test[index]\n",
    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
    "    fig = plt.figure(1, figsize=(3,3))\n",
    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
    "    ax1.set_title(title)\n",
    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
    "    plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "savitam"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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   "pygments_lexer": "ipython3",
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--- a/how-to-use-azureml/automated-machine-learning/06.auto-ml-sparse-data-train-test-split.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/06.auto-ml-sparse-data-train-test-split.ipynb
@@ -0,0 +1,384 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 06: Train Test Split and Handling Sparse Data\n",
    "\n",
    "In this example we use the scikit-learn's [20newsgroup](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_20newsgroups.html) to showcase how you can use AutoML for handling sparse data and how to specify custom cross validations splits.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you will learn how to:\n",
    "1. Create an `Experiment` in an existing `Workspace`.\n",
    "2. Configure AutoML using `AutoMLConfig`.\n",
    "4. Train the model.\n",
    "5. Explore the results.\n",
    "6. Test the best fitted model.\n",
    "\n",
    "In addition this notebook showcases the following features\n",
    "- Explicit train test splits \n",
    "- Handling **sparse data** in the input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an Experiment\n",
    "\n",
    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# choose a name for the experiment\n",
    "experiment_name = 'automl-local-missing-data'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-local-missing-data'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Sparse Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_20newsgroups\n",
    "from sklearn.feature_extraction.text import HashingVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "remove = ('headers', 'footers', 'quotes')\n",
    "categories = [\n",
    "    'alt.atheism',\n",
    "    'talk.religion.misc',\n",
    "    'comp.graphics',\n",
    "    'sci.space',\n",
    "]\n",
    "data_train = fetch_20newsgroups(subset = 'train', categories = categories,\n",
    "                                shuffle = True, random_state = 42,\n",
    "                                remove = remove)\n",
    "\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(data_train.data, data_train.target, test_size = 0.33, random_state = 42)\n",
    "\n",
    "\n",
    "vectorizer = HashingVectorizer(stop_words = 'english', alternate_sign = False,\n",
    "                               n_features = 2**16)\n",
    "X_train = vectorizer.transform(X_train)\n",
    "X_valid = vectorizer.transform(X_valid)\n",
    "\n",
    "summary_df = pd.DataFrame(index = ['No of Samples', 'No of Features'])\n",
    "summary_df['Train Set'] = [X_train.shape[0], X_train.shape[1]]\n",
    "summary_df['Validation Set'] = [X_valid.shape[0], X_valid.shape[1]]\n",
    "summary_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure AutoML\n",
    "\n",
    "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**task**|classification or regression|\n",
    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
    "|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.<br>**Note:** If input data is sparse, you cannot use *True*.|\n",
    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
    "|**X_valid**|(sparse) array-like, shape = [n_samples, n_features] for the custom validation set.|\n",
    "|**y_valid**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification for the custom validation set.|\n",
    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             primary_metric = 'AUC_weighted',\n",
    "                             iteration_timeout_minutes = 60,\n",
    "                             iterations = 5,\n",
    "                             preprocess = False,\n",
    "                             verbosity = logging.INFO,\n",
    "                             X = X_train, \n",
    "                             y = y_train,\n",
    "                             X_valid = X_valid, \n",
    "                             y_valid = y_valid, \n",
    "                             path = project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Models\n",
    "\n",
    "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
    "In this example, we specify `show_output = True` to print currently running iterations to the console."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = experiment.submit(automl_config, show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Widget for Monitoring Runs\n",
    "\n",
    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
    "\n",
    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "RunDetails(local_run).show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(local_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "    \n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = local_run.get_output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model Based on Any Other Metric\n",
    "Show the run and the model which has the smallest `accuracy` value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lookup_metric = \"accuracy\"\n",
    "# best_run, fitted_model = local_run.get_output(metric = lookup_metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a Specific Iteration\n",
    "Show the run and the model from the third iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# iteration = 3\n",
    "# best_run, fitted_model = local_run.get_output(iteration = iteration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the Best Fitted Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load test data.\n",
    "from pandas_ml import ConfusionMatrix\n",
    "\n",
    "data_test = fetch_20newsgroups(subset = 'test', categories = categories,\n",
    "                               shuffle = True, random_state = 42,\n",
    "                               remove = remove)\n",
    "\n",
    "X_test = vectorizer.transform(data_test.data)\n",
    "y_test = data_test.target\n",
    "\n",
    "# Test our best pipeline.\n",
    "\n",
    "y_pred = fitted_model.predict(X_test)\n",
    "y_pred_strings = [data_test.target_names[i] for i in y_pred]\n",
    "y_test_strings = [data_test.target_names[i] for i in y_test]\n",
    "\n",
    "cm = ConfusionMatrix(y_test_strings, y_pred_strings)\n",
    "print(cm)\n",
    "cm.plot()"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "savitam"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/07.auto-ml-exploring-previous-runs.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/07.auto-ml-exploring-previous-runs.ipynb
@@ -0,0 +1,336 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 07: Exploring Previous Runs\n",
    "\n",
    "In this example we present some examples on navigating previously executed runs. We also show how you can download a fitted model for any previous run.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you will learn how to:\n",
    "1. List all experiments in a workspace.\n",
    "2. List all AutoML runs in an experiment.\n",
    "3. Get details for an AutoML run, including settings, run widget, and all metrics.\n",
    "4. Download a fitted pipeline for any iteration.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# List all AutoML Experiments in a Workspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "import re\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.run import Run\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "experiment_list = Experiment.list(workspace=ws)\n",
    "\n",
    "summary_df = pd.DataFrame(index = ['No of Runs'])\n",
    "pattern = re.compile('^AutoML_[^_]*$')\n",
    "for experiment in experiment_list:\n",
    "    all_runs = list(experiment.get_runs())\n",
    "    automl_runs = []\n",
    "    for run in all_runs:\n",
    "        if(pattern.match(run.id)):\n",
    "            automl_runs.append(run)    \n",
    "    summary_df[experiment.name] = [len(automl_runs)]\n",
    "    \n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "summary_df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# List AutoML runs for an experiment\n",
    "Set `experiment_name` to any experiment name from the result of the Experiment.list cell to load the AutoML runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_name = 'automl-local-classification' # Replace this with any project name from previous cell.\n",
    "\n",
    "proj = ws.experiments[experiment_name]\n",
    "summary_df = pd.DataFrame(index = ['Type', 'Status', 'Primary Metric', 'Iterations', 'Compute', 'Name'])\n",
    "pattern = re.compile('^AutoML_[^_]*$')\n",
    "all_runs = list(proj.get_runs(properties={'azureml.runsource': 'automl'}))\n",
    "automl_runs_project = []\n",
    "for run in all_runs:\n",
    "    if(pattern.match(run.id)):\n",
    "        properties = run.get_properties()\n",
    "        tags = run.get_tags()\n",
    "        amlsettings = eval(properties['RawAMLSettingsString'])\n",
    "        if 'iterations' in tags:\n",
    "            iterations = tags['iterations']\n",
    "        else:\n",
    "            iterations = properties['num_iterations']\n",
    "        summary_df[run.id] = [amlsettings['task_type'], run.get_details()['status'], properties['primary_metric'], iterations, properties['target'], amlsettings['name']]\n",
    "        if run.get_details()['status'] == 'Completed':\n",
    "            automl_runs_project.append(run.id)\n",
    "    \n",
    "from IPython.display import HTML\n",
    "projname_html = HTML(\"<h3>{}</h3>\".format(proj.name))\n",
    "\n",
    "from IPython.display import display\n",
    "display(projname_html)\n",
    "display(summary_df.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get details for an AutoML run\n",
    "\n",
    "Copy the project name and run id from the previous cell output to find more details on a particular run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_id = automl_runs_project[0]  # Replace with your own run_id from above run ids\n",
    "assert (run_id in summary_df.keys()), \"Run id not found! Please set run id to a value from above run ids\"\n",
    "\n",
    "from azureml.widgets import RunDetails\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "ml_run = AutoMLRun(experiment = experiment, run_id = run_id)\n",
    "\n",
    "summary_df = pd.DataFrame(index = ['Type', 'Status', 'Primary Metric', 'Iterations', 'Compute', 'Name', 'Start Time', 'End Time'])\n",
    "properties = ml_run.get_properties()\n",
    "tags = ml_run.get_tags()\n",
    "status = ml_run.get_details()\n",
    "amlsettings = eval(properties['RawAMLSettingsString'])\n",
    "if 'iterations' in tags:\n",
    "    iterations = tags['iterations']\n",
    "else:\n",
    "    iterations = properties['num_iterations']\n",
    "start_time = None\n",
    "if 'startTimeUtc' in status:\n",
    "    start_time = status['startTimeUtc']\n",
    "end_time = None\n",
    "if 'endTimeUtc' in status:\n",
    "    end_time = status['endTimeUtc']\n",
    "summary_df[ml_run.id] = [amlsettings['task_type'], status['status'], properties['primary_metric'], iterations, properties['target'], amlsettings['name'], start_time, end_time]\n",
    "display(HTML('<h3>Runtime Details</h3>'))\n",
    "display(summary_df)\n",
    "\n",
    "#settings_df = pd.DataFrame(data = amlsettings, index = [''])\n",
    "display(HTML('<h3>AutoML Settings</h3>'))\n",
    "display(amlsettings)\n",
    "\n",
    "display(HTML('<h3>Iterations</h3>'))\n",
    "RunDetails(ml_run).show() \n",
    "\n",
    "children = list(ml_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "display(HTML('<h3>Metrics</h3>'))\n",
    "display(rundata)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download fitted models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download the Best Model for Any Given Metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metric = 'AUC_weighted' # Replace with a metric name.\n",
    "best_run, fitted_model = ml_run.get_output(metric = metric)\n",
    "fitted_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download the Model for Any Given Iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 1 # Replace with an iteration number.\n",
    "best_run, fitted_model = ml_run.get_output(iteration = iteration)\n",
    "fitted_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Register fitted model for deployment\n",
    "If neither `metric` nor `iteration` are specified in the `register_model` call, the iteration with the best primary metric is registered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "description = 'AutoML Model'\n",
    "tags = None\n",
    "ml_run.register_model(description = description, tags = tags)\n",
    "ml_run.model_id # Use this id to deploy the model as a web service in Azure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Register the Best Model for Any Given Metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metric = 'AUC_weighted' # Replace with a metric name.\n",
    "description = 'AutoML Model'\n",
    "tags = None\n",
    "ml_run.register_model(description = description, tags = tags, metric = metric)\n",
    "print(ml_run.model_id) # Use this id to deploy the model as a web service in Azure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Register the Model for Any Given Iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 1 # Replace with an iteration number.\n",
    "description = 'AutoML Model'\n",
    "tags = None\n",
    "ml_run.register_model(description = description, tags = tags, iteration = iteration)\n",
    "print(ml_run.model_id) # Use this id to deploy the model as a web service in Azure."
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "savitam"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/08.auto-ml-remote-execution-with-DataStore.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/08.auto-ml-remote-execution-with-DataStore.ipynb
@@ -0,0 +1,588 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 08: Remote Execution with DataStore\n",
    "\n",
    "This sample accesses a data file on a remote DSVM through DataStore. Advantages of using data store are:\n",
    "1. DataStore secures the access details.\n",
    "2. DataStore supports read, write to blob and file store\n",
    "3. AutoML natively supports copying data from DataStore to DSVM\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you would see\n",
    "1. Storing data in DataStore.\n",
    "2. get_data returning data from DataStore.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Experiment\n",
    "\n",
    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "import time\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.compute import DsvmCompute\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# choose a name for experiment\n",
    "experiment_name = 'automl-remote-datastore-file'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-remote-dsvm-file'\n",
    "\n",
    "experiment=Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Remote Linux DSVM\n",
    "Note: If creation fails with a message about Marketplace purchase eligibilty, go to portal.azure.com, start creating DSVM there, and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled it, you can exit without actually creating VM.\n",
    "\n",
    "**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you can switch to a different port (such as 5022), you can append the port number to the address. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "compute_target_name = 'mydsvmc'\n",
    "\n",
    "try:\n",
    "    while ws.compute_targets[compute_target_name].provisioning_state == 'Creating':\n",
    "        time.sleep(1)\n",
    "        \n",
    "    dsvm_compute = DsvmCompute(workspace=ws, name=compute_target_name)\n",
    "    print('found existing:', dsvm_compute.name)\n",
    "except:\n",
    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size=\"Standard_D2_v2\")\n",
    "    dsvm_compute = DsvmCompute.create(ws, name=compute_target_name, provisioning_configuration=dsvm_config)\n",
    "    dsvm_compute.wait_for_completion(show_output=True)\n",
    "    print(\"Waiting one minute for ssh to be accessible\")\n",
    "    time.sleep(60) # Wait for ssh to be accessible"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Copy data file to local\n",
    "\n",
    "Download the data file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mkdir data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
    "                     delimiter=\"\\t\", quotechar='\"')\n",
    "df.to_csv(\"data/data.tsv\", sep=\"\\t\", quotechar='\"', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upload data to the cloud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now make the data accessible remotely by uploading that data from your local machine into Azure so it can be accessed for remote training. The datastore is a convenient construct associated with your workspace for you to upload/download data, and interact with it from your remote compute targets. It is backed by Azure blob storage account.\n",
    "\n",
    "The data.tsv files are uploaded into a directory named data at the root of the datastore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Workspace, Datastore\n",
    "#blob_datastore = Datastore(ws, blob_datastore_name)\n",
    "ds = ws.get_default_datastore()\n",
    "print(ds.datastore_type, ds.account_name, ds.container_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ds.upload_files(\"data.tsv\")\n",
    "ds.upload(src_dir='./data', target_path='data', overwrite=True, show_progress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure & Run\n",
    "\n",
    "First let's create a DataReferenceConfigruation object to inform the system what data folder to download to the compute target.\n",
    "The path_on_compute should be an absolute path to ensure that the data files are downloaded only once.   The get_data method should use this same path to access the data files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.runconfig import DataReferenceConfiguration\n",
    "dr = DataReferenceConfiguration(datastore_name=ds.name, \n",
    "                   path_on_datastore='data', \n",
    "                   path_on_compute='/tmp/azureml_runs',\n",
    "                   mode='download', # download files from datastore to compute target\n",
    "                   overwrite=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.runconfig import RunConfiguration\n",
    "from azureml.core.conda_dependencies import CondaDependencies\n",
    "\n",
    "# create a new RunConfig object\n",
    "conda_run_config = RunConfiguration(framework=\"python\")\n",
    "\n",
    "# Set compute target to the Linux DSVM\n",
    "conda_run_config.target = dsvm_compute\n",
    "# set the data reference of the run coonfiguration\n",
    "conda_run_config.data_references = {ds.name: dr}\n",
    "\n",
    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
    "conda_run_config.environment.python.conda_dependencies = cd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Get Data File\n",
    "For remote executions you should author a get_data.py file containing a get_data() function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
    "\n",
    "The *get_data()* function returns a [dictionary](README.md#getdata).\n",
    "\n",
    "The read_csv uses the path_on_compute value specified in the DataReferenceConfiguration call plus the path_on_datastore folder and then the actual file name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(project_folder):\n",
    "    os.makedirs(project_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile $project_folder/get_data.py\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import os\n",
    "from os.path import expanduser, join, dirname\n",
    "\n",
    "def get_data():\n",
    "    # Burning man 2016 data\n",
    "    df = pd.read_csv(\"/tmp/azureml_runs/data/data.tsv\", delimiter=\"\\t\", quotechar='\"')\n",
    "    # get integer labels\n",
    "    le = LabelEncoder()\n",
    "    le.fit(df[\"Label\"].values)\n",
    "    y = le.transform(df[\"Label\"].values)\n",
    "    X = df.drop([\"Label\"], axis=1)\n",
    "\n",
    "    X_train, _, y_train, _ = train_test_split(X, y, test_size=0.1, random_state=42)\n",
    "\n",
    "    return { \"X\" : X_train.values, \"y\" : y_train }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
    "\n",
    "You can specify automl_settings as **kwargs** as well. Also note that you can use the get_data() symantic for local excutions too. \n",
    "\n",
    "<i>Note: For Remote DSVM and Batch AI you cannot pass Numpy arrays directly to AutoMLConfig.</i>\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration|\n",
    "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data|\n",
    "|**n_cross_validations**|Number of cross validation splits|\n",
    "|**max_concurrent_iterations**|Max number of iterations that would be executed in parallel.  This should be less than the number of cores on the DSVM\n",
    "|**preprocess**| *True/False* <br>Setting this to *True* enables Auto ML to perform preprocessing <br>on the input to handle *missing data*, and perform some common *feature extraction*|\n",
    "|**enable_cache**|Setting this to *True* enables preprocess done once and reuse the same preprocessed data for all the iterations. Default value is True.|\n",
    "|**max_cores_per_iteration**| Indicates how many cores on the compute target would be used to train a single pipeline.<br> Default is *1*, you can set it to *-1* to use all cores|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_settings = {\n",
    "    \"iteration_timeout_minutes\": 60,\n",
    "    \"iterations\": 4,\n",
    "    \"n_cross_validations\": 5,\n",
    "    \"primary_metric\": 'AUC_weighted',\n",
    "    \"preprocess\": True,\n",
    "    \"max_cores_per_iteration\": 1,\n",
    "    \"verbosity\": logging.INFO\n",
    "}\n",
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             path=project_folder,\n",
    "                             run_configuration=conda_run_config,\n",
    "                             #compute_target = dsvm_compute,\n",
    "                             data_script = project_folder + \"/get_data.py\",\n",
    "                             **automl_settings\n",
    "                            )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Models <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
    "\n",
    "For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets/models even when the experiment is running to retreive the best model up to that point. Once you are satisfied with the model you can cancel a particular iteration or the whole run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run = experiment.submit(automl_config, show_output=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
    "#### Widget for monitoring runs\n",
    "\n",
    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
    "\n",
    "You can click on a pipeline to see run properties and output logs. Logs are also available on the DSVM under /tmp/azureml_run/{iterationid}/azureml-logs\n",
    "\n",
    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "RunDetails(remote_run).show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wait until the run finishes.\n",
    "remote_run.wait_for_completion(show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use sdk methods to fetch all the child runs and see individual metrics that we log. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(remote_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Canceling Runs\n",
    "You can cancel ongoing remote runs using the *cancel()* and *cancel_iteration()* functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cancel the ongoing experiment and stop scheduling new iterations\n",
    "# remote_run.cancel()\n",
    "\n",
    "# Cancel iteration 1 and move onto iteration 2\n",
    "# remote_run.cancel_iteration(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-process cache cleanup\n",
    "The preprocess data gets cache at user default file store. When the run is completed the cache can be cleaned by running below cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run.clean_preprocessor_cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The *get_output* method returns the best run and the fitted model. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = remote_run.get_output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model based on any other metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lookup_metric = \"accuracy\"\n",
    "# best_run, fitted_model = remote_run.get_output(metric=lookup_metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a specific iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# iteration = 1\n",
    "# best_run, fitted_model = remote_run.get_output(iteration=iteration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the Best Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
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 "nbformat": 4,
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 }
--- a/how-to-use-azureml/automated-machine-learning/08b.auto-ml-remote-execution-with-dataprep.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/08b.auto-ml-remote-execution-with-dataprep.ipynb
@@ -0,0 +1,568 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 08b: Remote Execution with DataPrep\n",
    "\n",
    "This sample accesses a data file on a remote DSVM through Datastore using DataPrep. Advantages of using DataPrep are:\n",
    "1. DataPrep supports reading from and writing to datastores.\n",
    "2. DataPrep supports automatic file type and column type detection.\n",
    "3. DataPrep makes passing data into AutoML really simple.\n",
    "\n",
    "More DataPrep documentation and examples can be found [here](https://github.com/Microsoft/AMLDataPrepDocs).\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you would see\n",
    "1. Storing data in DataStore.\n",
    "2. Doing some basic data preparation using DataPrep and passing the prepared data (DataFlow) to AutoML for training (classficiation).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Experiment\n",
    "\n",
    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "import time\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.compute import DsvmCompute\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# choose a name for experiment\n",
    "experiment_name = 'automl-remote-datastore-file'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-remote-dsvm-file'\n",
    "\n",
    "experiment=Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Remote Linux DSVM\n",
    "Note: If creation fails with a message about Marketplace purchase eligibilty, go to portal.azure.com, start creating DSVM there, and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled it, you can exit without actually creating VM.\n",
    "\n",
    "**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you can switch to a different port (such as 5022), you can append the port number to the address. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "compute_target_name = 'automl-dataprep'\n",
    "\n",
    "try:\n",
    "    while ws.compute_targets[compute_target_name].provisioning_state == 'Creating':\n",
    "        time.sleep(1)\n",
    "        \n",
    "    dsvm_compute = DsvmCompute(workspace=ws, name=compute_target_name)\n",
    "    print('found existing:', dsvm_compute.name)\n",
    "except:\n",
    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size=\"Standard_D2_v2\")\n",
    "    dsvm_compute = DsvmCompute.create(ws, name=compute_target_name, provisioning_configuration=dsvm_config)\n",
    "    dsvm_compute.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Copy data file to local\n",
    "\n",
    "We will download a 1MB simple random sample of the Chicago Crime data into a local temporary directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tempfile\n",
    "import requests\n",
    "\n",
    "temp_folder = tempfile.mkdtemp()\n",
    "temp_tsv = os.path.join(temp_folder, 'crime0.csv')\n",
    "\n",
    "request = requests.get('https://dprepdata.blob.core.windows.net/demo/crime0-random.csv')\n",
    "with open(temp_tsv, 'w', encoding='utf-8') as f:\n",
    "    f.write(request.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upload data to the cloud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's make the data available in your datastore. Datastore is a convenient construct associated with your workspace for you to  reference different types of cloud storage locations (e.g. Azure Blob Containers, Azure File Shares, Azure Data Lake Stores, etc.). The benefit Datastore brings is you only need to register datastores once and you will be able to access them by name and will not need to expose secrets in your code. When you first create a workspace, a default datastore is registered for you which references the Azure Blob Container that was provisioned with the workspace. Let's upload the data we just got from the public location to the default datastore.\n",
    "\n",
    "The `csv` file is uploaded into a directory named `datasets` at the root of the datastore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Workspace, Datastore\n",
    "\n",
    "ds = ws.get_default_datastore()\n",
    "print(ds.datastore_type, ds.account_name, ds.container_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.upload(src_dir=temp_folder, target_path='datasets', overwrite=True, show_progress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Dataflow using DataPrep\n",
    "Let's use DataPrep to read the `csv` file from the datastore we just uploaded to and get the data profile to make sure our data looks good. We will predict the type of the offense (`Primary Type`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.dataprep as dprep\n",
    "\n",
    "dflow = dprep.read_csv(path=ds.path('datasets/crime0.csv'))\n",
    "dflow.get_profile()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also take a look at the first 5 rows of the data to give ourselves an idea of what the data looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dflow.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the first 5 rows, we see that there are some rows that have no value in the label column (`Primary Type`). Let's remove those rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dflow = dflow.drop_nulls('Primary Type')\n",
    "dflow.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've removed those rows, let's split the dataflow into a features dataflow and a label dataflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dflow.drop_columns(columns=['Primary Type', 'FBI Code'])\n",
    "y = dflow.keep_columns(columns=['Primary Type'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
    "\n",
    "You can specify automl_settings as **kwargs** as well. Also note that you can use the get_data() symantic for local excutions too. \n",
    "\n",
    "<i>Note: For Remote DSVM and Batch AI you cannot pass Numpy arrays directly to AutoMLConfig.</i>\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration|\n",
    "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data|\n",
    "|**n_cross_validations**|Number of cross validation splits|\n",
    "|**max_concurrent_iterations**|Max number of iterations that would be executed in parallel.  This should be less than the number of cores on the DSVM\n",
    "|**preprocess**| *True/False* <br>Setting this to *True* enables Auto ML to perform preprocessing <br>on the input to handle *missing data*, and perform some common *feature extraction*|\n",
    "|**enable_cache**|Setting this to *True* enables preprocess done once and reuse the same preprocessed data for all the iterations. Default value is True.|\n",
    "|**max_cores_per_iteration**| Indicates how many cores on the compute target would be used to train a single pipeline.<br> Default is *1*, you can set it to *-1* to use all cores|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.runconfig import RunConfiguration\n",
    "from azureml.core.conda_dependencies import CondaDependencies\n",
    "\n",
    "conda_run_config = RunConfiguration(framework=\"python\")\n",
    "\n",
    "conda_run_config.target = dsvm_compute\n",
    "\n",
    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]==0.1.0.1918169'], conda_packages=['numpy'], pin_sdk_version=False, pip_indexurl='https://azuremlsdktestpypi.azureedge.net/sdk-release/master/588E708E0DF342C4A80BD954289657CF')\n",
    "conda_run_config.environment.python.conda_dependencies = cd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_settings = {\n",
    "    \"iteration_timeout_minutes\": 60,\n",
    "    \"iterations\": 4,\n",
    "    \"n_cross_validations\": 5,\n",
    "    \"primary_metric\": 'accuracy',\n",
    "    \"preprocess\": True,\n",
    "    \"max_cores_per_iteration\": 1,\n",
    "    \"verbosity\": logging.INFO\n",
    "}\n",
    "automl_config = AutoMLConfig(task='classification',\n",
    "                             debug_log='automl_errors.log',\n",
    "                             path=project_folder,\n",
    "                             run_configuration=conda_run_config,\n",
    "                             X=X,\n",
    "                             y=y,\n",
    "                             **automl_settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Models <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
    "\n",
    "For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets/models even when the experiment is running to retreive the best model up to that point. Once you are satisfied with the model you can cancel a particular iteration or the whole run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run = experiment.submit(automl_config, show_output=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
    "#### Widget for monitoring runs\n",
    "\n",
    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
    "\n",
    "You can click on a pipeline to see run properties and output logs. Logs are also available on the DSVM under /tmp/azureml_run/{iterationid}/azureml-logs\n",
    "\n",
    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "RunDetails(remote_run).show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wait until the run finishes.\n",
    "remote_run.wait_for_completion(show_output = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use sdk methods to fetch all the child runs and see individual metrics that we log. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(remote_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Canceling Runs\n",
    "You can cancel ongoing remote runs using the *cancel()* and *cancel_iteration()* functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cancel the ongoing experiment and stop scheduling new iterations\n",
    "# remote_run.cancel()\n",
    "\n",
    "# Cancel iteration 1 and move onto iteration 2\n",
    "# remote_run.cancel_iteration(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-process cache cleanup\n",
    "The preprocess data gets cache at user default file store. When the run is completed the cache can be cleaned by running below cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run.clean_preprocessor_cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The *get_output* method returns the best run and the fitted model. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = remote_run.get_output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model based on any other metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lookup_metric = \"accuracy\"\n",
    "# best_run, fitted_model = remote_run.get_output(metric=lookup_metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a specific iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# iteration = 1\n",
    "# best_run, fitted_model = remote_run.get_output(iteration=iteration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the Best Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dflow = dprep.read_csv(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n",
    "dflow.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas_ml import ConfusionMatrix\n",
    "\n",
    "y_test = dflow.keep_columns(columns=['Primary Type']).to_pandas_dataframe()\n",
    "X_test = dflow.drop_columns(columns=['Primary Type', 'FBI Code']).to_pandas_dataframe()\n",
    "\n",
    "ypred = fitted_model.predict(X_test.values)\n",
    "\n",
    "cm = ConfusionMatrix(y_test['Primary Type'], ypred)\n",
    "\n",
    "print(cm)\n",
    "\n",
    "cm.plot()"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "savitam"
   }
  ],
  "kernelspec": {
   "display_name": "Python [conda env:cli_dev]",
   "language": "python",
   "name": "conda-env-cli_dev-py"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/09.auto-ml-classification-with-deployment.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/09.auto-ml-classification-with-deployment.ipynb
@@ -0,0 +1,501 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 09: Classification with Deployment\n",
    "\n",
    "In this example we use the scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) to showcase how you can use AutoML for a simple classification problem and deploy it to an Azure Container Instance (ACI).\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you will learn how to:\n",
    "1. Create an experiment using an existing workspace.\n",
    "2. Configure AutoML using `AutoMLConfig`.\n",
    "3. Train the model using local compute.\n",
    "4. Explore the results.\n",
    "5. Register the model.\n",
    "6. Create a container image.\n",
    "7. Create an Azure Container Instance (ACI) service.\n",
    "8. Test the ACI service.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an Experiment\n",
    "\n",
    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# choose a name for experiment\n",
    "experiment_name = 'automl-local-classification'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-local-classification'\n",
    "\n",
    "experiment=Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure AutoML\n",
    "\n",
    "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**task**|classification or regression|\n",
    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
    "|**n_cross_validations**|Number of cross validation splits.|\n",
    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_train = digits.data[10:,:]\n",
    "y_train = digits.target[10:]\n",
    "\n",
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             name = experiment_name,\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             primary_metric = 'AUC_weighted',\n",
    "                             iteration_timeout_minutes = 20,\n",
    "                             iterations = 10,\n",
    "                             n_cross_validations = 2,\n",
    "                             verbosity = logging.INFO,\n",
    "                             X = X_train, \n",
    "                             y = y_train,\n",
    "                             path = project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Models\n",
    "\n",
    "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
    "In this example, we specify `show_output = True` to print currently running iterations to the console."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = experiment.submit(automl_config, show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = local_run.get_output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Register the Fitted Model for Deployment\n",
    "If neither `metric` nor `iteration` are specified in the `register_model` call, the iteration with the best primary metric is registered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "description = 'AutoML Model'\n",
    "tags = None\n",
    "model = local_run.register_model(description = description, tags = tags)\n",
    "local_run.model_id # This will be written to the script file later in the notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Scoring Script"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile score.py\n",
    "import pickle\n",
    "import json\n",
    "import numpy\n",
    "import azureml.train.automl\n",
    "from sklearn.externals import joblib\n",
    "from azureml.core.model import Model\n",
    "\n",
    "\n",
    "def init():\n",
    "    global model\n",
    "    model_path = Model.get_model_path(model_name = '<<modelid>>') # this name is model.id of model that we want to deploy\n",
    "    # deserialize the model file back into a sklearn model\n",
    "    model = joblib.load(model_path)\n",
    "\n",
    "def run(rawdata):\n",
    "    try:\n",
    "        data = json.loads(rawdata)['data']\n",
    "        data = numpy.array(data)\n",
    "        result = model.predict(data)\n",
    "    except Exception as e:\n",
    "        result = str(e)\n",
    "        return json.dumps({\"error\": result})\n",
    "    return json.dumps({\"result\":result.tolist()})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a YAML File for the Environment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To ensure the fit results are consistent with the training results, the SDK dependency versions need to be the same as the environment that trains the model. Details about retrieving the versions can be found in notebook [12.auto-ml-retrieve-the-training-sdk-versions](12.auto-ml-retrieve-the-training-sdk-versions.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_name = 'automl-local-classification'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "ml_run = AutoMLRun(experiment = experiment, run_id = local_run.id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dependencies = ml_run.get_run_sdk_dependencies(iteration = 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
    "    print('{}\\t{}'.format(p, dependencies[p]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.conda_dependencies import CondaDependencies\n",
    "\n",
    "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-sdk[automl]'])\n",
    "\n",
    "conda_env_file_name = 'myenv.yml'\n",
    "myenv.save_to_file('.', conda_env_file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Substitute the actual version number in the environment file.\n",
    "# This is not strictly needed in this notebook because the model should have been generated using the current SDK version.\n",
    "# However, we include this in case this code is used on an experiment from a previous SDK version.\n",
    "\n",
    "with open(conda_env_file_name, 'r') as cefr:\n",
    "    content = cefr.read()\n",
    "\n",
    "with open(conda_env_file_name, 'w') as cefw:\n",
    "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
    "\n",
    "# Substitute the actual model id in the script file.\n",
    "\n",
    "script_file_name = 'score.py'\n",
    "\n",
    "with open(script_file_name, 'r') as cefr:\n",
    "    content = cefr.read()\n",
    "\n",
    "with open(script_file_name, 'w') as cefw:\n",
    "    cefw.write(content.replace('<<modelid>>', local_run.model_id))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a Container Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.image import Image, ContainerImage\n",
    "\n",
    "image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
    "                                 execution_script = script_file_name,\n",
    "                                 conda_file = conda_env_file_name,\n",
    "                                 tags = {'area': \"digits\", 'type': \"automl_classification\"},\n",
    "                                 description = \"Image for automl classification sample\")\n",
    "\n",
    "image = Image.create(name = \"automlsampleimage\",\n",
    "                     # this is the model object \n",
    "                     models = [model],\n",
    "                     image_config = image_config, \n",
    "                     workspace = ws)\n",
    "\n",
    "image.wait_for_creation(show_output = True)\n",
    "\n",
    "if image.creation_state == 'Failed':\n",
    "    print(\"Image build log at: \" + image.image_build_log_uri)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deploy the Image as a Web Service on Azure Container Instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.webservice import AciWebservice\n",
    "\n",
    "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
    "                                               memory_gb = 1, \n",
    "                                               tags = {'area': \"digits\", 'type': \"automl_classification\"}, \n",
    "                                               description = 'sample service for Automl Classification')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.webservice import Webservice\n",
    "\n",
    "aci_service_name = 'automl-sample-01'\n",
    "print(aci_service_name)\n",
    "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
    "                                           image = image,\n",
    "                                           name = aci_service_name,\n",
    "                                           workspace = ws)\n",
    "aci_service.wait_for_deployment(True)\n",
    "print(aci_service.state)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Delete a Web Service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#aci_service.delete()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Logs from a Deployed Web Service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#aci_service.get_logs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test a Web Service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomly select digits and test\n",
    "digits = datasets.load_digits()\n",
    "X_test = digits.data[:10, :]\n",
    "y_test = digits.target[:10]\n",
    "images = digits.images[:10]\n",
    "\n",
    "for index in np.random.choice(len(y_test), 3, replace = False):\n",
    "    print(index)\n",
    "    test_sample = json.dumps({'data':X_test[index:index + 1].tolist()})\n",
    "    predicted = aci_service.run(input_data = test_sample)\n",
    "    label = y_test[index]\n",
    "    predictedDict = json.loads(predicted)\n",
    "    title = \"Label value = %d  Predicted value = %s \" % ( label,predictedDict['result'][0])\n",
    "    fig = plt.figure(1, figsize = (3,3))\n",
    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
    "    ax1.set_title(title)\n",
    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "savitam"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/10.auto-ml-multi-output-example.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/10.auto-ml-multi-output-example.ipynb
@@ -0,0 +1,294 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 10: Multi-output\n",
    "\n",
    "This notebook shows how to use AutoML to train multi-output problems by leveraging the correlation between the outputs using indicator vectors.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformer Functions\n",
    "The transformations of inputs `X` and `y` are happening as follows, e.g. `y = {y_1, y_2}`, then `X` becomes\n",
    "    \n",
    "`X 1 0`\n",
    "     \n",
    "`X 0 1`\n",
    "\n",
    "and `y` becomes,\n",
    "\n",
    "`y_1`\n",
    "\n",
    "`y_2`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import sparse\n",
    "from scipy import linalg\n",
    "\n",
    "#Transformer functions\n",
    "def multi_output_transform_x_y(X, y):\n",
    "    X_new = multi_output_transformer_x(X, y.shape[1])\n",
    "    y_new = multi_output_transform_y(y)\n",
    "    return X_new, y_new\n",
    "\n",
    "def multi_output_transformer_x(X, number_of_columns_y):\n",
    "    indicator_vecs = linalg.block_diag(*([np.ones((X.shape[0], 1))] * number_of_columns_y))\n",
    "    if sparse.issparse(X):\n",
    "        X_new = sparse.vstack(np.tile(X, number_of_columns_y))\n",
    "        indicator_vecs = sparse.coo_matrix(indicator_vecs)\n",
    "        X_new = sparse.hstack((X_new, indicator_vecs))\n",
    "    else:\n",
    "        X_new = np.tile(X, (number_of_columns_y, 1))\n",
    "        X_new = np.hstack((X_new, indicator_vecs))\n",
    "    return X_new\n",
    "\n",
    "def multi_output_transform_y(y):\n",
    "    return y.reshape(-1, order=\"F\")\n",
    "\n",
    "def multi_output_inverse_transform_y(y, number_of_columns_y):\n",
    "    return y.reshape((-1, number_of_columns_y), order = \"F\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AutoML Experiment Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# Choose a name for the experiment and specify the project folder.\n",
    "experiment_name = 'automl-local-multi-output'\n",
    "project_folder = './sample_projects/automl-local-multi-output'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Random Dataset for Test Purposes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.RandomState(1)\n",
    "X_train = np.sort(200 * rng.rand(600, 1) - 100, axis = 0)\n",
    "y_train = np.array([np.pi * np.sin(X_train).ravel(), np.pi * np.cos(X_train).ravel()]).T\n",
    "y_train += (0.5 - rng.rand(*y_train.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform X and y transformation using the transformer function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_transformed, y_train_transformed = multi_output_transform_x_y(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Configure AutoML using the transformed results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_config = AutoMLConfig(task = 'regression',\n",
    "                             debug_log = 'automl_errors_multi.log',\n",
    "                             primary_metric = 'r2_score',\n",
    "                             iterations = 10,\n",
    "                             n_cross_validations = 2,\n",
    "                             verbosity = logging.INFO,\n",
    "                             X = X_train_transformed,\n",
    "                             y = y_train_transformed,\n",
    "                             path = project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit the Transformed Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = experiment.submit(automl_config, show_output = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the best fit model.\n",
    "best_run, fitted_model = local_run.get_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate random data set for predicting.\n",
    "X_test = np.sort(200 * rng.rand(200, 1) - 100, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform predict data.\n",
    "X_test_transformed = multi_output_transformer_x(X_test, y_train.shape[1])\n",
    "\n",
    "# Predict and inverse transform the prediction.\n",
    "y_predict = fitted_model.predict(X_test_transformed)\n",
    "y_predict = multi_output_inverse_transform_y(y_predict, y_train.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(y_predict)"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "savitam"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/11.auto-ml-sample-weight.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/11.auto-ml-sample-weight.ipynb
@@ -0,0 +1,251 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 11: Sample Weight\n",
    "\n",
    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use sample weight with AutoML. Sample weight is used where some sample values are more important than others.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you will learn how to configure AutoML to use `sample_weight` and you will see the difference sample weight makes to the test results.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an Experiment\n",
    "\n",
    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# Choose names for the regular and the sample weight experiments.\n",
    "experiment_name = 'non_sample_weight_experiment'\n",
    "sample_weight_experiment_name = 'sample_weight_experiment'\n",
    "\n",
    "project_folder = './sample_projects/automl-local-classification'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "sample_weight_experiment=Experiment(ws, sample_weight_experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace Name'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure AutoML\n",
    "\n",
    "Instantiate two `AutoMLConfig` objects. One will be used with `sample_weight` and one without."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_train = digits.data[100:,:]\n",
    "y_train = digits.target[100:]\n",
    "\n",
    "# The example makes the sample weight 0.9 for the digit 4 and 0.1 for all other digits.\n",
    "# This makes the model more likely to classify as 4 if the image it not clear.\n",
    "sample_weight = np.array([(0.9 if x == 4 else 0.01) for x in y_train])\n",
    "\n",
    "automl_classifier = AutoMLConfig(task = 'classification',\n",
    "                                 debug_log = 'automl_errors.log',\n",
    "                                 primary_metric = 'AUC_weighted',\n",
    "                                 iteration_timeout_minutes = 60,\n",
    "                                 iterations = 10,\n",
    "                                 n_cross_validations = 2,\n",
    "                                 verbosity = logging.INFO,\n",
    "                                 X = X_train, \n",
    "                                 y = y_train,\n",
    "                                 path = project_folder)\n",
    "\n",
    "automl_sample_weight = AutoMLConfig(task = 'classification',\n",
    "                                    debug_log = 'automl_errors.log',\n",
    "                                    primary_metric = 'AUC_weighted',\n",
    "                                    iteration_timeout_minutes = 60,\n",
    "                                    iterations = 10,\n",
    "                                    n_cross_validations = 2,\n",
    "                                    verbosity = logging.INFO,\n",
    "                                    X = X_train, \n",
    "                                    y = y_train,\n",
    "                                    sample_weight = sample_weight,\n",
    "                                    path = project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Models\n",
    "\n",
    "Call the `submit` method on the experiment objects and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
    "In this example, we specify `show_output = True` to print currently running iterations to the console."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = experiment.submit(automl_classifier, show_output = True)\n",
    "sample_weight_run = sample_weight_experiment.submit(automl_sample_weight, show_output = True)\n",
    "\n",
    "best_run, fitted_model = local_run.get_output()\n",
    "best_run_sample_weight, fitted_model_sample_weight = sample_weight_run.get_output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Best Fitted Model\n",
    "\n",
    "#### Load Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_test = digits.data[:100, :]\n",
    "y_test = digits.target[:100]\n",
    "images = digits.images[:100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compare the Models\n",
    "The prediction from the sample weight model is more likely to correctly predict 4's.  However, it is also more likely to predict 4 for some images that are not labelled as 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Randomly select digits and test.\n",
    "for index in range(0,len(y_test)):\n",
    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
    "    predicted_sample_weight = fitted_model_sample_weight.predict(X_test[index:index + 1])[0]\n",
    "    label = y_test[index]\n",
    "    if predicted == 4 or predicted_sample_weight == 4 or label == 4:\n",
    "        title = \"Label value = %d  Predicted value = %d Prediced with sample weight = %d\" % (label, predicted, predicted_sample_weight)\n",
    "        fig = plt.figure(1, figsize=(3,3))\n",
    "        ax1 = fig.add_axes((0,0,.8,.8))\n",
    "        ax1.set_title(title)\n",
    "        plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
    "        plt.show()"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "savitam"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/12.auto-ml-retrieve-the-training-sdk-versions.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/12.auto-ml-retrieve-the-training-sdk-versions.ipynb
@@ -0,0 +1,227 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 12: Retrieving Training SDK Versions\n",
    "\n",
    "This example shows how to find the SDK versions used for an experiment.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train models using AutoML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# Choose a name for the experiment and specify the project folder.\n",
    "experiment_name = 'automl-local-classification'\n",
    "project_folder = './sample_projects/automl-local-classification'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_train = digits.data[10:,:]\n",
    "y_train = digits.target[10:]\n",
    "\n",
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             primary_metric = 'AUC_weighted',\n",
    "                             iterations = 3,\n",
    "                             n_cross_validations = 2,\n",
    "                             verbosity = logging.INFO,\n",
    "                             X = X_train, \n",
    "                             y = y_train,\n",
    "                             path = project_folder)\n",
    "\n",
    "local_run = experiment.submit(automl_config, show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Retrieve the SDK versions from RunHistory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the SDK versions from RunHistory, first the run id needs to be recorded. This can either be done by copying it from the output message or by retrieving it after each run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use a run id copied from an output message.\n",
    "#run_id = 'AutoML_c0585b1f-a0e6-490b-84c7-3a099468b28e'\n",
    "\n",
    "# Retrieve the run id from a run.\n",
    "run_id = local_run.id\n",
    "print(run_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize a new `AutoMLRun` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_name = 'automl-local-classification'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "ml_run = AutoMLRun(experiment = experiment, run_id = run_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get parent training SDK versions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_run.get_run_sdk_dependencies()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the traning SDK versions of a specific run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_run.get_run_sdk_dependencies(iteration = 2)"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "savitam"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/13a.auto-ml-dataprep.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/13a.auto-ml-dataprep.ipynb
@@ -0,0 +1,446 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 13: Prepare Data using `azureml.dataprep` for Local Execution\n",
    "In this example we showcase how you can use the `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone; full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
    "\n",
    "Make sure you have executed the [setup](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you will learn how to:\n",
    "1. Define data loading and preparation steps in a `Dataflow` using `azureml.dataprep`.\n",
    "2. Pass the `Dataflow` to AutoML for a local run.\n",
    "3. Pass the `Dataflow` to AutoML for a remote run."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an Experiment\n",
    "\n",
    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "import azureml.dataprep as dprep\n",
    "from azureml.train.automl import AutoMLConfig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    " \n",
    "# choose a name for experiment\n",
    "experiment_name = 'automl-dataprep-local'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-dataprep-local'\n",
    " \n",
    "experiment = Experiment(ws, experiment_name)\n",
    " \n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace Name'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data using DataPrep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
    "# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
    "simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
    "X = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1)  # Remove the header row.\n",
    "\n",
    "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
    "# and convert column types manually.\n",
    "# Here we read a comma delimited file and convert all columns to integers.\n",
    "y = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Review the Data Preparation Result\n",
    "\n",
    "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.skip(1).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure AutoML\n",
    "\n",
    "This creates a general AutoML settings object applicable for both local and remote runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_settings = {\n",
    "    \"iteration_timeout_minutes\" : 10,\n",
    "    \"iterations\" : 2,\n",
    "    \"primary_metric\" : 'AUC_weighted',\n",
    "    \"preprocess\" : False,\n",
    "    \"verbosity\" : logging.INFO,\n",
    "    \"n_cross_validations\": 3\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Local Run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pass Data with `Dataflow` Objects\n",
    "\n",
    "The `Dataflow` objects captured above can be passed to the `submit` method for a local run. AutoML will retrieve the results from the `Dataflow` for model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             X = X,\n",
    "                             y = y,\n",
    "                             **automl_settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = experiment.submit(automl_config, show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Widget for Monitoring Runs\n",
    "\n",
    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
    "\n",
    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "RunDetails(local_run).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retrieve All Child Runs\n",
    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(local_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "    \n",
    "import pandas as pd\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = local_run.get_output()\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model Based on Any Other Metric\n",
    "Show the run and the model that has the smallest `log_loss` value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_metric = \"log_loss\"\n",
    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a Specific Iteration\n",
    "Show the run and the model from the first iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 0\n",
    "best_run, fitted_model = local_run.get_output(iteration = iteration)\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Best Fitted Model\n",
    "\n",
    "#### Load Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "digits = datasets.load_digits()\n",
    "X_test = digits.data[:10, :]\n",
    "y_test = digits.target[:10]\n",
    "images = digits.images[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing Our Best Fitted Model\n",
    "We will try to predict 2 digits and see how our model works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomly select digits and test\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import random\n",
    "import numpy as np\n",
    "\n",
    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
    "    print(index)\n",
    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
    "    label = y_test[index]\n",
    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
    "    fig = plt.figure(1, figsize=(3,3))\n",
    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
    "    ax1.set_title(title)\n",
    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appendix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Capture the `Dataflow` Objects for Later Use in AutoML\n",
    "\n",
    "`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object can be branched at any point for further usage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sklearn.digits.data + target\n",
    "digits_complete = dprep.auto_read_file('https://dprepdata.blob.core.windows.net/automl-notebook-data/digits-complete.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`digits_complete` (sourced from `sklearn.datasets.load_digits()`) is forked into `dflow_X` to capture all the feature columns and `dflow_y` to capture the label column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits_complete.to_pandas_dataframe().shape\n",
    "labels_column = 'Column64'\n",
    "dflow_X = digits_complete.drop_columns(columns = [labels_column])\n",
    "dflow_y = digits_complete.keep_columns(columns = [labels_column])"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "savitam"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/13b.auto-ml-dataprep-remote-execution.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/13b.auto-ml-dataprep-remote-execution.ipynb
@@ -0,0 +1,497 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 13: Prepare Data using `azureml.dataprep` for Remote Execution (DSVM)\n",
    "In this example we showcase how you can use the `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone; full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
    "\n",
    "Make sure you have executed the [setup](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you will learn how to:\n",
    "1. Define data loading and preparation steps in a `Dataflow` using `azureml.dataprep`.\n",
    "2. Pass the `Dataflow` to AutoML for a local run.\n",
    "3. Pass the `Dataflow` to AutoML for a remote run."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an Experiment\n",
    "\n",
    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import time\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.compute import DsvmCompute\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "import azureml.dataprep as dprep\n",
    "from azureml.train.automl import AutoMLConfig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    " \n",
    "# choose a name for experiment\n",
    "experiment_name = 'automl-dataprep-remote-dsvm'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-dataprep-remote-dsvm'\n",
    " \n",
    "experiment = Experiment(ws, experiment_name)\n",
    " \n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace Name'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data using DataPrep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
    "# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
    "simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
    "X = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1)  # Remove the header row.\n",
    "\n",
    "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
    "# and convert column types manually.\n",
    "# Here we read a comma delimited file and convert all columns to integers.\n",
    "y = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Review the Data Preparation Result\n",
    "\n",
    "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.skip(1).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure AutoML\n",
    "\n",
    "This creates a general AutoML settings object applicable for both local and remote runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_settings = {\n",
    "    \"iteration_timeout_minutes\" : 10,\n",
    "    \"iterations\" : 2,\n",
    "    \"primary_metric\" : 'AUC_weighted',\n",
    "    \"preprocess\" : False,\n",
    "    \"verbosity\" : logging.INFO,\n",
    "    \"n_cross_validations\": 3\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remote Run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create or Attach a Remote Linux DSVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsvm_name = 'mydsvmc'\n",
    "\n",
    "try:\n",
    "    while ws.compute_targets[dsvm_name].provisioning_state == 'Creating':\n",
    "        time.sleep(1)\n",
    "        \n",
    "    dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
    "    print('Found existing DVSM.')\n",
    "except:\n",
    "    print('Creating a new DSVM.')\n",
    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
    "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
    "    dsvm_compute.wait_for_completion(show_output = True)\n",
    "    print(\"Waiting one minute for ssh to be accessible\")\n",
    "    time.sleep(60) # Wait for ssh to be accessible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.runconfig import RunConfiguration\n",
    "from azureml.core.conda_dependencies import CondaDependencies\n",
    "\n",
    "conda_run_config = RunConfiguration(framework=\"python\")\n",
    "\n",
    "conda_run_config.target = dsvm_compute\n",
    "\n",
    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
    "conda_run_config.environment.python.conda_dependencies = cd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pass Data with `Dataflow` Objects\n",
    "\n",
    "The `Dataflow` objects captured above can also be passed to the `submit` method for a remote run. AutoML will serialize the `Dataflow` object and send it to the remote compute target. The `Dataflow` will not be evaluated locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             path = project_folder,\n",
    "                             run_configuration=conda_run_config,\n",
    "                             X = X,\n",
    "                             y = y,\n",
    "                             **automl_settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run = experiment.submit(automl_config, show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Widget for Monitoring Runs\n",
    "\n",
    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
    "\n",
    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "RunDetails(remote_run).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retrieve All Child Runs\n",
    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(remote_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "    \n",
    "import pandas as pd\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = remote_run.get_output()\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model Based on Any Other Metric\n",
    "Show the run and the model that has the smallest `log_loss` value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_metric = \"log_loss\"\n",
    "best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a Specific Iteration\n",
    "Show the run and the model from the first iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 0\n",
    "best_run, fitted_model = remote_run.get_output(iteration = iteration)\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Best Fitted Model\n",
    "\n",
    "#### Load Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "digits = datasets.load_digits()\n",
    "X_test = digits.data[:10, :]\n",
    "y_test = digits.target[:10]\n",
    "images = digits.images[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing Our Best Fitted Model\n",
    "We will try to predict 2 digits and see how our model works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomly select digits and test\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import random\n",
    "import numpy as np\n",
    "\n",
    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
    "    print(index)\n",
    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
    "    label = y_test[index]\n",
    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
    "    fig = plt.figure(1, figsize=(3,3))\n",
    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
    "    ax1.set_title(title)\n",
    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appendix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Capture the `Dataflow` Objects for Later Use in AutoML\n",
    "\n",
    "`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object can be branched at any point for further usage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sklearn.digits.data + target\n",
    "digits_complete = dprep.auto_read_file('https://dprepdata.blob.core.windows.net/automl-notebook-data/digits-complete.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`digits_complete` (sourced from `sklearn.datasets.load_digits()`) is forked into `dflow_X` to capture all the feature columns and `dflow_y` to capture the label column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits_complete.to_pandas_dataframe().shape\n",
    "labels_column = 'Column64'\n",
    "dflow_X = digits_complete.drop_columns(columns = [labels_column])\n",
    "dflow_y = digits_complete.keep_columns(columns = [labels_column])"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "savitam"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/14.auto-ml-model-explanation.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/14.auto-ml-model-explanation.ipynb
@@ -0,0 +1,348 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 14: Explain classification model and visualize the explanation\n",
    "\n",
    "In this example we use the sklearn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) to showcase how you can use the AutoML Classifier for a simple classification problem.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you would see\n",
    "1. Creating an Experiment in an existing Workspace\n",
    "2. Instantiating AutoMLConfig\n",
    "3. Training the Model using local compute and explain the model\n",
    "4. Visualization model's feature importance in widget\n",
    "5. Explore best model's explanation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Experiment\n",
    "\n",
    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "import pandas as pd\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# choose a name for experiment\n",
    "experiment_name = 'automl-local-classification'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-local-classification-model-explanation'\n",
    "\n",
    "experiment=Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace Name'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Iris Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "y = iris.target\n",
    "X = iris.data\n",
    "\n",
    "features = iris.feature_names\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    test_size=0.1,\n",
    "                                                    random_state=100,\n",
    "                                                    stratify=y)\n",
    "\n",
    "X_train = pd.DataFrame(X_train, columns=features)\n",
    "X_test = pd.DataFrame(X_test, columns=features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate Auto ML Config\n",
    "\n",
    "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**task**|classification or regression|\n",
    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
    "|**max_time_sec**|Time limit in minutes for each iterations|\n",
    "|**iterations**|Number of iterations. In each iteration Auto ML trains the data with a specific pipeline|\n",
    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
    "|**X_valid**|(sparse) array-like, shape = [n_samples, n_features]|\n",
    "|**y_valid**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]|\n",
    "|**model_explainability**|Indicate to explain each trained pipeline or not |\n",
    "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             primary_metric = 'AUC_weighted',\n",
    "                             iteration_timeout_minutes = 200,\n",
    "                             iterations = 10,\n",
    "                             verbosity = logging.INFO,\n",
    "                             X = X_train, \n",
    "                             y = y_train,\n",
    "                             X_valid = X_test,\n",
    "                             y_valid = y_test,\n",
    "                             model_explainability=True,\n",
    "                             path=project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model\n",
    "\n",
    "You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
    "You will see the currently running iterations printing to the console."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = experiment.submit(automl_config, show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Widget for monitoring runs\n",
    "\n",
    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
    "\n",
    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "RunDetails(local_run).show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The *get_output* method on automl_classifier returns the best run and the fitted model for the last *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = local_run.get_output()\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best Model 's explanation\n",
    "\n",
    "Retrieve the explanation from the best_run. And explanation information includes:\n",
    "\n",
    "1.\tshap_values: The explanation information generated by shap lib\n",
    "2.\texpected_values: The expected value of the model applied to set of X_train data.\n",
    "3.\toverall_summary: The model level feature importance values sorted in descending order\n",
    "4.\toverall_imp: The feature names sorted in the same order as in overall_summary\n",
    "5.\tper_class_summary: The class level feature importance values sorted in descending order. Only available for the classification case\n",
    "6.\tper_class_imp: The feature names sorted in the same order as in per_class_summary. Only available for the classification case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.train.automl.automlexplainer import retrieve_model_explanation\n",
    "\n",
    "shap_values, expected_values, overall_summary, overall_imp, per_class_summary, per_class_imp = \\\n",
    "    retrieve_model_explanation(best_run)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(overall_summary)\n",
    "print(overall_imp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(per_class_summary)\n",
    "print(per_class_imp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Beside retrieve the existed model explanation information, explain the model with different train/test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.train.automl.automlexplainer import explain_model\n",
    "\n",
    "shap_values, expected_values, overall_summary, overall_imp, per_class_summary, per_class_imp = \\\n",
    "    explain_model(fitted_model, X_train, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(overall_summary)\n",
    "print(overall_imp)"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "xif"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/17.auto-ml-classification_with_tensorflow.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/17.auto-ml-classification_with_tensorflow.ipynb
@@ -0,0 +1,390 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 17: Classification with Local Compute with Tesnorflow DNNClassifier and LinearClassifier using whitelist models feature.\n",
    "\n",
    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "This notebooks shows how can automl can be trained on a a selected list of models,see the readme.md for the models.\n",
    "This trains the model exclusively on tensorflow based models.\n",
    "\n",
    "In this notebook you will learn how to:\n",
    "1. Create an `Experiment` in an existing `Workspace`.\n",
    "2. Configure AutoML using `AutoMLConfig`.\n",
    "3. Train the model on a whilelisted models using local compute. \n",
    "4. Explore the results.\n",
    "5. Test the best fitted model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an Experiment\n",
    "\n",
    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# Choose a name for the experiment and specify the project folder.\n",
    "experiment_name = 'automl-local-classification'\n",
    "project_folder = './sample_projects/automl-local-classification'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace Name'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Training Data\n",
    "\n",
    "This uses scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "digits = datasets.load_digits()\n",
    "\n",
    "# Exclude the first 100 rows from training so that they can be used for test.\n",
    "X_train = digits.data[100:,:]\n",
    "y_train = digits.target[100:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure AutoML\n",
    "\n",
    "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**task**|classification or regression|\n",
    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
    "|**n_cross_validations**|Number of cross validation splits.|\n",
    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             primary_metric = 'AUC_weighted',\n",
    "                             iteration_timeout_minutes = 60,\n",
    "                             iterations = 10,\n",
    "                             n_cross_validations = 3,\n",
    "                             verbosity = logging.INFO,\n",
    "                             X = X_train, \n",
    "                             y = y_train,\n",
    "                             enable_tf=True,\n",
    "                             whitelist_models=[\"TensorFlowLinearClassifier\", \"TensorFlowDNN\"],\n",
    "                             path = project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Models\n",
    "\n",
    "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
    "In this example, we specify `show_output = True` to print currently running iterations to the console."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = experiment.submit(automl_config, show_output = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Widget for Monitoring Runs\n",
    "\n",
    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
    "\n",
    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "RunDetails(local_run).show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(local_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = local_run.get_output()\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model Based on Any Other Metric\n",
    "Show the run and the model that has the smallest `log_loss` value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_metric = \"log_loss\"\n",
    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a Specific Iteration\n",
    "Show the run and the model from the third iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 3\n",
    "third_run, third_model = local_run.get_output(iteration = iteration)\n",
    "print(third_run)\n",
    "print(third_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Best Fitted Model\n",
    "\n",
    "#### Load Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_test = digits.data[:10, :]\n",
    "y_test = digits.target[:10]\n",
    "images = digits.images[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing Our Best Fitted Model\n",
    "We will try to predict 2 digits and see how our model works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Randomly select digits and test.\n",
    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
    "    print(index)\n",
    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
    "    label = y_test[index]\n",
    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
    "    fig = plt.figure(1, figsize = (3,3))\n",
    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
    "    ax1.set_title(title)\n",
    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "savitam"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
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 }
--- a/how-to-use-azureml/automated-machine-learning/18a.auto-ml-forecasting.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/18a.auto-ml-forecasting.ipynb
@@ -0,0 +1,397 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 18: Energy Demand Forecasting\n",
    "\n",
    "In this example, we show how AutoML can be used for energy demand forecasting.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you would see\n",
    "1. Creating an Experiment in an existing Workspace\n",
    "2. Instantiating AutoMLConfig with new task type \"forecasting\" for timeseries data training, and other timeseries related settings: for this dataset we use the basic one: \"time_column_name\" \n",
    "3. Training the Model using local compute\n",
    "4. Exploring the results\n",
    "5. Testing the fitted model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Experiment\n",
    "\n",
    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.core\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import logging\n",
    "import warnings\n",
    "warnings.showwarning = lambda *args, **kwargs: None\n",
    "\n",
    "\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# choose a name for the run history container in the workspace\n",
    "experiment_name = 'automl-energydemandforecasting'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-local-energydemandforecasting'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Run History Name'] = experiment_name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Data\n",
    "Read energy demanding data from file, and preview data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"nyc_energy.csv\", parse_dates=['timeStamp'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data to train and test\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = data[data['timeStamp'] < '2017-02-01']\n",
    "test = data[data['timeStamp'] >= '2017-02-01']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare the test data, we will feed X_test to the fitted model and get prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = test.pop('demand').values\n",
    "X_test = test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the train data to train and valid\n",
    "\n",
    "Use one month's data as valid data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train[train['timeStamp'] < '2017-01-01']\n",
    "X_valid = train[train['timeStamp'] >= '2017-01-01']\n",
    "y_train = X_train.pop('demand').values\n",
    "y_valid = X_valid.pop('demand').values\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_valid.shape)\n",
    "print(y_valid.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate Auto ML Config\n",
    "\n",
    "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**task**|forecasting|\n",
    "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
    "|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
    "|**X_valid**|Data used to evaluate a model in a iteration. (sparse) array-like, shape = [n_samples, n_features]|\n",
    "|**y_valid**|Data used to evaluate a model in a iteration. (sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
    "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_column_name = 'timeStamp'\n",
    "automl_settings = {\n",
    "    \"time_column_name\": time_column_name,\n",
    "}\n",
    "\n",
    "\n",
    "automl_config = AutoMLConfig(task = 'forecasting',\n",
    "                             debug_log = 'automl_nyc_energy_errors.log',\n",
    "                             primary_metric='normalized_root_mean_squared_error',\n",
    "                             iterations = 10,\n",
    "                             iteration_timeout_minutes = 5,\n",
    "                             X = X_train,\n",
    "                             y = y_train,\n",
    "                             X_valid = X_valid,\n",
    "                             y_valid = y_valid,\n",
    "                             path=project_folder,\n",
    "                             verbosity = logging.INFO,\n",
    "                            **automl_settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model\n",
    "\n",
    "You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
    "You will see the currently running iterations printing to the console."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = experiment.submit(automl_config, show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "Below we select the best pipeline from our iterations. The get_output method on automl_classifier returns the best run and the fitted model for the last fit invocation. There are overloads on get_output that allow you to retrieve the best run and fitted model for any logged metric or a particular iteration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = local_run.get_output()\n",
    "fitted_model.steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Best Fitted Model\n",
    "\n",
    "Predict on training and test set, and calculate residual values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = fitted_model.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define a Check Data Function\n",
    "\n",
    "Remove the nan values from y_test to avoid error when calculate metrics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _check_calc_input(y_true, y_pred, rm_na=True):\n",
    "    \"\"\"\n",
    "    Check that 'y_true' and 'y_pred' are non-empty and\n",
    "    have equal length.\n",
    "\n",
    "    :param y_true: Vector of actual values\n",
    "    :type y_true: array-like\n",
    "\n",
    "    :param y_pred: Vector of predicted values\n",
    "    :type y_pred: array-like\n",
    "\n",
    "    :param rm_na:\n",
    "        If rm_na=True, remove entries where y_true=NA and y_pred=NA.\n",
    "    :type rm_na: boolean\n",
    "\n",
    "    :return:\n",
    "        Tuple (y_true, y_pred). if rm_na=True,\n",
    "        the returned vectors may differ from their input values.\n",
    "    :rtype: Tuple with 2 entries\n",
    "    \"\"\"\n",
    "    if len(y_true) != len(y_pred):\n",
    "        raise ValueError(\n",
    "            'the true values and prediction values do not have equal length.')\n",
    "    elif len(y_true) == 0:\n",
    "        raise ValueError(\n",
    "            'y_true and y_pred are empty.')\n",
    "    # if there is any non-numeric element in the y_true or y_pred,\n",
    "    # the ValueError exception will be thrown.\n",
    "    y_true = np.array(y_true).astype(float)\n",
    "    y_pred = np.array(y_pred).astype(float)\n",
    "    if rm_na:\n",
    "        # remove entries both in y_true and y_pred where at least\n",
    "        # one element in y_true or y_pred is missing\n",
    "        y_true_rm_na = y_true[~(np.isnan(y_true) | np.isnan(y_pred))]\n",
    "        y_pred_rm_na = y_pred[~(np.isnan(y_true) | np.isnan(y_pred))]\n",
    "        return (y_true_rm_na, y_pred_rm_na)\n",
    "    else:\n",
    "        return y_true, y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use the Check Data Function to remove the nan values from y_test to avoid error when calculate metrics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test,y_pred =  _check_calc_input(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate metrics for the prediction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
    "print('R2 score: %.2f' % r2_score(y_test, y_pred))\n",
    "\n",
    "\n",
    "\n",
    "# Plot outputs\n",
    "%matplotlib notebook\n",
    "test_pred = plt.scatter(y_test, y_pred, color='b')\n",
    "test_test = plt.scatter(y_test, y_test, color='g')\n",
    "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "xiaga"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/18b.auto-ml-forecasting.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/18b.auto-ml-forecasting.ipynb
@@ -0,0 +1,390 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 18B: Orange Juice Sales Forecasting\n",
    "\n",
    "In this example, we use AutoML to find and tune a time-series forecasting model.\n",
    "\n",
    "Make sure you have executed the [configuration notebook](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook, you will:\n",
    "1. Create an Experiment in an existing Workspace\n",
    "2. Instantiate an AutoMLConfig \n",
    "3. Find and train a forecasting model using local compute\n",
    "4. Evaluate the performance of the model\n",
    "\n",
    "## Sample Data\n",
    "The examples in the follow code samples use the [University of Chicago's Dominick's Finer Foods dataset](https://research.chicagobooth.edu/kilts/marketing-databases/dominicks) to forecast orange juice sales. Dominick's was a grocery chain in the Chicago metropolitan area."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Experiment\n",
    "\n",
    "As part of the setup you have already created a <b>Workspace</b>. To run AutoML, you also need to create an <b>Experiment</b>. An Experiment is a named object in a Workspace which represents a predictive task, the output of which is a trained model and a set of evaluation metrics for the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.core\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import logging\n",
    "\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# choose a name for the run history container in the workspace\n",
    "experiment_name = 'automl-ojsalesforecasting'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-local-ojsalesforecasting'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Run History Name'] = experiment_name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Data\n",
    "You are now ready to load the historical orange juice sales data. We will load the CSV file into a plain pandas DataFrame; the time column in the CSV is called _WeekStarting_, so it will be specially parsed into the datetime type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_column_name = 'WeekStarting'\n",
    "data = pd.read_csv(\"dominicks_OJ.csv\", parse_dates=[time_column_name])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each row in the DataFrame holds a quantity of weekly sales for an OJ brand at a single store. The data also includes the sales price, a flag indicating if the OJ brand was advertised in the store that week, and some customer demographic information based on the store location. For historical reasons, the data also include the logarithm of the sales quantity. The Dominick's grocery data is commonly used to illustrate econometric modeling techniques where logarithms of quantities are generally preferred.    \n",
    "\n",
    "The task is now to build a time-series model for the _Quantity_ column. It is important to note that this dataset is comprised of many individual time-series - one for each unique combination of _Store_ and _Brand_. To distinguish the individual time-series, we thus define the **grain** - the columns whose values determine the boundaries between time-series: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grain_column_names = ['Store', 'Brand']\n",
    "nseries = data.groupby(grain_column_names).ngroups\n",
    "print('Data contains {0} individual time-series.'.format(nseries))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Splitting\n",
    "For the purposes of demonstration and later forecast evaluation, we now split the data into a training and a testing set. The test set will contain the final 20 weeks of observed sales for each time-series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ntest_periods = 20\n",
    "\n",
    "def split_last_n_by_grain(df, n):\n",
    "    \"\"\"\n",
    "    Group df by grain and split on last n rows for each group\n",
    "    \"\"\"\n",
    "    df_grouped = (df.sort_values(time_column_name) # Sort by ascending time\n",
    "                  .groupby(grain_column_names, group_keys=False))\n",
    "    df_head = df_grouped.apply(lambda dfg: dfg.iloc[:-n])\n",
    "    df_tail = df_grouped.apply(lambda dfg: dfg.iloc[-n:])\n",
    "    return df_head, df_tail\n",
    "\n",
    "X_train, X_test = split_last_n_by_grain(data, ntest_periods)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "\n",
    "For forecasting tasks, AutoML uses pre-processing and estimation steps that are specific to time-series. AutoML will undertake the following pre-processing steps:\n",
    "* Detect time-series sample frequency (e.g. hourly, daily, weekly) and create new records for absent time points to make the series regular. A regular time series has a well-defined frequency and has a value at every sample point in a contiguous time span \n",
    "* Impute missing values in the target (via forward-fill) and feature columns (using median column values) \n",
    "* Create grain-based features to enable fixed effects across different series\n",
    "* Create time-based features to assist in learning seasonal patterns\n",
    "* Encode categorical variables to numeric quantities\n",
    "\n",
    "AutoML will currently train a single, regression-type model across **all** time-series in a given training set. This allows the model to generalize across related series.\n",
    "\n",
    "You are almost ready to start an AutoML training job. We will first need to create a validation set from the existing training set (i.e. for hyper-parameter tuning): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nvalidation_periods = 20\n",
    "X_train, X_validate = split_last_n_by_grain(X_train, nvalidation_periods)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to separate the target column from the rest of the DataFrame: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_column_name = 'Quantity'\n",
    "y_train = X_train.pop(target_column_name).values\n",
    "y_validate = X_validate.pop(target_column_name).values "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an AutoMLConfig\n",
    "\n",
    "The AutoMLConfig object defines the settings and data for an AutoML training job. Here, we set necessary inputs like the task type, the number of AutoML iterations to try, and the training and validation data. \n",
    "\n",
    "For forecasting tasks, there are some additional parameters that can be set: the name of the input data column, holding the date/time and the grain column names. A time column is required for forecasting, while the grain is optional. If a grain is not given, the forecaster assumes that the whole dataset is a single time-series. We also pass a list of columns to drop prior to modeling. The _logQuantity_ column is completely correlated with the target quantity, so it must be removed to prevent a target leak. \n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**task**|forecasting|\n",
    "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
    "|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
    "|**X**|Training matrix of features, shape = [n_training_samples, n_features]|\n",
    "|**y**|Target values, shape = [n_training_samples, ]|\n",
    "|**X_valid**|Validation matrix of features, shape = [n_validation_samples, n_features]|\n",
    "|**y_valid**|Target values for validation, shape = [n_validation_samples, ]\n",
    "|**enable_ensembling**|Allow AutoML to create ensembles of the best performing models\n",
    "|**debug_log**|Log file path for writing debugging information\n",
    "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_settings = {\n",
    "    'time_column_name': time_column_name,\n",
    "    'grain_column_names': grain_column_names,\n",
    "    'drop_column_names': ['logQuantity']\n",
    "}\n",
    "\n",
    "automl_config = AutoMLConfig(task='forecasting',\n",
    "                             debug_log='automl_oj_sales_errors.log',\n",
    "                             primary_metric='normalized_root_mean_squared_error',\n",
    "                             iterations=10,\n",
    "                             X=X_train,\n",
    "                             y=y_train,\n",
    "                             X_valid=X_validate,\n",
    "                             y_valid=y_validate,\n",
    "                             enable_ensembling=False,\n",
    "                             path=project_folder,\n",
    "                             verbosity=logging.INFO,\n",
    "                            **automl_settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model\n",
    "\n",
    "You can now submit a new training run. For local runs, the execution is synchronous. Depending on the data and number of iterations this operation may take several minutes.\n",
    "Information from each iteration will be printed to the console."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = experiment.submit(automl_config, show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "Each run within an Experiment stores serialized (i.e. pickled) pipelines from the AutoML iterations. We can now retrieve the pipeline with the best performance on the validation dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_pipeline = local_run.get_output()\n",
    "fitted_pipeline.steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make Predictions from the Best Fitted Model\n",
    "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. First, we remove the target values from the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = X_test.pop(target_column_name).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To produce predictions on the test set, we need to know the feature values at all dates in the test set. This requirement is somewhat reasonable for the OJ sales data since the features mainly consist of price, which is usually set in advance, and customer demographics which are approximately constant for each store over the 20 week forecast horizon in the testing data. \n",
    "\n",
    "The target predictions can be retrieved by calling the `predict` method on the best model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = fitted_pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate evaluation metrics for the prediction\n",
    "To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MAPE(actual, pred):\n",
    "    \"\"\"\n",
    "    Calculate mean absolute percentage error.\n",
    "    Remove NA and values where actual is close to zero\n",
    "    \"\"\"\n",
    "    not_na = ~(np.isnan(actual) | np.isnan(pred))\n",
    "    not_zero = ~np.isclose(actual, 0.0)\n",
    "    actual_safe = actual[not_na & not_zero]\n",
    "    pred_safe = pred[not_na & not_zero]\n",
    "    APE = 100*np.abs((actual_safe - pred_safe)/actual_safe)\n",
    "    return np.mean(APE)\n",
    "\n",
    "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
    "print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
    "print('MAPE: %.2f' % MAPE(y_test, y_pred))"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "erwright"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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--- a/how-to-use-azureml/automated-machine-learning/README.md
+++ b/how-to-use-azureml/automated-machine-learning/README.md
@@ -68,21 +68,21 @@ There's no need to install mini-conda specifically.
 The **automl/automl_setup** script creates a new conda environment, installs the necessary packages, configures the widget and starts a jupyter notebook.
 It takes the conda environment name as an optional parameter.  The default conda environment name is azure_automl.  The exact command depends on the operating system.  See the specific sections below for Windows, Mac and Linux.  It can take about 10 minutes to execute.
 ## Windows
-Start an **Anaconda Prompt** window, cd to the **how-to-use-azureml/automated-machine-learning** folder where the sample notebooks were extracted and then run:
+Start an **Anaconda Prompt** window, 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 **how-to-use-azureml/automated-machine-learning** folder where the sample notebooks were extracted and then run:
+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 **how-to-use-azureml/automated-machine-learning** folder where the sample notebooks were extracted and then run:
+cd to the **automl** folder where the sample notebooks were extracted and then run:
 ```
 bash automl_setup_linux.sh
@@ -131,7 +131,7 @@ bash automl_setup_linux.sh
    - Specify automl settings as kwargs
 - [auto-ml-remote-attach.ipynb](remote-attach/auto-ml-remote-attach.ipynb)
-    - Dataset: Scikit learn's [20newsgroup](http://scikit-learn.org/stable/datasets/twenty_newsgroups.html)
+    - 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
@@ -154,7 +154,7 @@ bash automl_setup_linux.sh
    - Download fitted pipeline for any iteration
 - [auto-ml-remote-execution-with-datastore.ipynb](remote-execution-with-datastore/auto-ml-remote-execution-with-datastore.ipynb)
-    - Dataset: Scikit learn's [20newsgroup](http://scikit-learn.org/stable/datasets/twenty_newsgroups.html)
+    - Dataset: scikit learn's [digit dataset](https://innovate.burningman.org/datasets-page/)
    - Download the data and store it in DataStore.
 - [auto-ml-classification-with-deployment.ipynb](classification-with-deployment/auto-ml-classification-with-deployment.ipynb)
@@ -183,13 +183,9 @@ bash automl_setup_linux.sh
    - Simple example of using Auto ML for classification with whitelisting tensorflow models.checkout
    - Uses local compute for training
- [auto-ml-forecasting-a.ipynb](forecasting-a/auto-ml-forecasting-a.ipynb)
+- [auto-ml-timeseries.ipynb](timeseries/auto-ml-timeseries.ipynb)
-    - Dataset: [NYC energy demand data](forecasting-a/nyc_energy.csv)
+    - Dataset: NYC energy demanding data
-    - Example of using AutoML for training a forecasting model
+    - Example of using AutoML for timeseries data training
 - [auto-ml-forecasting-b.ipynb](forecasting-b/auto-ml-forecasting-b.ipynb)
    - Dataset: [Dominick's grocery sales of orange juice](forecasting-b/dominicks_OJ.csv)
    - Example of training an AutoML forecasting model on multiple time-series
 <a name="documentation"></a>
 # Documentation
@@ -300,57 +296,11 @@ The main code of the file must be indented so that it is under this condition.
 <a name="troubleshooting"></a>
 # Troubleshooting
-## automl_setup fails
+## Iterations fail and the log contains "MemoryError"
 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 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`.
 3. Pass a new name as the first parameter to automl_setup so that it creates a new conda environment. You can view existing conda environments using `conda env list` and remove them with `conda env remove -n <environmentname>`. 
 ## configuration.ipynb fails
 1) For local conda, make sure that you have susccessfully run automl_setup first.
 2) Check that the subscription_id is correct.  You can find the subscription_id in the Azure Portal by selecting All Service and then Subscriptions. The characters "<" and ">" should not be included in the subscription_id value.  For example, `subscription_id = "12345678-90ab-1234-5678-1234567890abcd"` has the valid format.
 3) Check that you have Contributor or Owner access to the Subscription.
 4) Check that the region is one of the supported regions: `eastus2`, `eastus`, `westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`
 5) Check that you have access to the region using the Azure Portal.
 ## workspace.from_config fails
 If the call `ws = Workspace.from_config()` fails:
 1) Make sure that you have run the `configuration.ipynb` notebook successfully.
 2) If you are running a notebook from a folder that is not under the folder where you ran `configuration.ipynb`, copy the folder aml_config and the file config.json that it contains to the new folder.  Workspace.from_config reads the config.json for the notebook folder or it parent folder.
 3) If you are switching to a new subscription, resource group, workspace or region, make sure that you run the `configuration.ipynb` notebook again.  Changing config.json directly will only work if the workspace already exists in the specified resource group under the specified subscription.
 4) If you want to change the region, please change the workspace, resource group or subscription.  `Workspace.create` will not create or update a workspace if it already exists, even if the region specified is different.
 ## Sample notebook fails
 If a sample notebook fails with an error that property, method or library does not exist:
 1) Check that you have selected correct kernel in jupyter notebook.  The kernel is displayed in the top right of the notebook page.  It can be changed using the `Kernel | Change Kernel` menu option.  For Azure Notebooks, it should be `Python 3.6`.  For local conda environments, it should be the conda envioronment name that you specified in automl_setup.  The default is azure_automl.  Note that the kernel is saved as part of the notebook.  So, if you switch to a new conda environment, you will have to select the new kernel in the notebook.
 2) Check that the notebook is for the SDK version that you are using.  You can check the SDK version by executing `azureml.core.VERSION` in a jupyter notebook cell.  You can download previous version of the sample notebooks from GitHub by clicking the `Branch` button, selecting the `Tags` tab and then selecting the version.
 ## Remote run: DsvmCompute.create fails 
 There are several reasons why the DsvmCompute.create can fail.  The reason is usually in the error message but you have to look at the end of the error message for the detailed reason.  Some common reasons are:
 1) `Compute name is invalid, it should start with a letter, be between 2 and 16 character, and only include letters (a-zA-Z), numbers (0-9) and \'-\'.`  Note that underscore is not allowed in the name.
 2) `The requested VM size xxxxx is not available in the current region.`  You can select a different region or vm_size. 
 ## Remote run: Unable to establish SSH connection
 AutoML uses the SSH protocol to communicate with remote DSVMs.  This defaults to port 22.  Possible causes for this error are:
 1) The DSVM is not ready for SSH connections.  When DSVM creation completes, the DSVM might still not be ready to acceept SSH connections.  The sample notebooks have a one minute delay to allow for this.
 2) Your Azure Subscription may restrict the IP address ranges that can access the DSVM on port 22.  You can check this in the Azure Portal by selecting the Virtual Machine and then clicking Networking.  The Virtual Machine name is the name that you provided in the notebook plus 10 alpha numeric characters to make the name unique.  The Inbound Port Rules define what can access the VM on specific ports.  Note that there is a priority priority order.  So, a Deny entry with a low priority number will override a Allow entry with a higher priority number.
 ## Remote run: setup iteration fails
 This is often an issue with the `get_data` method.
 1) Check that the `get_data` method is valid by running it locally.
 2) Make sure that `get_data` isn't referring to any local files.  `get_data` is executed on the remote DSVM.  So, it doesn't have direct access to local data files.  Instead you can store the data files with DataStore.  See [auto-ml-remote-execution-with-datastore.ipynb](remote-execution-with-datastore/auto-ml-remote-execution-with-datastore.ipynb)
 3) You can get to the error log for the setup iteration by clicking the `Click here to see the run in Azure portal` link, click `Back to Experiment`, click on the highest run number and then click on Logs.
 ## Remote run: disk full
 AutoML creates files under /tmp/azureml_runs for each iteration that it runs.  It creates a folder with the iteration id.  For example: AutoML_9a038a18-77cc-48f1-80fb-65abdbc33abe_93.  Under this, there is a azureml-logs folder, which contains logs.  If you run too many iterations on the same DSVM, these files can fill the disk.
 You can delete the files under /tmp/azureml_runs or just delete the VM and create a new one.
 If your get_data downloads files, make sure the delete them or they can use disk space as well.
 When using DataStore, it is good to specify an absolute path for the files so that they are downloaded just once.  If you specify a relative path, it will download a file for each iteration.
 ## Remote run: Iterations fail and the log contains "MemoryError"
 This can be caused by insufficient memory on the DSVM.  AutoML loads all training data into memory.  So, the available memory should be more than the training data size.
 If you are using a remote DSVM, memory is needed for each concurrent iteration.  The max_concurrent_iterations setting specifies the maximum concurrent iterations.  For example, if the training data size is 8Gb and max_concurrent_iterations is set to 10, the minimum memory required is at least 80Gb.
 To resolve this issue, allocate a DSVM with more memory or reduce the value specified for max_concurrent_iterations.
-## Remote run: Iterations show as "Not Responding" in the RunDetails widget.
+## Iterations show as "Not Responding" in the RunDetails widget.
 This can be caused by too many concurrent iterations for a remote DSVM.  Each concurrent iteration usually takes 100% of a core when it is running.  Some iterations can use multiple cores.  So, the max_concurrent_iterations setting should always be less than the number of cores of the DSVM.
 To resolve this issue, try reducing the value specified for the max_concurrent_iterations setting.
--- a/how-to-use-azureml/automated-machine-learning/automl_setup.cmd
+++ b/how-to-use-azureml/automated-machine-learning/automl_setup.cmd
@@ -11,8 +11,8 @@ IF NOT EXIST %automl_env_file% GOTO YmlMissing
 call conda activate %conda_env_name% 2>nul:
 if not errorlevel 1 (
-  echo Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment %conda_env_name%
+  echo Upgrading azureml-sdk[automl] in existing conda environment %conda_env_name%
-  call pip install --upgrade azureml-sdk[automl,notebooks,explain]
+  call pip install --upgrade azureml-sdk[automl,notebooks]
  if errorlevel 1 goto ErrorExit
 ) else (
  call conda env create -f %automl_env_file% -n %conda_env_name%
@@ -37,7 +37,7 @@ echo ***************************************
 echo * AutoML setup completed successfully *
 echo ***************************************
 echo.
-echo Starting jupyter notebook - please run the configuration notebook 
+echo Starting jupyter notebook - please run notebook 00.configuration
 echo.
 jupyter notebook --log-level=50
--- a/how-to-use-azureml/automated-machine-learning/automl_setup_linux.sh
+++ b/how-to-use-azureml/automated-machine-learning/automl_setup_linux.sh
@@ -21,8 +21,8 @@ fi
 if source activate $CONDA_ENV_NAME 2> /dev/null
 then
-   echo "Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment" $CONDA_ENV_NAME
+   echo "Upgrading azureml-sdk[automl] in existing conda environment" $CONDA_ENV_NAME
-   pip install --upgrade azureml-sdk[automl,notebooks,explain]
+   pip install --upgrade azureml-sdk[automl,notebooks]
 else
   conda env create -f $AUTOML_ENV_FILE -n $CONDA_ENV_NAME &&
   source activate $CONDA_ENV_NAME &&
@@ -35,7 +35,7 @@ else
   echo "* AutoML setup completed successfully *" &&
   echo "***************************************" &&
   echo "" &&
-   echo "Starting jupyter notebook - please run the configuration notebook" &&
+   echo "Starting jupyter notebook - please run notebook 00.configuration" &&
   echo "" &&
   jupyter notebook --log-level=50
 fi
--- a/how-to-use-azureml/automated-machine-learning/automl_setup_mac.sh
+++ b/how-to-use-azureml/automated-machine-learning/automl_setup_mac.sh
@@ -21,8 +21,8 @@ fi
 if source activate $CONDA_ENV_NAME 2> /dev/null
 then
-   echo "Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment" $CONDA_ENV_NAME
+   echo "Upgrading azureml-sdk[automl] in existing conda environment" $CONDA_ENV_NAME
-   pip install --upgrade azureml-sdk[automl,notebooks,explain]
+   pip install --upgrade azureml-sdk[automl,notebooks]
 else
   conda env create -f $AUTOML_ENV_FILE -n $CONDA_ENV_NAME &&
   source activate $CONDA_ENV_NAME &&
@@ -37,7 +37,7 @@ else
   echo "* AutoML setup completed successfully *" &&
   echo "***************************************" &&
   echo "" &&
-   echo "Starting jupyter notebook - please run the configuration notebook" &&
+   echo "Starting jupyter notebook - please run notebook 00.configuration" &&
   echo "" &&
   jupyter notebook --log-level=50
 fi
--- a/how-to-use-azureml/automated-machine-learning/classification_with_tensorflow/auto-ml-classification_with_tensorflow.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification_with_tensorflow/auto-ml-classification_with_tensorflow.ipynb
@@ -13,7 +13,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# Automated Machine Learning: Classification with Local Compute with Tensorflow DNNClassifier and LinearClassifier using whitelist models\n",
+    "# Automated Machine Learning: Classification with Local Compute with Tesnorflow DNNClassifier and LinearClassifier using whitelist models\n",
    "\n",
    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
    "\n",
--- a/how-to-use-azureml/automated-machine-learning/configuration.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/configuration.ipynb
@@ -1,154 +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": [
    "# Automated Machine Learning 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"
   ]
  },
  {
   "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()"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "savitam"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb
@@ -24,15 +24,6 @@
    "3. Pass the `Dataflow` to AutoML for a remote run."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compatibility\n",
    "\n",
    "Currently, Data Prep only supports __Ubuntu 16__ and __Red Hat Enterprise Linux 7__. We are working on supporting more linux distros."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
--- a/how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.ipynb
@@ -24,15 +24,6 @@
    "3. Pass the `Dataflow` to AutoML for a remote run."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compatibility\n",
    "\n",
    "Currently, Data Prep only supports __Ubuntu 16__ and __Red Hat Enterprise Linux 7__. We are working on supporting more linux distros."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
--- a/how-to-use-azureml/automated-machine-learning/dominicks_OJ.csv
+++ b/how-to-use-azureml/automated-machine-learning/dominicks_OJ.csv
--- a/how-to-use-azureml/automated-machine-learning/forecasting-b/auto-ml-forecasting-b.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-b/auto-ml-forecasting-b.ipynb
@@ -210,7 +210,7 @@
    "\n",
    "The AutoMLConfig object defines the settings and data for an AutoML training job. Here, we set necessary inputs like the task type, the number of AutoML iterations to try, and the training and validation data. \n",
    "\n",
-    "For forecasting tasks, there are some additional parameters that can be set: the name of the column holding the date/time and the grain column names. A time column is required for forecasting, while the grain is optional. If a grain is not given, the forecaster assumes that the whole dataset is a single time-series. We also pass a list of columns to drop prior to modeling. The _logQuantity_ column is completely correlated with the target quantity, so it must be removed to prevent a target leak. \n",
+    "For forecasting tasks, there are some additional parameters that can be set: the name of the input data column, holding the date/time and the grain column names. A time column is required for forecasting, while the grain is optional. If a grain is not given, the forecaster assumes that the whole dataset is a single time-series. We also pass a list of columns to drop prior to modeling. The _logQuantity_ column is completely correlated with the target quantity, so it must be removed to prevent a target leak. \n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
--- a/how-to-use-azureml/automated-machine-learning/nyc_energy.csv
+++ b/how-to-use-azureml/automated-machine-learning/nyc_energy.csv
--- a/how-to-use-azureml/azure-databricks/01.Installation_and_Configuration.ipynb
+++ b/how-to-use-azureml/azure-databricks/01.Installation_and_Configuration.ipynb
@@ -1,179 +1,264 @@
 {
-  "cells": [
+ "cells": [
-    {
+  {
-      "cell_type": "markdown",
+   "cell_type": "markdown",
-      "metadata": {},
+   "metadata": {},
-      "source": [
+   "source": [
-        "Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
+    "Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
-        "\n",
+    "\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
+    "\n",
-        "Licensed under the MIT License."
+    "Licensed under the MIT License."
-      ]
+   ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package. You can select the option to attach the library to all clusters or just one cluster.\n",
        "\n",
        "**azureml-sdk**\n",
        "* Source: Upload Python Egg or PyPi\n",
        "* PyPi Name: `azureml-sdk[databricks]`\n",
        "* Select Install Library"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import azureml.core\n",
        "\n",
        "# Check core SDK version number - based on build number of preview/master.\n",
        "print(\"SDK version:\", azureml.core.VERSION)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![04ACI](files/tables/image2b.JPG)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Please specify the Azure subscription Id, resource group name, workspace name, and the region in which you want to create the Azure Machine Learning Workspace.\n",
        "\n",
        "You can get the value of your Azure subscription ID from the Azure Portal, and then selecting Subscriptions from the menu on the left.\n",
        "\n",
        "For the resource_group, use the name of the resource group that contains your Azure Databricks Workspace.\n",
        "\n",
        "NOTE: If you provide a resource group name that does not exist, the resource group will be automatically created. This may or may not succeed in your environment, depending on the permissions you have on your Azure Subscription."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# subscription_id = \"<your-subscription-id>\"\n",
        "# resource_group = \"<your-existing-resource-group>\"\n",
        "# workspace_name = \"<a-new-or-existing-workspace; it is unrelated to Databricks workspace>\"\n",
        "# workspace_region = \"<your-resource group-region>\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# import the Workspace class and check the azureml SDK version\n",
        "# exist_ok checks if workspace exists or not.\n",
        "\n",
        "from azureml.core import Workspace\n",
        "\n",
        "ws = Workspace.create(name = workspace_name,\n",
        "                      subscription_id = subscription_id,\n",
        "                      resource_group = resource_group, \n",
        "                      location = workspace_region,\n",
        "                      exist_ok=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#get workspace details\n",
        "ws.get_details()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace(workspace_name = workspace_name,\n",
        "               subscription_id = subscription_id,\n",
        "               resource_group = resource_group)\n",
        "\n",
        "# persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
        "ws.write_config()\n",
        "##if you need to give a different path/filename please use this\n",
        "##write_config(path=\"/databricks/driver/aml_config/\",file_name=<alias_conf.cfg>)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "help(Workspace)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# import the Workspace class and check the azureml SDK version\n",
        "from azureml.core import Workspace\n",
        "\n",
        "ws = Workspace.from_config()\n",
        "#ws = Workspace.from_config(<full path>)\n",
        "print('Workspace name: ' + ws.name, \n",
        "      'Azure region: ' + ws.location, \n",
        "      'Subscription id: ' + ws.subscription_id, \n",
        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "pasha"
      },
      {
        "name": "wamartin"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.0"
    },
    "name": "01.Installation_and_Configuration",
    "notebookId": 3836944406456490
  },
-  "nbformat": 4,
+  {
-  "nbformat_minor": 1
+   "cell_type": "markdown",
-}
+   "metadata": {},
   "source": [
    "We support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package. You can select the option to attach the library to all clusters or just one cluster.\n",
    "\n",
    "**azureml-sdk**\n",
    "* Source: Upload Python Egg or PyPi\n",
    "* PyPi Name: `azureml-sdk[databricks]`\n",
    "* Select Install Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.core\n",
    "\n",
    "# Check core SDK version number - based on build number of preview/master.\n",
    "print(\"SDK version:\", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![04ACI](files/tables/image2b.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please specify the Azure subscription Id, resource group name, workspace name, and the region in which you want to create the Azure Machine Learning Workspace.\n",
    "\n",
    "You can get the value of your Azure subscription ID from the Azure Portal, and then selecting Subscriptions from the menu on the left.\n",
    "\n",
    "For the resource_group, use the name of the resource group that contains your Azure Databricks Workspace.\n",
    "\n",
    "NOTE: If you provide a resource group name that does not exist, the resource group will be automatically created. This may or may not succeed in your environment, depending on the permissions you have on your Azure Subscription."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subscription_id = \"<your-subscription-id>\"\n",
    "# resource_group = \"<your-existing-resource-group>\"\n",
    "# workspace_name = \"<a-new-or-existing-workspace; it is unrelated to Databricks workspace>\"\n",
    "# workspace_region = \"<your-resource group-region>\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##TESTONLY\n",
    "# import auth creds from notebook parameters\n",
    "tenant = dbutils.widgets.get('tenant_id')\n",
    "username = dbutils.widgets.get('service_principal_id')\n",
    "password = dbutils.widgets.get('service_principal_password')\n",
    "\n",
    "auth = azureml.core.authentication.ServicePrincipalAuthentication(tenant, username, password)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##TESTONLY\n",
    "subscription_id  = dbutils.widgets.get('subscription_id')\n",
    "resource_group = dbutils.widgets.get('resource_group')\n",
    "workspace_name = dbutils.widgets.get('workspace_name')\n",
    "workspace_region = dbutils.widgets.get('workspace_region')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##TESTONLY\n",
    "# import the Workspace class and check the azureml SDK version\n",
    "# exist_ok checks if workspace exists or not.\n",
    "\n",
    "from azureml.core import Workspace\n",
    "\n",
    "ws = Workspace.create(name = workspace_name,\n",
    "                      subscription_id = subscription_id,\n",
    "                      resource_group = resource_group, \n",
    "                      location = workspace_region,\n",
    "                      auth = auth,\n",
    "                      exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##PUBLISHONLY\n",
    "## import the Workspace class and check the azureml SDK version\n",
    "## exist_ok checks if workspace exists or not.\n",
    "#\n",
    "#from azureml.core import Workspace\n",
    "#\n",
    "#ws = Workspace.create(name = workspace_name,\n",
    "#                      subscription_id = subscription_id,\n",
    "#                      resource_group = resource_group, \n",
    "#                      location = workspace_region,\n",
    "#                      exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get workspace details\n",
    "ws.get_details()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##TESTONLY\n",
    "ws = Workspace(workspace_name = workspace_name,\n",
    "               subscription_id = subscription_id,\n",
    "               resource_group = resource_group,\n",
    "               auth = auth)\n",
    "\n",
    "# persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
    "ws.write_config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##PUBLISHONLY\n",
    "#ws = Workspace(workspace_name = workspace_name,\n",
    "#               subscription_id = subscription_id,\n",
    "#               resource_group = resource_group)\n",
    "#\n",
    "## persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
    "#ws.write_config()\n",
    "###if you need to give a different path/filename please use this\n",
    "###write_config(path=\"/databricks/driver/aml_config/\",file_name=<alias_conf.cfg>)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "help(Workspace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##TESTONLY\n",
    "# import the Workspace class and check the azureml SDK version\n",
    "from azureml.core import Workspace\n",
    "\n",
    "ws = Workspace.from_config(auth = auth)\n",
    "#ws = Workspace.from_config(<full path>)\n",
    "print('Workspace name: ' + ws.name, \n",
    "      'Azure region: ' + ws.location, \n",
    "      'Subscription id: ' + ws.subscription_id, \n",
    "      'Resource group: ' + ws.resource_group, sep = '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##PUBLISHONLY\n",
    "## import the Workspace class and check the azureml SDK version\n",
    "#from azureml.core import Workspace\n",
    "#\n",
    "#ws = Workspace.from_config()\n",
    "##ws = Workspace.from_config(<full path>)\n",
    "#print('Workspace name: ' + ws.name, \n",
    "#      'Azure region: ' + ws.location, \n",
    "#      'Subscription id: ' + ws.subscription_id, \n",
    "#      'Resource group: ' + ws.resource_group, sep = '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "pasha"
   },
   {
    "name": "wamartin"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  },
  "name": "01.Installation_and_Configuration",
  "notebookId": 3836944406456490
 },
 "nbformat": 4,
 "nbformat_minor": 1
 }
--- a/how-to-use-azureml/azure-databricks/automl_adb_readme.md
+++ b/how-to-use-azureml/azure-databricks/automl_adb_readme.md
@@ -1,47 +0,0 @@
 **PREVIEW capability** 
 Automated ML now supports Azure Databricks as a local compute to perform training (**public preview**). Azure Databricks is a managed Spark offering on Azure and customers already use it for advanced analytics. It provides a collaborative Notebook based environment with CPU or GPU based compute cluster. 
 - Customers who use Azure Databricks for advanced analytics can now use the same cluster to run automated machine learning experiments. 
 - You can keep the data within the same cluster. 
 - You can leverage the local worker nodes with autoscale and auto termination capabilities. 
 - You can use multiple cores of your Azure Databricks cluster to perform simultenous training. 
 - You can further tune the model generated by automated machine learning if you chose to. 
 - Every run (including the best run) is available as a pipeline. 
 - The model from the pipeline can be registered in Azure ML SDK workspace and then deployed to Azure managed compute (ACI or AKS) using the Azure Machine learning SDK.
 **Create Azure Databricks Cluster:**
 Select New Cluster and fill in following detail:
 - Cluster name: _yourclustername_
 - Cluster Mode: Any. **High Concurrency** preferred
 - Databricks Runtime: Any 4.x runtime.
 - Python version: **3**
 - Workers: 2 or higher.  
 - Max. number of **concurrent iterations** in Automated ML settings is **<=** to the number of **worker nodes** in your Databricks cluster.
 - Worker node VM types: **Memory optimized VM** preferred. 
 - Uncheck _Enable Autoscaling_
 It will take few minutes to create the cluster. Please ensure that the cluster state is running before proceeding further.
 **Install Azure ML with Automated ML SDK on your Azure Databricks cluster**
 - Select Import library
 - Source: Upload Python Egg or PyPI
 - PyPi Name: **azureml-sdk[automl_databricks]**
 - Click Install Library
 - Do not select _Attach automatically to all clusters_. In case you have selected earlier then you can go to your Home folder and deselect it.
 - Select the check box _Attach_ next to your cluster name
 (More details on how to attach and detach libs are here - [https://docs.databricks.com/user-guide/libraries.html#attach-a-library-to-a-cluster](https://docs.databricks.com/user-guide/libraries.html#attach-a-library-to-a-cluster) )
 - Ensure that there are no errors until Status changes to _Attached_. It may take a couple of minutes.
 **Note** - If you have the old build the please deselect it from cluster’s installed libs > move to trash. Install the new build and restart the cluster. And if still there is an issue then detach and reattach your cluster.
 **Now you can run the Automated ML sample notebook on your Azure Databricks cluster. Please let us know your feedback.**
--- a/how-to-use-azureml/machine-learning-pipelines/00.pipeline-setup.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/00.pipeline-setup.ipynb
@@ -0,0 +1,81 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install pandas\n",
    "!pip install requests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Widgets\n",
    "Install the following widgets to see the status of each run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!jupyter nbextension install --py --user azureml.widgets\n",
    "!jupyter nbextension enable --py --user azureml.widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "hichando"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/how-to-use-azureml/machine-learning-pipelines/README.md
+++ b/how-to-use-azureml/machine-learning-pipelines/README.md
@@ -34,16 +34,15 @@ Azure Machine Learning Pipelines optimize for simplicity, speed, and efficiency.
 In this directory, there are two types of notebooks: 
-* The first type of notebooks will introduce you to core Azure Machine Learning Pipelines features. These notebooks below belong in this category, and are designed to go in sequence; they're all located in the "intro-to-pipelines" folder:
+* The first type of notebooks will introduce you to core Azure Machine Learning Pipelines features. The notebooks below belong in this category, and are designed to go in sequence:
-1. [aml-pipelines-getting-started.ipynb](https://aka.ms/pl-get-started)
+1. aml-pipelines-getting-started.ipynb
-2. [aml-pipelines-with-data-dependency-steps.ipynb](https://aka.ms/pl-data-dep)
+2. aml-pipelines-with-data-dependency-steps.ipynb
-3. [aml-pipelines-publish-and-run-using-rest-endpoint.ipynb](https://aka.ms/pl-pub-rep)
+3. aml-pipelines-publish-and-run-using-rest-endpoint.ipynb
-4. [aml-pipelines-data-transfer.ipynb](https://aka.ms/pl-data-trans)
+4. aml-pipelines-data-transfer.ipynb
-5. [aml-pipelines-use-databricks-as-compute-target.ipynb](https://aka.ms/pl-databricks)
+5. aml-pipelines-use-databricks-as-compute-target.ipynb
-6. [aml-pipelines-use-adla-as-compute-target.ipynb] (https://aka.ms/pl-adla)
+6. aml-pipelines-use-adla-as-compute-target.ipynb
 * The second type of notebooks illustrate more sophisticated scenarios, and are independent of each other. These notebooks include:
-
+  - pipeline-batch-scoring.ipynb
-1. [pipeline-batch-scoring.ipynb](https://aka.ms/pl-batch-score)
+  - pipeline-style-transfer.ipynb
 2. [pipeline-style-transfer.ipynb] (https://aka.ms/pl-style-trans)
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb
@@ -251,18 +251,9 @@
    "    aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n",
    "    aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
    "    \n",
-    "print(\"Azure Machine Learning Compute attached\")\n"
+    "print(\"Azure Machine Learning Compute attached\")\n",
-   ]
+    "# For a more detailed view of current Azure Machine Learning Compute status, use the 'status' property    \n",
-  },
+    "print(aml_compute.status.serialize())"
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For a more detailed view of current Azure Machine Learning Compute status, use the 'status' property\n",
    "# example: un-comment the following line.\n",
    "# print(aml_compute.status.serialize())"
   ]
  },
  {
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb
@@ -91,18 +91,9 @@
    "                                                                min_nodes = 1, \n",
    "                                                                max_nodes = 4)    \n",
    "    aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n",
-    "    aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n"
+    "    aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
-   ]
+    "    \n",
-  },
+    "print(aml_compute.status.serialize())\n"
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For a more detailed view of current Azure Machine Learning Compute status, use the 'status' property\n",
    "# example: un-comment the following line.\n",
    "# print(aml_compute.status.serialize())"
   ]
  },
  {
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb
@@ -149,18 +149,9 @@
    "    aml_compute = ComputeTarget.create(ws, aml_compute_target, provisioning_config)\n",
    "    aml_compute.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
    "    \n",
-    "print(\"Aml Compute attached\")\n"
+    "print(\"Aml Compute attached\")\n",
-   ]
+    "# For a more detailed view of current AmlCompute status, use the 'status' property    \n",
-  },
+    "print(aml_compute.status.serialize())"
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For a more detailed view of current Azure Machine Learning Compute status, use the 'status' property\n",
    "# example: un-comment the following line.\n",
    "# print(aml_compute.status.serialize())"
   ]
  },
  {
--- a/how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/batch_scoring.py
+++ b/how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/batch_scoring.py
@@ -1,119 +0,0 @@
 # Copyright (c) Microsoft. All rights reserved.
 # Licensed under the MIT license.
 import os
 import argparse
 import datetime
 import time
 import tensorflow as tf
 from math import ceil
 import numpy as np
 import shutil
 from tensorflow.contrib.slim.python.slim.nets import inception_v3
 from azureml.core.model import Model
 slim = tf.contrib.slim
 parser = argparse.ArgumentParser(description="Start a tensorflow model serving")
 parser.add_argument('--model_name', dest="model_name", required=True)
 parser.add_argument('--label_dir', dest="label_dir", required=True)
 parser.add_argument('--dataset_path', dest="dataset_path", required=True)
 parser.add_argument('--output_dir', dest="output_dir", required=True)
 parser.add_argument('--batch_size', dest="batch_size", type=int, required=True)
 args = parser.parse_args()
 image_size = 299
 num_channel = 3
 # create output directory if it does not exist
 os.makedirs(args.output_dir, exist_ok=True)
 def get_class_label_dict(label_file):
    label = []
    proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()
    for l in proto_as_ascii_lines:
        label.append(l.rstrip())
    return label
 class DataIterator:
    def __init__(self, data_dir):
        self.file_paths = []
        image_list = os.listdir(data_dir)
        # total_size = len(image_list)
        self.file_paths = [data_dir + '/' + file_name.rstrip() for file_name in image_list]
        self.labels = [1 for file_name in self.file_paths]
    @property
    def size(self):
        return len(self.labels)
    def input_pipeline(self, batch_size):
        images_tensor = tf.convert_to_tensor(self.file_paths, dtype=tf.string)
        labels_tensor = tf.convert_to_tensor(self.labels, dtype=tf.int64)
        input_queue = tf.train.slice_input_producer([images_tensor, labels_tensor], shuffle=False)
        labels = input_queue[1]
        images_content = tf.read_file(input_queue[0])
        image_reader = tf.image.decode_jpeg(images_content, channels=num_channel, name="jpeg_reader")
        float_caster = tf.cast(image_reader, tf.float32)
        new_size = tf.constant([image_size, image_size], dtype=tf.int32)
        images = tf.image.resize_images(float_caster, new_size)
        images = tf.divide(tf.subtract(images, [0]), [255])
        image_batch, label_batch = tf.train.batch([images, labels], batch_size=batch_size, capacity=5 * batch_size)
        return image_batch
 def main(_):
    # start_time = datetime.datetime.now()
    label_file_name = os.path.join(args.label_dir, "labels.txt")
    label_dict = get_class_label_dict(label_file_name)
    classes_num = len(label_dict)
    test_feeder = DataIterator(data_dir=args.dataset_path)
    total_size = len(test_feeder.labels)
    count = 0
    # get model from model registry
    model_path = Model.get_model_path(args.model_name)
    with tf.Session() as sess:
        test_images = test_feeder.input_pipeline(batch_size=args.batch_size)
        with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
            input_images = tf.placeholder(tf.float32, [args.batch_size, image_size, image_size, num_channel])
            logits, _ = inception_v3.inception_v3(input_images,
                                                  num_classes=classes_num,
                                                  is_training=False)
            probabilities = tf.argmax(logits, 1)
        sess.run(tf.global_variables_initializer())
        sess.run(tf.local_variables_initializer())
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(sess=sess, coord=coord)
        saver = tf.train.Saver()
        saver.restore(sess, model_path)
        out_filename = os.path.join(args.output_dir, "result-labels.txt")
        with open(out_filename, "w") as result_file:
            i = 0
            while count < total_size and not coord.should_stop():
                test_images_batch = sess.run(test_images)
                file_names_batch = test_feeder.file_paths[i * args.batch_size:
                                                          min(test_feeder.size, (i + 1) * args.batch_size)]
                results = sess.run(probabilities, feed_dict={input_images: test_images_batch})
                new_add = min(args.batch_size, total_size - count)
                count += new_add
                i += 1
                for j in range(new_add):
                    result_file.write(os.path.basename(file_names_batch[j]) + ": " + label_dict[results[j]] + "\n")
                result_file.flush()
            coord.request_stop()
            coord.join(threads)
        # copy the file to artifacts
        shutil.copy(out_filename, "./outputs/")
        # Move the processed data out of the blob so that the next run can process the data.
 if __name__ == "__main__":
    tf.app.run()
--- a/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/neural_style_mpi.py
+++ b/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/neural_style_mpi.py
@@ -1,207 +0,0 @@
 # Original source: https://github.com/pytorch/examples/blob/master/fast_neural_style/neural_style/neural_style.py
 import argparse
 import os
 import sys
 import re
 from PIL import Image
 import torch
 from torchvision import transforms
 from mpi4py import MPI
 def load_image(filename, size=None, scale=None):
    img = Image.open(filename)
    if size is not None:
        img = img.resize((size, size), Image.ANTIALIAS)
    elif scale is not None:
        img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), Image.ANTIALIAS)
    return img
 def save_image(filename, data):
    img = data.clone().clamp(0, 255).numpy()
    img = img.transpose(1, 2, 0).astype("uint8")
    img = Image.fromarray(img)
    img.save(filename)
 class TransformerNet(torch.nn.Module):
    def __init__(self):
        super(TransformerNet, self).__init__()
        # Initial convolution layers
        self.conv1 = ConvLayer(3, 32, kernel_size=9, stride=1)
        self.in1 = torch.nn.InstanceNorm2d(32, affine=True)
        self.conv2 = ConvLayer(32, 64, kernel_size=3, stride=2)
        self.in2 = torch.nn.InstanceNorm2d(64, affine=True)
        self.conv3 = ConvLayer(64, 128, kernel_size=3, stride=2)
        self.in3 = torch.nn.InstanceNorm2d(128, affine=True)
        # Residual layers
        self.res1 = ResidualBlock(128)
        self.res2 = ResidualBlock(128)
        self.res3 = ResidualBlock(128)
        self.res4 = ResidualBlock(128)
        self.res5 = ResidualBlock(128)
        # Upsampling Layers
        self.deconv1 = UpsampleConvLayer(128, 64, kernel_size=3, stride=1, upsample=2)
        self.in4 = torch.nn.InstanceNorm2d(64, affine=True)
        self.deconv2 = UpsampleConvLayer(64, 32, kernel_size=3, stride=1, upsample=2)
        self.in5 = torch.nn.InstanceNorm2d(32, affine=True)
        self.deconv3 = ConvLayer(32, 3, kernel_size=9, stride=1)
        # Non-linearities
        self.relu = torch.nn.ReLU()
    def forward(self, X):
        y = self.relu(self.in1(self.conv1(X)))
        y = self.relu(self.in2(self.conv2(y)))
        y = self.relu(self.in3(self.conv3(y)))
        y = self.res1(y)
        y = self.res2(y)
        y = self.res3(y)
        y = self.res4(y)
        y = self.res5(y)
        y = self.relu(self.in4(self.deconv1(y)))
        y = self.relu(self.in5(self.deconv2(y)))
        y = self.deconv3(y)
        return y
 class ConvLayer(torch.nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride):
        super(ConvLayer, self).__init__()
        reflection_padding = kernel_size // 2
        self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
        self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride)
    def forward(self, x):
        out = self.reflection_pad(x)
        out = self.conv2d(out)
        return out
 class ResidualBlock(torch.nn.Module):
    """ResidualBlock
    introduced in: https://arxiv.org/abs/1512.03385
    recommended architecture: http://torch.ch/blog/2016/02/04/resnets.html
    """
    def __init__(self, channels):
        super(ResidualBlock, self).__init__()
        self.conv1 = ConvLayer(channels, channels, kernel_size=3, stride=1)
        self.in1 = torch.nn.InstanceNorm2d(channels, affine=True)
        self.conv2 = ConvLayer(channels, channels, kernel_size=3, stride=1)
        self.in2 = torch.nn.InstanceNorm2d(channels, affine=True)
        self.relu = torch.nn.ReLU()
    def forward(self, x):
        residual = x
        out = self.relu(self.in1(self.conv1(x)))
        out = self.in2(self.conv2(out))
        out = out + residual
        return out
 class UpsampleConvLayer(torch.nn.Module):
    """UpsampleConvLayer
    Upsamples the input and then does a convolution. This method gives better results
    compared to ConvTranspose2d.
    ref: http://distill.pub/2016/deconv-checkerboard/
    """
    def __init__(self, in_channels, out_channels, kernel_size, stride, upsample=None):
        super(UpsampleConvLayer, self).__init__()
        self.upsample = upsample
        if upsample:
            self.upsample_layer = torch.nn.Upsample(mode='nearest', scale_factor=upsample)
        reflection_padding = kernel_size // 2
        self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
        self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride)
    def forward(self, x):
        x_in = x
        if self.upsample:
            x_in = self.upsample_layer(x_in)
        out = self.reflection_pad(x_in)
        out = self.conv2d(out)
        return out
 def stylize(args, comm):
    rank = comm.Get_rank()
    size = comm.Get_size()
    device = torch.device("cuda" if args.cuda else "cpu")
    with torch.no_grad():
        style_model = TransformerNet()
        state_dict = torch.load(os.path.join(args.model_dir, args.style + ".pth"))
        # remove saved deprecated running_* keys in InstanceNorm from the checkpoint
        for k in list(state_dict.keys()):
            if re.search(r'in\d+\.running_(mean|var)$', k):
                del state_dict[k]
        style_model.load_state_dict(state_dict)
        style_model.to(device)
        filenames = os.listdir(args.content_dir)
        filenames = sorted(filenames)
        partition_size = len(filenames) // size
        partitioned_filenames = filenames[rank * partition_size: (rank + 1) * partition_size]
        print("RANK {} - is processing {} images out of the total {}".format(rank, len(partitioned_filenames),
                                                                             len(filenames)))
        output_paths = []
        for filename in partitioned_filenames:
            # print("Processing {}".format(filename))
            full_path = os.path.join(args.content_dir, filename)
            content_image = load_image(full_path, scale=args.content_scale)
            content_transform = transforms.Compose([
                transforms.ToTensor(),
                transforms.Lambda(lambda x: x.mul(255))
            ])
            content_image = content_transform(content_image)
            content_image = content_image.unsqueeze(0).to(device)
            output = style_model(content_image).cpu()
            output_path = os.path.join(args.output_dir, filename)
            save_image(output_path, output[0])
            output_paths.append(output_path)
        print("RANK {} - number of pre-aggregated output files {}".format(rank, len(output_paths)))
        output_paths_list = comm.gather(output_paths, root=0)
        if rank == 0:
            print("RANK {} - number of aggregated output files {}".format(rank, len(output_paths_list)))
            print("RANK {} - end".format(rank))
 def main():
    arg_parser = argparse.ArgumentParser(description="parser for fast-neural-style")
    arg_parser.add_argument("--content-scale", type=float, default=None,
                            help="factor for scaling down the content image")
    arg_parser.add_argument("--model-dir", type=str, required=True,
                            help="saved model to be used for stylizing the image.")
    arg_parser.add_argument("--cuda", type=int, required=True,
                            help="set it to 1 for running on GPU, 0 for CPU")
    arg_parser.add_argument("--style", type=str, help="style name")
    arg_parser.add_argument("--content-dir", type=str, required=True,
                            help="directory holding the images")
    arg_parser.add_argument("--output-dir", type=str, required=True,
                            help="directory holding the output images")
    args = arg_parser.parse_args()
    comm = MPI.COMM_WORLD
    if args.cuda and not torch.cuda.is_available():
        print("ERROR: cuda is not available, try running on CPU")
        sys.exit(1)
    os.makedirs(args.output_dir, exist_ok=True)
    stylize(args, comm)
 if __name__ == "__main__":
    main()
--- a/how-to-use-azureml/training-with-deep-learning/distributed-cntk-with-custom-docker/cntk_distr_mnist.py
+++ b/how-to-use-azureml/training-with-deep-learning/distributed-cntk-with-custom-docker/cntk_distr_mnist.py
@@ -1,117 +0,0 @@
 # Copyright (c) Microsoft. All rights reserved.
 # Licensed under the MIT license.
 # Adapted from:
 # https://github.com/Microsoft/CNTK/blob/master/Examples/Image/Classification/ConvNet/Python/ConvNet_MNIST.py
 # ====================================================================
 """Train a CNN model on the MNIST dataset via distributed training."""
 from __future__ import print_function
 import numpy as np
 import os
 import cntk as C
 import argparse
 from cntk.train.training_session import CheckpointConfig, TestConfig
 def create_reader(path, is_training, input_dim, label_dim, total_number_of_samples):
    """Define the reader for both training and evaluation action."""
    return C.io.MinibatchSource(C.io.CTFDeserializer(path, C.io.StreamDefs(
        features=C.io.StreamDef(field='features', shape=input_dim),
        labels=C.io.StreamDef(field='labels', shape=label_dim)
    )), randomize=is_training, max_samples=total_number_of_samples)
 def convnet_mnist(max_epochs, output_dir, data_dir, debug_output=False, epoch_size=60000, minibatch_size=64):
    """Creates and trains a feedforward classification model for MNIST images."""
    image_height = 28
    image_width = 28
    num_channels = 1
    input_dim = image_height * image_width * num_channels
    num_output_classes = 10
    # Input variables denoting the features and label data
    input_var = C.ops.input_variable((num_channels, image_height, image_width), np.float32)
    label_var = C.ops.input_variable(num_output_classes, np.float32)
    # Instantiate the feedforward classification model
    scaled_input = C.ops.element_times(C.ops.constant(0.00390625), input_var)
    with C.layers.default_options(activation=C.ops.relu, pad=False):
        conv1 = C.layers.Convolution2D((5, 5), 32, pad=True)(scaled_input)
        pool1 = C.layers.MaxPooling((3, 3), (2, 2))(conv1)
        conv2 = C.layers.Convolution2D((3, 3), 48)(pool1)
        pool2 = C.layers.MaxPooling((3, 3), (2, 2))(conv2)
        conv3 = C.layers.Convolution2D((3, 3), 64)(pool2)
        f4 = C.layers.Dense(96)(conv3)
        drop4 = C.layers.Dropout(0.5)(f4)
        z = C.layers.Dense(num_output_classes, activation=None)(drop4)
    ce = C.losses.cross_entropy_with_softmax(z, label_var)
    pe = C.metrics.classification_error(z, label_var)
    # Load train data
    reader_train = create_reader(os.path.join(data_dir, 'Train-28x28_cntk_text.txt'), True,
                                 input_dim, num_output_classes, max_epochs * epoch_size)
    # Load test data
    reader_test = create_reader(os.path.join(data_dir, 'Test-28x28_cntk_text.txt'), False,
                                input_dim, num_output_classes, C.io.FULL_DATA_SWEEP)
    # Set learning parameters
    lr_per_sample = [0.001] * 10 + [0.0005] * 10 + [0.0001]
    lr_schedule = C.learning_parameter_schedule_per_sample(lr_per_sample, epoch_size=epoch_size)
    mms = [0] * 5 + [0.9990239141819757]
    mm_schedule = C.learners.momentum_schedule_per_sample(mms, epoch_size=epoch_size)
    # Instantiate the trainer object to drive the model training
    local_learner = C.learners.momentum_sgd(z.parameters, lr_schedule, mm_schedule)
    progress_printer = C.logging.ProgressPrinter(
        tag='Training',
        rank=C.train.distributed.Communicator.rank(),
        num_epochs=max_epochs,
    )
    learner = C.train.distributed.data_parallel_distributed_learner(local_learner)
    trainer = C.Trainer(z, (ce, pe), learner, progress_printer)
    # define mapping from reader streams to network inputs
    input_map_train = {
        input_var: reader_train.streams.features,
        label_var: reader_train.streams.labels
    }
    input_map_test = {
        input_var: reader_test.streams.features,
        label_var: reader_test.streams.labels
    }
    C.logging.log_number_of_parameters(z)
    print()
    C.train.training_session(
        trainer=trainer,
        mb_source=reader_train,
        model_inputs_to_streams=input_map_train,
        mb_size=minibatch_size,
        progress_frequency=epoch_size,
        checkpoint_config=CheckpointConfig(frequency=epoch_size,
                                           filename=os.path.join(output_dir, "ConvNet_MNIST")),
        test_config=TestConfig(reader_test, minibatch_size=minibatch_size,
                               model_inputs_to_streams=input_map_test)
    ).train()
    return
 if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--num_epochs', help='Total number of epochs to train', type=int, default='40')
    parser.add_argument('--output_dir', help='Output directory', required=False, default='outputs')
    parser.add_argument('--data_dir', help='Directory with training data')
    args = parser.parse_args()
    os.makedirs(args.output_dir, exist_ok=True)
    convnet_mnist(args.num_epochs, args.output_dir, args.data_dir)
    # Must call MPI finalize when process exit without exceptions
    C.train.distributed.Communicator.finalize()
--- a/how-to-use-azureml/training-with-deep-learning/distributed-cntk-with-custom-docker/distributed-cntk-with-custom-docker.ipynb
+++ b/how-to-use-azureml/training-with-deep-learning/distributed-cntk-with-custom-docker/distributed-cntk-with-custom-docker.ipynb
@@ -1,394 +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": [
    "# Distributed CNTK using custom docker images\n",
    "In this tutorial, you will train a CNTK model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using a custom docker image and distributed training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
    "* Go through the [00.configuration.ipynb]() notebook to:\n",
    "    * install the AML SDK\n",
    "    * create a workspace and its configuration file (`config.json`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check core SDK version number\n",
    "import azureml.core\n",
    "\n",
    "print(\"SDK version:\", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "Diagnostics"
    ]
   },
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "\n",
    "set_diagnostics_collection(send_diagnostics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize workspace\n",
    "\n",
    "Initialize a [Workspace](https://review.docs.microsoft.com/en-us/azure/machine-learning/service/concept-azure-machine-learning-architecture?branch=release-ignite-aml#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.workspace import Workspace\n",
    "\n",
    "ws = Workspace.from_config()\n",
    "print('Workspace name: ' + ws.name, \n",
    "      'Azure region: ' + ws.location, \n",
    "      'Subscription id: ' + ws.subscription_id, \n",
    "      'Resource group: ' + ws.resource_group, sep = '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create or Attach existing AmlCompute\n",
    "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
    "\n",
    "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
    "\n",
    "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
    "from azureml.core.compute_target import ComputeTargetException\n",
    "\n",
    "# choose a name for your cluster\n",
    "cluster_name = \"gpucluster\"\n",
    "\n",
    "try:\n",
    "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
    "    print('Found existing compute target.')\n",
    "except ComputeTargetException:\n",
    "    print('Creating a new compute target...')\n",
    "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6',\n",
    "                                                           max_nodes=4)\n",
    "\n",
    "    # create the cluster\n",
    "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
    "\n",
    "    compute_target.wait_for_completion(show_output=True)\n",
    "\n",
    "# Use the 'status' property to get a detailed status for the current AmlCompute. \n",
    "print(compute_target.status.serialize())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upload training data\n",
    "For this tutorial, we will be using the MNIST dataset.\n",
    "\n",
    "First, let's download the dataset. We've included the `install_mnist.py` script to download the data and convert it to a CNTK-supported format. Our data files will get written to a directory named `'mnist'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import install_mnist\n",
    "\n",
    "install_mnist.main('mnist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make the data accessible for remote training, you will need to upload the data from your local machine to the cloud. AML provides a convenient way to do so via a [Datastore](https://docs.microsoft.com/azure/machine-learning/service/how-to-access-data). The datastore provides a mechanism for you to upload/download data, and interact with it from your remote compute targets. \n",
    "\n",
    "Each workspace is associated with a default datastore. In this tutorial, we will upload the training data to this default datastore, which we will then mount on the remote compute for training in the next section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = ws.get_default_datastore()\n",
    "print(ds.datastore_type, ds.account_name, ds.container_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code will upload the training data to the path `./mnist` on the default datastore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.upload(src_dir='./mnist', target_path='./mnist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's get a reference to the path on the datastore with the training data. We can do so using the `path` method. In the next section, we can then pass this reference to our training script's `--data_dir` argument. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_on_datastore = 'mnist'\n",
    "ds_data = ds.path(path_on_datastore)\n",
    "print(ds_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train model on the remote compute\n",
    "Now that we have the cluster ready to go, let's run our distributed training job."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a project directory\n",
    "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "project_folder = './cntk-distr'\n",
    "os.makedirs(project_folder, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copy the training script `cntk_distr_mnist.py` into this project directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shutil\n",
    "\n",
    "shutil.copy('cntk_distr_mnist.py', project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create an experiment\n",
    "Create an [experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed CNTK tutorial. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Experiment\n",
    "\n",
    "experiment_name = 'cntk-distr'\n",
    "experiment = Experiment(ws, name=experiment_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create an Estimator\n",
    "The AML SDK's base Estimator enables you to easily submit custom scripts for both single-node and distributed runs. You should this generic estimator for training code using frameworks such as sklearn or CNTK that don't have corresponding custom estimators. For more information on using the generic estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-ml-models)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.train.estimator import *\n",
    "\n",
    "script_params = {\n",
    "    '--num_epochs': 20,\n",
    "    '--data_dir': ds_data.as_mount(),\n",
    "    '--output_dir': './outputs'\n",
    "}\n",
    "\n",
    "estimator = Estimator(source_directory=project_folder,\n",
    "                      compute_target=compute_target,\n",
    "                      entry_script='cntk_distr_mnist.py',\n",
    "                      script_params=script_params,\n",
    "                      node_count=2,\n",
    "                      process_count_per_node=1,\n",
    "                      distributed_backend='mpi',     \n",
    "                      pip_packages=['cntk-gpu==2.6'],\n",
    "                      custom_docker_base_image='microsoft/mmlspark:gpu-0.12',\n",
    "                      use_gpu=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would like to train our model using a [pre-built Docker container](https://hub.docker.com/r/microsoft/mmlspark/). To do so, specify the name of the docker image to the argument `custom_docker_base_image`. You can only provide images available in public docker repositories such as Docker Hub using this argument. To use an image from a private docker repository, use the constructor's `environment_definition` parameter instead. Finally, we provide the `cntk` package to `pip_packages` to install CNTK 2.6 on our custom image.\n",
    "\n",
    "The above code specifies that we will run our training script on `2` nodes, with one worker per node. In order to run distributed CNTK, which uses MPI, you must provide the argument `distributed_backend='mpi'`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submit job\n",
    "Run your experiment by submitting your estimator object. Note that this call is asynchronous."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run = experiment.submit(estimator)\n",
    "print(run)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Monitor your run\n",
    "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "\n",
    "RunDetails(run).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, you can block until the script has completed training before running more code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run.wait_for_completion(show_output=True)"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "minxia"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/how-to-use-azureml/training-with-deep-learning/distributed-cntk-with-custom-docker/install_mnist.py
+++ b/how-to-use-azureml/training-with-deep-learning/distributed-cntk-with-custom-docker/install_mnist.py
@@ -1,96 +0,0 @@
 # Copyright (c) Microsoft. All rights reserved.
 # Licensed under the MIT license.
 # Script from:
 # https://github.com/Microsoft/CNTK/blob/master/Examples/Image/DataSets/MNIST/install_mnist.py
 from __future__ import print_function
 try:
    from urllib.request import urlretrieve
 except ImportError:
    from urllib import urlretrieve
 import gzip
 import os
 import struct
 import numpy as np
 def loadData(src, cimg):
    print('Downloading ' + src)
    gzfname, h = urlretrieve(src, './delete.me')
    print('Done.')
    try:
        with gzip.open(gzfname) as gz:
            n = struct.unpack('I', gz.read(4))
            # Read magic number.
            if n[0] != 0x3080000:
                raise Exception('Invalid file: unexpected magic number.')
            # Read number of entries.
            n = struct.unpack('>I', gz.read(4))[0]
            if n != cimg:
                raise Exception('Invalid file: expected {0} entries.'.format(cimg))
            crow = struct.unpack('>I', gz.read(4))[0]
            ccol = struct.unpack('>I', gz.read(4))[0]
            if crow != 28 or ccol != 28:
                raise Exception('Invalid file: expected 28 rows/cols per image.')
            # Read data.
            res = np.fromstring(gz.read(cimg * crow * ccol), dtype=np.uint8)
    finally:
        os.remove(gzfname)
    return res.reshape((cimg, crow * ccol))
 def loadLabels(src, cimg):
    print('Downloading ' + src)
    gzfname, h = urlretrieve(src, './delete.me')
    print('Done.')
    try:
        with gzip.open(gzfname) as gz:
            n = struct.unpack('I', gz.read(4))
            # Read magic number.
            if n[0] != 0x1080000:
                raise Exception('Invalid file: unexpected magic number.')
            # Read number of entries.
            n = struct.unpack('>I', gz.read(4))
            if n[0] != cimg:
                raise Exception('Invalid file: expected {0} rows.'.format(cimg))
            # Read labels.
            res = np.fromstring(gz.read(cimg), dtype=np.uint8)
    finally:
        os.remove(gzfname)
    return res.reshape((cimg, 1))
 def load(dataSrc, labelsSrc, cimg):
    data = loadData(dataSrc, cimg)
    labels = loadLabels(labelsSrc, cimg)
    return np.hstack((data, labels))
 def savetxt(filename, ndarray):
    with open(filename, 'w') as f:
        labels = list(map(' '.join, np.eye(10, dtype=np.uint).astype(str)))
        for row in ndarray:
            row_str = row.astype(str)
            label_str = labels[row[-1]]
            feature_str = ' '.join(row_str[:-1])
            f.write('|labels {} |features {}\n'.format(label_str, feature_str))
 def main(data_dir):
    os.makedirs(data_dir, exist_ok=True)
    train = load('http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',
                 'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz', 60000)
    print('Writing train text file...')
    train_txt = os.path.join(data_dir, 'Train-28x28_cntk_text.txt')
    savetxt(train_txt, train)
    print('Done.')
    test = load('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz',
                'http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz', 10000)
    print('Writing test text file...')
    test_txt = os.path.join(data_dir, 'Test-28x28_cntk_text.txt')
    savetxt(test_txt, test)
    print('Done.')
 if __name__ == "__main__":
    main('mnist')
--- a/how-to-use-azureml/training-with-deep-learning/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb
+++ b/how-to-use-azureml/training-with-deep-learning/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb
@@ -14,7 +14,7 @@
   "metadata": {},
   "source": [
    "# Distributed PyTorch with Horovod\n",
-    "In this tutorial, you will train a PyTorch model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using distributed training via [Horovod](https://github.com/uber/horovod) across a GPU cluster."
+    "In this tutorial, you will train a PyTorch model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using distributed training via [Horovod](https://github.com/uber/horovod)."
   ]
  },
  {
@@ -22,8 +22,11 @@
   "metadata": {},
   "source": [
    "## Prerequisites\n",
-    "* Go through the [Configuration](https://github.com/Azure/MachineLearningNotebooks/blob/master/configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`\n",
+    "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning (AML)\n",
-    "* Review the [tutorial](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) on single-node PyTorch training using Azure Machine Learning"
+    "* Go through the [00.configuration.ipynb](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) notebook to:\n",
    "    * install the AML SDK\n",
    "    * create a workspace and its configuration file (`config.json`)\n",
    "* Review the [tutorial](https://aka.ms/aml-notebook-pytorch) on single-node PyTorch training using the SDK"
   ]
  },
  {
@@ -89,12 +92,10 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "## Create or attach existing AmlCompute\n",
+    "## Create a remote compute target\n",
-    "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource. Specifically, the below code creates an `STANDARD_NC6` GPU cluster that autoscales from `0` to `4` nodes.\n",
+    "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an `AmlCompute` cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n",
    "\n",
-    "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace, this code will skip the creation process.\n",
+    "**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process."
    "\n",
    "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
   ]
  },
  {
@@ -114,7 +115,7 @@
    "    print('Found existing compute target.')\n",
    "except ComputeTargetException:\n",
    "    print('Creating a new compute target...')\n",
-    "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6',\n",
+    "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
    "                                                           max_nodes=4)\n",
    "\n",
    "    # create the cluster\n",
@@ -122,7 +123,7 @@
    "\n",
    "    compute_target.wait_for_completion(show_output=True)\n",
    "\n",
-    "# Use the 'status' property to get a detailed status for the current AmlCompute. \n",
+    "# Use the 'status' property to get a detailed status for the current cluster. \n",
    "print(compute_target.status.serialize())"
   ]
  },
@@ -130,7 +131,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "The above code creates GPU compute. If you instead want to create CPU compute, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
+    "The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
   ]
  },
  {
@@ -138,7 +139,7 @@
   "metadata": {},
   "source": [
    "## Train model on the remote compute\n",
-    "Now that we have the AmlCompute ready to go, let's run our distributed training job."
+    "Now that we have the cluster ready to go, let's run our distributed training job."
   ]
  },
  {
@@ -165,27 +166,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "### Prepare training script\n",
+    "Copy the training script `pytorch_horovod_mnist.py` into this project directory."
    "Now you will need to create your training script. In this tutorial, the script for distributed training of MNIST is already provided for you at `pytorch_horovod_mnist.py`. In practice, you should be able to take any custom PyTorch training script as is and run it with Azure ML without having to modify your code.\n",
    "\n",
    "However, if you would like to use Azure ML's [metric logging](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#logging) capabilities, you will have to add a small amount of Azure ML logic inside your training script. In this example, at each logging interval, we will log the loss for that minibatch to our Azure ML run.\n",
    "\n",
    "To do so, in `pytorch_horovod_mnist.py`, we will first access the Azure ML `Run` object within the script:\n",
    "```Python\n",
    "from azureml.core.run import Run\n",
    "run = Run.get_context()\n",
    "```\n",
    "Later within the script, we log the loss metric to our run:\n",
    "```Python\n",
    "run.log('loss', loss.item())\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once your script is ready, copy the training script `pytorch_horovod_mnist.py` into the project directory."
   ]
  },
  {
@@ -224,7 +205,7 @@
   "metadata": {},
   "source": [
    "### Create a PyTorch estimator\n",
-    "The Azure ML SDK's PyTorch estimator enables you to easily submit PyTorch training jobs for both single-node and distributed runs. For more information on the PyTorch estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-pytorch)."
+    "The AML SDK's PyTorch estimator enables you to easily submit PyTorch training jobs for both single-node and distributed runs. For more information on the PyTorch estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-pytorch)."
   ]
  },
  {
@@ -251,26 +232,6 @@
    "The above code specifies that we will run our training script on `2` nodes, with one worker per node. In order to execute a distributed run using MPI/Horovod, you must provide the argument `distributed_backend='mpi'`. Using this estimator with these settings, PyTorch, Horovod and their dependencies will be installed for you. However, if your script also uses other packages, make sure to install them via the `PyTorch` constructor's `pip_packages` or `conda_packages` parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use the latest version of PyTorch 1.0, run the following cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator.conda_dependencies.remove_conda_package('pytorch=0.4.0')\n",
    "estimator.conda_dependencies.remove_pip_package('horovod==0.13.11')\n",
    "estimator.conda_dependencies.add_conda_package('pytorch-nightly')\n",
    "estimator.conda_dependencies.add_channel('pytorch')\n",
    "estimator.conda_dependencies.add_pip_package('horovod==0.15.2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
@@ -294,7 +255,7 @@
   "metadata": {},
   "source": [
    "### Monitor your run\n",
-    "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes. You can see that the widget automatically plots and visualizes the loss metric that we logged to the Azure ML run."
+    "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
   ]
  },
  {
--- a/how-to-use-azureml/training-with-deep-learning/distributed-pytorch-with-horovod/pytorch_horovod_mnist.py
+++ b/how-to-use-azureml/training-with-deep-learning/distributed-pytorch-with-horovod/pytorch_horovod_mnist.py
@@ -1,16 +1,17 @@
 # Copyright 2017 Uber Technologies, Inc.
 # Licensed under the Apache License, Version 2.0
 # Script from horovod/examples: https://github.com/uber/horovod/blob/master/examples/pytorch_mnist.py
 from __future__ import print_function
 import argparse
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 from torchvision import datasets, transforms
 from torch.autograd import Variable
 import torch.utils.data.distributed
 import horovod.torch as hvd
 from azureml.core.run import Run
 # get the Azure ML run object
 run = Run.get_context()
 # Training settings
 parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
 parser.add_argument('--batch-size', type=int, default=64, metavar='N',
@@ -29,8 +30,6 @@ parser.add_argument('--seed', type=int, default=42, metavar='S',
                    help='random seed (default: 42)')
 parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                    help='how many batches to wait before logging training status')
 parser.add_argument('--fp16-allreduce', action='store_true', default=False,
                    help='use fp16 compression during allreduce')
 args = parser.parse_args()
 args.cuda = not args.no_cuda and torch.cuda.is_available()
@@ -98,13 +97,9 @@ hvd.broadcast_parameters(model.state_dict(), root_rank=0)
 optimizer = optim.SGD(model.parameters(), lr=args.lr * hvd.size(),
                      momentum=args.momentum)
 # Horovod: (optional) compression algorithm.
 compression = hvd.Compression.fp16 if args.fp16_allreduce else hvd.Compression.none
 # Horovod: wrap optimizer with DistributedOptimizer.
-optimizer = hvd.DistributedOptimizer(optimizer,
+optimizer = hvd.DistributedOptimizer(
-                                     named_parameters=model.named_parameters(),
+    optimizer, named_parameters=model.named_parameters())
                                     compression=compression)
 def train(epoch):
@@ -113,6 +108,7 @@ def train(epoch):
    for batch_idx, (data, target) in enumerate(train_loader):
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        data, target = Variable(data), Variable(target)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
@@ -121,16 +117,13 @@ def train(epoch):
        if batch_idx % args.log_interval == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_sampler),
-                100. * batch_idx / len(train_loader), loss.item()))
+                100. * batch_idx / len(train_loader), loss.data[0]))
            # log the loss to the Azure ML run
            run.log('loss', loss.item())
 def metric_average(val, name):
-    tensor = torch.tensor(val)
+    tensor = torch.FloatTensor([val])
    avg_tensor = hvd.allreduce(tensor, name=name)
-    return avg_tensor.item()
+    return avg_tensor[0]
 def test():
@@ -140,9 +133,10 @@ def test():
    for data, target in test_loader:
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        data, target = Variable(data, volatile=True), Variable(target)
        output = model(data)
        # sum up batch loss
-        test_loss += F.nll_loss(output, target, size_average=False).item()
+        test_loss += F.nll_loss(output, target, size_average=False).data[0]
        # get the index of the max log-probability
        pred = output.data.max(1, keepdim=True)[1]
        test_accuracy += pred.eq(target.data.view_as(pred)).cpu().float().sum()
--- a/how-to-use-azureml/training/README.md
+++ b/how-to-use-azureml/training/README.md
@@ -1,8 +0,0 @@
 ## Using basic training APIs
 Follow these sample notebooks to learn:
 1. [Train within notebook](train-within-notebook): train a simple scikit-learn model using the Jupyter kernel and deploy the model to Azure Container Service.
 2. [Train on local](train-on-local): train a model using local computer as compute target.
 3. [Train on remove VM](train-on-remote-vm): train a model using a remote Azure VM as compute target.
 4. [Logging API](logging-api): experiment with various logging functions to create runs and automatically generate graphs.
--- a/how-to-use-azureml/training/logging-api/logging-api.ipynb
+++ b/how-to-use-azureml/training/logging-api/logging-api.ipynb
@@ -22,7 +22,7 @@
   "metadata": {},
   "source": [
    "## Prerequisites\n",
-    "Make sure you go through the [Configuration](../../../configuration.ipynb) Notebook first if you haven't. Also make sure you have tqdm and matplotlib installed in the current kernel.\n",
+    "Make sure you go through the [00. Installation and Configuration](../../00.configuration.ipynb) Notebook first if you haven't. Also make sure you have tqdm and matplotlib installed in the current kernel.\n",
    "\n",
    "```\n",
    "(myenv) $ conda install -y tqdm matplotlib\n",
--- a/how-to-use-azureml/training/train-on-local/train-on-local.ipynb
+++ b/how-to-use-azureml/training/train-on-local/train-on-local.ipynb
@@ -29,7 +29,7 @@
   "metadata": {},
   "source": [
    "## Prerequisites\n",
-    "Make sure you go through the [Configuration](../../../configuration.ipynb) Notebook first if you haven't."
+    "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
   ]
  },
  {
@@ -281,7 +281,6 @@
   "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",
    "NOTE: The GPU base image must be used on Microsoft Azure Services only such as ACI, AML Compute, Azure VMs, and AKS.\n",
    "\n",
    "You can also ask the system to pull down a Docker image and execute your scripts in it."
   ]
--- a/how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb
+++ b/how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb
@@ -30,7 +30,7 @@
   "metadata": {},
   "source": [
    "## Prerequisites\n",
-    "Make sure you go through the [Configuration](../../../configuration.ipynb) Notebook first if you haven't."
+    "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
   ]
  },
  {
--- a/how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb
+++ b/how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb
@@ -13,58 +13,60 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# Train and deploy a model\n",
+    "# 01. Train in the Notebook & Deploy Model to ACI\n",
    "_**Create and deploy a model directly from a notebook**_\n",
    "\n",
-    "---\n",
+    "* Load workspace\n",
-    "---\n",
+    "* Train a simple regression model directly in the Notebook python kernel\n",
-    "\n",
+    "* Record run history\n",
-    "## Contents\n",
+    "* Find the best model in run history and download it.\n",
-    "1. [Introduction](#Introduction)\n",
+    "* Deploy the model as an Azure Container Instance (ACI)"
    "1. [Setup](#Setup)\n",
    "1. [Data](#Data)\n",
    "1. [Train](#Train)\n",
    "    1. Viewing run results\n",
    "    1. Simple parameter sweep\n",
    "    1. Viewing experiment results\n",
    "    1. Select the best model\n",
    "1. [Deploy](#Deploy)\n",
    "    1. Register the model\n",
    "    1. Create a scoring file\n",
    "    1. Describe your environment\n",
    "    1. Descrice your target compute\n",
    "    1. Deploy your webservice\n",
    "    1. Test your webservice\n",
    "    1. Clean up\n",
    "1. [Next Steps](#Next%20Steps)\n",
    "\n",
    "---\n",
    "\n",
    "## Introduction\n",
    "Azure Machine Learning provides capabilities to control all aspects of model training and deployment directly from a notebook using the AML Python SDK.  In this notebook we will\n",
    "* connect to our AML Workspace\n",
    "* create an experiment that contains multiple runs with tracked metrics\n",
    "* choose the best model created across all runs\n",
    "* deploy that model as a service\n",
    "\n",
    "In the end we will have a model deployed as a web service which we can call from an HTTP endpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "---\n",
+    "## Prerequisites\n",
    "1. Make sure you go through the [00. Installation and Configuration](../../00.configuration.ipynb) Notebook first if you haven't. \n",
    "\n",
-    "## Setup\n",
+    "2. Install following pre-requisite libraries to your conda environment and restart notebook.\n",
    "Make sure you have completed the [Configuration](../../../configuration.ipynb) notebook to set up your Azure Machine Learning workspace and ensure other common prerequisites are met.  From the configuration, the important sections are the workspace configuration and ACI regristration.\n",
    "\n",
    "We will also need the following libraries install to our conda environment.  If these are not installed, use the following command to do so and restart the notebook.\n",
    "```shell\n",
    "(myenv) $ conda install -y matplotlib tqdm scikit-learn\n",
    "```\n",
    "\n",
-    "For this notebook we need the Azure ML SDK and access to our workspace.  The following cell imports the SDK, checks the version, and accesses our already configured AzureML workspace."
+    "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"
   ]
  },
  {
@@ -77,15 +79,32 @@
   },
   "outputs": [],
   "source": [
    "import azureml.core\n",
    "from azureml.core import Experiment, Run, Workspace\n",
    "import azureml.core\n",
    "\n",
    "# Check core SDK version number\n",
-    "print(\"This notebook was created using version 1.0.2 of the Azure ML SDK\")\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
-    "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")\n",
+   ]
-    "print(\"\")\n",
+  },
-    "\n",
+  {
   "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",
@@ -97,10 +116,8 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "---\n",
+    "## Set experiment name\n",
-    "\n",
+    "Choose a name for experiment."
    "## Data\n",
    "We will use the diabetes dataset for this experiement, a well-known small dataset that comes with scikit-learn.  This cell loads the dataset and splits it into random training and testing sets.\n"
   ]
  },
  {
@@ -109,6 +126,23 @@
   "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",
@@ -121,25 +155,36 @@
    "data = {\n",
    "    \"train\":{\"X\": X_train, \"y\": y_train},        \n",
    "    \"test\":{\"X\": X_test, \"y\": y_test}\n",
-    "}\n",
+    "}"
    "\n",
    "print (\"Data contains\", len(data['train']['X']), \"training samples and\",len(data['test']['X']), \"test samples\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "---\n",
+    "### Train a simple Ridge model\n",
-    "## Train\n",
+    "Train a very simple Ridge regression model in scikit-learn, and save it as a pickle file."
-    "\n",
+   ]
-    "Let's use scikit-learn to train a simple Ridge regression model.  We use AML to record interesting information about the model in an Experiment.  An Experiment contains a series of trials called Runs.  During this trial we use AML in the following way:\n",
+  },
-    "* We access an experiment from our AML workspace by name, which will be created if it doesn't exist\n",
+  {
-    "* We use `start_logging` to create a new run in this experiment\n",
+   "cell_type": "code",
-    "* We use `run.log()` to record a parameter, alpha, and an accuracy measure - the Mean Squared Error (MSE) to the run.  We will be able to review and compare these measures in the Azure Portal at a later time.\n",
+   "execution_count": null,
-    "* We store the resulting model in the **outputs** directory, which is automatically captured by AML when the run is complete.\n",
+   "metadata": {},
-    "* We use `run.take_snapshot()` to capture *this* notebook so we can reproduce this experiment at a later time.\n",
+   "outputs": [],
-    "* We use `run.complete()` to indicate that the run is over and results can be captured and finalized"
+   "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."
   ]
  },
  {
@@ -153,29 +198,18 @@
   },
   "outputs": [],
   "source": [
-    "# Get an experiment object from Azure Machine Learning\n",
+    "experiment = Experiment(workspace=ws, name=experiment_name)\n",
-    "experiment = Experiment(workspace=ws, name=\"train-within-notebook\")\n",
+    "run = experiment.start_logging()\n",
    "\n",
-    "# Create a run object in the experiment\n",
+    "run.tag(\"Description\",\"My first run!\")\n",
    "run = experiment.start_logging()# Log the algorithm parameter alpha to the run\n",
    "run.log('alpha', 0.03)\n",
-    "\n",
+    "reg = Ridge(alpha=0.03)\n",
-    "# Create, fit, and test the scikit-learn Ridge regression model\n",
+    "reg.fit(data['train']['X'], data['train']['y'])\n",
-    "regression_model = Ridge(alpha=0.03)\n",
+    "preds = reg.predict(data['test']['X'])\n",
    "regression_model.fit(data['train']['X'], data['train']['y'])\n",
    "preds = regression_model.predict(data['test']['X'])\n",
    "\n",
    "# Output the Mean Squared Error to the notebook and to the run\n",
    "print('Mean Squared Error is', mean_squared_error(data['test']['y'], preds))\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",
    "# Save the model to the outputs directory for capture\n",
    "joblib.dump(value=regression_model, filename='outputs/model.pkl')\n",
    "\n",
    "# Take a snapshot of the directory containing this notebook\n",
    "run.take_snapshot('./')\n",
    "\n",
    "# Complete the run\n",
    "run.complete()"
   ]
  },
@@ -183,8 +217,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "### Viewing run results\n",
+    "We can browse to the recorded run. Please make sure you use Chrome to navigate the run history page."
    "Azure Machine Learning stores all the details about the run in the Azure cloud.  Let's access those details by retrieving a link to the run using the default run output.  Clicking on the resulting link will take you to an interactive page presenting all run information."
   ]
  },
  {
@@ -201,11 +234,7 @@
   "metadata": {},
   "source": [
    "### Simple parameter sweep\n",
-    "Now let's take the same concept from above and modify the **alpha** parameter.  For each value of alpha we will create a run that will store metrics and the resulting model.  In the end we can use the captured run history to determine which model was the best for us to deploy. \n",
+    "Sweep over alpha values of a sklearn ridge model, and capture metrics and trained model in the Azure ML experiment."
    "\n",
    "Note that by using `with experiment.start_logging() as run` AML will automatically call `run.complete()` at the end of each loop.\n",
    "\n",
    "This example also uses the **tqdm** library to provide a thermometer feedback"
   ]
  },
  {
@@ -228,28 +257,24 @@
    "    # 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",
-    "        regression_model = Ridge(alpha=alpha)\n",
+    "        reg = Ridge(alpha=alpha)\n",
-    "        regression_model.fit(X=data[\"train\"][\"X\"], y=data[\"train\"][\"y\"])\n",
+    "        reg.fit(X=data[\"train\"][\"X\"], y=data[\"train\"][\"y\"])\n",
-    "        preds = regression_model.predict(X=data[\"test\"][\"X\"])\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",
-    "        # Save the model to the outputs directory for capture\n",
+    "        with open(model_name, \"wb\") as file:\n",
-    "        joblib.dump(value=regression_model, filename='outputs/model.pkl')\n",
+    "            joblib.dump(value=reg, filename=file)\n",
    "        \n",
-    "        # Capture this notebook with the run\n",
+    "        # upload the serialized model into run history record\n",
-    "        run.take_snapshot('./')\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": "markdown",
   "metadata": {},
   "source": [
    "### Viewing experiment results\n",
    "Similar to viewing the run, we can also view the entire experiment.  The experiment report view in the Azure portal lets us view all the runs in a table, and also allows us to customize charts.  This way, we can see how the alpha parameter impacts the quality of the model"
   ]
  },
  {
@@ -266,12 +291,8 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "### Select the best model \n",
+    "## Select best model from the experiment\n",
-    "Now that we've created many runs with different parameters, we need to determine which model is the best for deployment.  For this, we will iterate over the set of runs.  From each run we will take the *run id* using the `id` property, and examine the metrics by calling `run.get_metrics()`.  \n",
+    "Load all experiment run metrics recursively from the experiment into a dictionary object."
    "\n",
    "Since each run may be different, we do need to check if the run has the metric that we are looking for, in this case, **mse**.  To find the best run, we create a dictionary mapping the run id's to the metrics.\n",
    "\n",
    "Finally, we use the `tag` method to mark the best run to make it easier to find later. "
   ]
  },
  {
@@ -283,45 +304,37 @@
    "runs = {}\n",
    "run_metrics = {}\n",
    "\n",
    "# Create dictionaries containing the runs and the metrics for all runs containing the 'mse' metric\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\n",
+    "        run_metrics[r.id] = metrics"
-    "\n",
+   ]
-    "# Find the run with the best (lowest) mean squared error and display the id and metrics\n",
+  },
  {
   "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])\n",
+    "print('Metrics:', run_metrics[best_run_id])"
    "\n",
    "# Tag the best run for identification later\n",
    "best_run.tag(\"Best Run\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "---\n",
+    "You can add tags to your runs to make them easier to catalog"
    "## Deploy\n",
    "Now that we have trained a set of models and identified the run containing the best model, we want to deploy the model for real time inferencing.  The process of deploying a model involves\n",
    "* registering a model in your workspace\n",
    "* creating a scoring file containing init and run methods\n",
    "* creating an environment dependency file describing packages necessary for your scoring file\n",
    "* creating a docker image containing a properly described environment, your model, and your scoring file\n",
    "* deploying that docker image as a web service"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Register a model\n",
    "We have already identified which run contains the \"best model\" by our evaluation criteria.  Each run has a file structure associated with it that contains various files collected during the run.  Since a run can have many outputs we need to tell AML which file from those outputs represents the model that we want to use for our deployment.  We can use the `run.get_file_names()` method to list the files associated with the run, and then use the `run.register_model()` method to place the model in the workspace's model registry.\n",
    "\n",
    "When using `run.register_model()` we supply a `model_name` that is meaningful for our scenario and the `model_path` of the model relative to the run.  In this case, the model path is what is returned from `run.get_file_names()`"
   ]
  },
  {
@@ -334,19 +347,104 @@
   },
   "outputs": [],
   "source": [
-    "# View the files in the run\n",
+    "best_run.tag(key=\"Description\", value=\"The best one\")\n",
-    "for f in best_run.get_file_names():\n",
+    "best_run.get_tags()"
    "    print(f)\n",
    "    \n",
    "# Register the model with the workspace\n",
    "model = best_run.register_model(model_name='best_model', model_path='outputs/model.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "Once a model is registered, it is accessible from the list of models on the AML workspace.  If you register models with the same name multiple times, AML keeps a version history of those models for you.  The `Model.list()` lists all models in a workspace, and can be filtered by name, tags, or model properties.   "
+    "### 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."
   ]
  },
  {
@@ -359,9 +457,8 @@
   },
   "outputs": [],
   "source": [
    "# Find all models called \"best_model\" and display their version numbers\n",
    "from azureml.core.model import Model\n",
-    "models = Model.list(ws, name='best_model')\n",
+    "models = Model.list(workspace=ws, name='best_model')\n",
    "for m in models:\n",
    "    print(m.name, m.version)"
   ]
@@ -370,22 +467,54 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "### Create a scoring file\n",
+    "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."
-    "\n",
+   ]
-    "Since your model file can essentially be anything you want it to be, you need to supply a scoring script that can load your model and then apply the model to new data.  This script is your 'scoring file'.  This scoring file is a python program containing, at a minimum, two methods `init()` and `run()`.  The `init()` method is called once when your deployment is started so you can load your model and any other required objects.  This method uses the `get_model_path` function to locate the registered model inside the docker container.  The `run()` method is called interactively when the web service is called with one or more data samples to predict.\n",
+  },
-    "\n",
+  {
-    "The scoring file used for this exercise is [here](score.py).  \n"
+   "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": [
-    "### Describe your environment\n",
+    "## Scoring script\n",
    "\n",
-    "Each modelling process may require a unique set of packages.  Therefore we need to create a dependency file providing instructions to AML on how to contstruct a docker image that can support the models and any other objects required for inferencing.  In the following cell, we create a environment dependency file, *myenv.yml* that specifies which libraries are needed by the scoring script.  You can create this file manually, or use the `CondaDependencies` class to create it for you.\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",
-    "Next we use this environment file to describe the docker container that we need to create in order to deploy our model.  This container is created using our environment description and includes our scoring script."
+    "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."
   ]
  },
  {
@@ -395,33 +524,24 @@
   "outputs": [],
   "source": [
    "from azureml.core.conda_dependencies import CondaDependencies \n",
    "from azureml.core.image import ContainerImage\n",
    "\n",
-    "# Create an empty conda environment and add the scikit-learn package\n",
+    "myenv = CondaDependencies.create(conda_packages=[\"scikit-learn\"])\n",
-    "env = CondaDependencies()\n",
+    "print(myenv.serialize_to_string())\n",
    "env.add_conda_package(\"scikit-learn\")\n",
    "\n",
    "# Display the environment\n",
    "print(env.serialize_to_string())\n",
    "\n",
    "# Write the environment to disk\n",
    "with open(\"myenv.yml\",\"w\") as f:\n",
-    "    f.write(env.serialize_to_string())\n",
+    "    f.write(myenv.serialize_to_string())"
    "\n",
    "# Create a configuration object indicating how our deployment container needs to be created\n",
    "image_config = ContainerImage.image_configuration(execution_script=\"score.py\", \n",
    "                                    runtime=\"python\", \n",
    "                                    conda_file=\"myenv.yml\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "### Describe your target compute\n",
+    "## Deploy web service into an Azure Container Instance\n",
-    "In addition to the container, we also need to describe the type of compute we want to allocate for our webservice.  In in this example we are using an [Azure Container Instance](https://azure.microsoft.com/en-us/services/container-instances/) which is a good choice for quick and cost-effective dev/test deployment scenarios.  ACI instances require the number of cores you want to run and memory you need.  Tags and descriptions are available for you to identify the instances in AML when viewing the Compute tab in the AML Portal.\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",
-    "For production workloads, it is better to use [Azure Kubernentes Service (AKS)](https://azure.microsoft.com/en-us/services/kubernetes-service/) instead. Try [this notebook](11.production-deploy-to-aks.ipynb) to see how that can be done from Azure ML.\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."
   ]
  },
  {
@@ -435,7 +555,7 @@
   },
   "outputs": [],
   "source": [
-    "from azureml.core.webservice import AciWebservice\n",
+    "from azureml.core.webservice import AciWebservice, Webservice\n",
    "\n",
    "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
    "                                               memory_gb=1, \n",
@@ -447,22 +567,26 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "### Deploy your webservice\n",
+    "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",
    "The final step to deploying your webservice is to call `WebService.deploy_from_model()`.  This function uses the deployment and image configurations created above to perform the following:\n",
    "* Build a docker image\n",
    "* Deploy to the docker image to an Azure Container Instance\n",
    "* Copy your model files to the Azure Container Instance\n",
    "* Call the `init()` function in your scoring file\n",
    "* Provide an HTTP endpoint for scoring calls\n",
    "\n",
-    "The `deploy_from_model` method requires the following parameters\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."
-    "* `workspace` - the workspace containing the service\n",
+   ]
-    "* `name` - a unique named used to identify the service in the workspace\n",
+  },
-    "* `models` - an array of models to be deployed into the container\n",
+  {
-    "* `image_config` - a configuration object describing the image environment\n",
+   "cell_type": "code",
-    "* `deployment_config` - a configuration object describing the compute type\n",
+   "execution_count": null,
-    " \n",
+   "metadata": {
-    "**Note:** The web service creation can take several minutes.  "
+    "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\")"
   ]
  },
  {
@@ -477,16 +601,14 @@
   "outputs": [],
   "source": [
    "%%time\n",
-    "from azureml.core.webservice import Webservice\n",
+    "# this will take 5-10 minutes to finish\n",
-    "\n",
+    "# you can also use \"az container list\" command to find the ACI being deployed\n",
    "# Create the webservice using all of the precreated configurations and our best model\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",
    "# Wait for the service deployment to complete while displaying log output\n",
    "service.wait_for_deployment(show_output=True)"
   ]
  },
@@ -495,14 +617,28 @@
   "metadata": {},
   "source": [
    "\n",
-    "### Test your webservice"
+    "## 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": [
-    "Now that your web service is runing you can send JSON data directly to the service using the `run` method.  This cell pulls the first test sample from the original dataset into JSON and then sends it to the service."
+    "Use the `run` API to call the web service with one row of data to get a prediction."
   ]
  },
  {
@@ -517,10 +653,8 @@
   "outputs": [],
   "source": [
    "import json\n",
-    "# scrape the first row from the test set.\n",
+    "# score the first row from the test set.\n",
    "test_samples = json.dumps({\"data\": X_test[0:1, :].tolist()})\n",
    "\n",
    "#score on our service\n",
    "service.run(input_data = test_samples)"
   ]
  },
@@ -528,7 +662,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "This cell shows how you can send multiple rows to the webservice at once.  It then calculates the residuals - that is, the errors - by subtracting out the actual values from the results.  These residuals are used later to show a plotted result."
+    "Feed the entire test set and calculate the errors (residual values)."
   ]
  },
  {
@@ -553,7 +687,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "This cell shows how you can use the `service.scoring_uri` property to access the HTTP endpoint of the service and call it using standard POST operations."
+    "You can also send raw HTTP request to test the web service."
   ]
  },
  {
@@ -570,14 +704,16 @@
    "import requests\n",
    "import json\n",
    "\n",
-    "# use the first row from the test set again\n",
+    "# 2 rows of input data, each with 10 made-up numerical features\n",
-    "test_samples = json.dumps({\"data\": X_test[0:1, :].tolist()})\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",
    "# create the required header\n",
    "headers = {'Content-Type':'application/json'}\n",
    "\n",
-    "# post the request to the service and display the result\n",
+    "# for AKS deployment you'd need to the service key in the header as well\n",
-    "resp = requests.post(service.scoring_uri, test_samples, headers = headers)\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)"
   ]
  },
@@ -585,10 +721,8 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "### Residual graph\n",
+    "## Residual graph\n",
-    "One way to understand the behavior of your model is to see how the data performs against data with known results.  This cell uses matplotlib to create a histogram of the residual values, or errors, created from scoring the test samples.\n",
+    "Plot a residual value graph to chart the errors on the entire test set. Observe the nice bell curve."
    "\n",
    "A good model should have residual values that cluster around 0 - that is, no error.  Observing the resulting histogram can also show you if the model is skewed in any particular direction."
   ]
  },
  {
@@ -597,10 +731,6 @@
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "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",
@@ -623,14 +753,14 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "### Clean up"
+    "## Delete ACI to clean up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "Delete the ACI instance to stop the compute and any associated billing."
+    "Deleting ACI is super fast!"
   ]
  },
  {
@@ -647,36 +777,6 @@
    "%%time\n",
    "service.delete()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Next Steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, you created a series of models inside the notebook using local data, stored them inside an AML experiment, found the best one and deployed it as a live service!  From here you can continue to use Azure Machine Learning in this regard to run your own experiments and deploy your own models, or you can expand into further capabilities of AML!\n",
    "\n",
    "If you have a model that is difficult to process locally, either because the data is remote or the model is large, try the [train-on-remote-vm](../train-on-remote-vm) notebook to learn about submitting remote jobs.\n",
    "\n",
    "If you want to take advantage of multiple cloud machines to perform large parameter sweeps try the [train-hyperparameter-tune-deploy-with-pytorch](../../training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch\n",
    ") sample.\n",
    "\n",
    "If you want to deploy models to a production cluster try the [production-deploy-to-aks](../../deployment/production-deploy-to-aks\n",
    ") notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
@@ -686,7 +786,7 @@
   }
  ],
  "kernelspec": {
-   "display_name": "Python [Python 3.6]",
+   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
--- a/sdk-license/LICENSE.txt
+++ b/sdk-license/LICENSE.txt
@@ -1,13 +0,0 @@
 This software is made available to you on the condition that you agree to
 [your agreement][1] governing your use of Azure.
 If you do not have an existing agreement governing your use of Azure, you agree that 
 your agreement governing use of Azure is the [Microsoft Online Subscription Agreement][2]
 (which incorporates the [Online Services Terms][3]).
 By using the software you agree to these terms. This software may collect data
 that is transmitted to Microsoft. Please see the [Microsoft Privacy Statement][4]
 to learn more about how Microsoft processes personal data.
 [1]: https://azure.microsoft.com/en-us/support/legal/
 [2]: https://azure.microsoft.com/en-us/support/legal/subscription-agreement/
 [3]: http://www.microsoftvolumelicensing.com/DocumentSearch.aspx?Mode=3&DocumentTypeId=46
 [4]: http://go.microsoft.com/fwlink/?LinkId=248681 
--- a/sdk-preview-license/LICENSE.txt
+++ b/sdk-preview-license/LICENSE.txt
--- a/tutorials/img-classification-part1-training.ipynb
+++ b/tutorials/img-classification-part1-training.ipynb
@@ -64,7 +64,7 @@
   },
   "outputs": [],
   "source": [
-    "%matplotlib inline\n",
+    "%matplotlib notebook\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
@@ -129,10 +129,11 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "### Create or Attach existing AmlCompute\n",
+    "### Create remote compute target\n",
    "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
    "\n",
-    "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process."
+    "Azure Machine Learning Managed Compute(AmlCompute) is a managed service that enables data scientists to train machine learning models on clusters of Azure virtual machines, including VMs with GPU support.  In this tutorial, you create AmlCompute as your training environment. This code creates compute for you if it does not already exist in your workspace. \n",
    "\n",
    " **Creation of the compute takes approximately 5 minutes.** If the compute is already in the workspace this code uses it and skips the creation process."
   ]
  },
  {
@@ -141,7 +142,7 @@
   "metadata": {
    "tags": [
     "create mlc",
-     "amlcompute"
+     "batchai"
    ]
   },
   "outputs": [],
@@ -151,12 +152,12 @@
    "import os\n",
    "\n",
    "# choose a name for your cluster\n",
-    "compute_name = os.environ.get(\"AML_COMPUTE_CLUSTER_NAME\", \"cpucluster\")\n",
+    "compute_name = os.environ.get(\"BATCHAI_CLUSTER_NAME\", \"cpucluster\")\n",
-    "compute_min_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MIN_NODES\", 0)\n",
+    "compute_min_nodes = os.environ.get(\"BATCHAI_CLUSTER_MIN_NODES\", 0)\n",
-    "compute_max_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MAX_NODES\", 4)\n",
+    "compute_max_nodes = os.environ.get(\"BATCHAI_CLUSTER_MAX_NODES\", 4)\n",
    "\n",
    "# This example uses CPU VM. For using GPU VM, set SKU to STANDARD_NC6\n",
-    "vm_size = os.environ.get(\"AML_COMPUTE_CLUSTER_SKU\", \"STANDARD_D2_V2\")\n",
+    "vm_size = os.environ.get(\"BATCHAI_CLUSTER_SKU\", \"STANDARD_D2_V2\")\n",
    "\n",
    "\n",
    "if compute_name in ws.compute_targets:\n",
@@ -176,7 +177,7 @@
    "    # if no min node count is provided it will use the scale settings for the cluster\n",
    "    compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
    "    \n",
-    "     # For a more detailed view of current AmlCompute status, use the 'status' property    \n",
+    "     # For a more detailed view of current BatchAI cluster status, use the 'status' property    \n",
    "    print(compute_target.status.serialize())"
   ]
  },
@@ -472,12 +473,12 @@
    "\n",
    "* The name of the estimator object, `est`\n",
    "* The directory that contains your scripts. All the files in this directory are uploaded into the cluster nodes for execution. \n",
-    "* The compute target.  In this case you will use the AmlCompute you created\n",
+    "* The compute target.  In this case you will use the Batch AI cluster you created\n",
    "* The training script name, train.py\n",
    "* Parameters required from the training script \n",
    "* Python packages needed for training\n",
    "\n",
-    "In this tutorial, this target is AmlCompute. All files in the script folder are uploaded into the cluster nodes for execution. The data_folder is set to use the datastore (`ds.as_mount()`)."
+    "In this tutorial, this target is the Batch AI cluster. All files in the script folder are uploaded into the cluster nodes for execution. The data_folder is set to use the datastore (`ds.as_mount()`)."
   ]
  },
  {
@@ -519,7 +520,7 @@
   "metadata": {
    "tags": [
     "remote run",
-     "amlcompute",
+     "batchai",
     "scikit-learn"
    ]
   },
@@ -588,7 +589,7 @@
   "metadata": {
    "tags": [
     "remote run",
-     "amlcompute",
+     "batchai",
     "scikit-learn"
    ]
   },
@@ -683,7 +684,7 @@
    "\n",
    "You are ready to deploy this registered model using the instructions in the next part of the tutorial series:\n",
    "\n",
-    "> [Tutorial 2 - Deploy models](img-classification-part2-deploy.ipynb)"
+    "> [Tutorial 2 - Deploy models](02.deploy-models.ipynb)"
   ]
  }
 ],
@@ -694,9 +695,9 @@
   }
  ],
  "kernelspec": {
-   "display_name": "Python 3.6",
+   "display_name": "Python 3",
   "language": "python",
-   "name": "python36"
+   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
--- a/tutorials/img-classification-part2-deploy.ipynb
+++ b/tutorials/img-classification-part2-deploy.ipynb
@@ -15,7 +15,7 @@
   "source": [
    "# Tutorial #2:  Deploy an image classification model in Azure Container Instance (ACI)\n",
    "\n",
-    "This tutorial is **part two of a two-part tutorial series**. In the [previous tutorial](img-classification-part1-training.ipynb), you trained machine learning models and then registered a model in your workspace on the cloud.  \n",
+    "This tutorial is **part two of a two-part tutorial series**. In the [previous tutorial](01.train-models.ipynb), you trained machine learning models and then registered a model in your workspace on the cloud.  \n",
    "\n",
    "Now, you're ready to deploy the model as a web service in [Azure Container Instances](https://docs.microsoft.com/azure/container-instances/) (ACI). A web service is an image, in this case a Docker image, that encapsulates the scoring logic and the model itself. \n",
    "\n",
@@ -97,7 +97,7 @@
   },
   "outputs": [],
   "source": [
-    "%matplotlib inline\n",
+    "%matplotlib notebook\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
--- a/tutorials/regression-part1-data-prep.ipynb
+++ b/tutorials/regression-part1-data-prep.ipynb
@@ -13,25 +13,31 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# Tutorial #1: Prepare data for regression modeling"
+    "# Tutorial: Use Azure DataPrep SDK to prepare data for machine learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "In this tutorial, you learn how to prep data for regression modeling using the Azure Machine Learning Data Prep SDK. Perform various transformations to filter and combine two different NYC Taxi data sets. The end goal of this tutorial set is to predict the cost of a taxi trip by training a model on data features including pickup hour, day of week, number of passengers, and coordinates. This tutorial is part one of a two-part tutorial series.\n",
+    "Prepare data for use as a training data set in a machine learning model with the Azure DataPrep SDK. Perform various transformations to filter and combine two different NYC Taxi data sets. Learn some of the unique features of the DataPrep SDK: \n",
    "\n",
-    "In this tutorial, you:\n",
+    "* Transform data from derived examples \n",
    "* Infer field type from data \n",
    "\n",
-    "\n",
+    "This tutorial is part one of a two-part tutorial series."
-    "> * Setup a Python environment and import packages\n",
+   ]
-    "> * Load two datasets with different field names\n",
+  },
-    "> * Cleanse data to remove anomalies\n",
+  {
-    "> * Transform data using intelligent transforms to create new features\n",
+   "cell_type": "markdown",
-    "> * Save your dataflow object to use in a regression model\n",
+   "metadata": {},
-    "\n",
+   "source": [
-    "You can prepare your data in Python using the [Azure Machine Learning Data Prep SDK](https://aka.ms/data-prep-sdk)."
+    "In this tutorial you:\n",
    "* Load two datasets with different field names \n",
    "* Cleanse the data  \n",
    "* Use smart transforms to predict your logic based on an example\n",
    "* Use automated feature engineering to build dynamic fields \n",
    "* Merge the two datasets to use for your machine learning training \n"
   ]
  },
  {
@@ -39,7 +45,7 @@
   "metadata": {},
   "source": [
    "## Import packages\n",
-    "Begin by importing the SDK."
+    "Begin by importing the Azure DataPrep SDK."
   ]
  },
  {
@@ -56,7 +62,7 @@
   "metadata": {},
   "source": [
    "## Load data\n",
-    "Download two different NYC Taxi data sets into dataflow objects. These datasets contain slightly different fields. The method `auto_read_file()` automatically recognizes the input file type."
+    "Download two different NYC Taxi data sets into dataflow objects.  These datasets contain slightly different fields. The method `auto_read_file()` automatically recognizes the input file type."
   ]
  },
  {
@@ -71,18 +77,17 @@
    "yellow_path = \"/\".join([dataset_root, \"yellow-small/*\"])\n",
    "\n",
    "green_df = dprep.read_csv(path=green_path, header=dprep.PromoteHeadersMode.GROUPED)\n",
    "# auto_read_file will automatically identify and parse the file type, and is useful if you don't know the file type\n",
    "yellow_df = dprep.auto_read_file(path=yellow_path)\n",
    "\n",
-    "green_df.head(5)\n",
+    "display(green_df.head(5))\n",
-    "yellow_df.head(5)"
+    "display(yellow_df.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "## Cleanse data"
+    "## Data transformation"
   ]
  },
  {
@@ -212,7 +217,7 @@
   "outputs": [],
   "source": [
    "yellow_df = tmp_df\n",
-    "combined_df = green_df.append_rows([yellow_df])"
+    "combined_df = green_df.append_rows(other_activities=[yellow_df])"
   ]
  },
  {
@@ -400,7 +405,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "## Transform data"
+    "## Feature engineering"
   ]
  },
  {
@@ -476,7 +481,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "Use the type inference functionality to automatically check the data type of each field, and display the inference results."
+    "Use the type inference functionality to automatically check the data type of each field, and display the inference results using `inference_info()`."
   ]
  },
  {
@@ -487,7 +492,7 @@
   "source": [
    "type_infer = tmp_df.builders.set_column_types()\n",
    "type_infer.learn()\n",
-    "type_infer"
+    "type_infer.inference_info"
   ]
  },
  {
@@ -522,45 +527,7 @@
   "source": [
    "dflow_prepared = tmp_df\n",
    "package = dprep.Package([dflow_prepared])\n",
-    "package.save(\".\\dflows\")"
+    "package.save(\".\\dflow\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean up resources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Delete the file `dflows` (whether you are running locally or in Azure Notebooks) in your current directory if you do not wish to continue with part two of the tutorial. If you continue on to part two, you will need the `dflows` file in the current directory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this Azure Machine Learning Data Prep SDK tutorial, you:\n",
    "\n",
    "> * Set up your development environment\n",
    "> * Loaded and cleansed data sets\n",
    "> * Used smart transforms to predict your logic based on an example\n",
    "> * Merged and packaged datasets for machine learning training\n",
    "\n",
    "You are ready to use this training data in the next part of the tutorial series:\n",
    "\n",
    "\n",
    "> [Tutorial #2: Train regression model](regression-part2-automated-ml.ipynb)"
   ]
  }
 ],
--- a/tutorials/regression-part2-automated-ml.ipynb
+++ b/tutorials/regression-part2-automated-ml.ipynb
@@ -1,467 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial #2: Train a regression model with automated machine learning\n",
    "\n",
    "This tutorial is **part two of a two-part tutorial series**. In the previous tutorial, you [prepared the NYC taxi data for regression modeling](regression-part1-data-prep.ipynb).\n",
    "\n",
    "Now, you're ready to start building your model with Azure Machine Learning service. In this part of the tutorial, you will use the prepared data and automatically generate a regression model to predict taxi fare prices. Using the automated ML capabilities of the service, you define your machine learning goals and constraints, launch the automated machine learning process and then allow the algorithm selection and hyperparameter-tuning to happen for you. The automated ML technique iterates over many combinations of algorithms and hyperparameters until it finds the best model based on your criterion.\n",
    "\n",
    "In this tutorial, you learn how to:\n",
    "\n",
    "> * Setup a Python environment and import the SDK packages\n",
    "> * Configure an Azure Machine Learning service workspace\n",
    "> * Auto-train a regression model \n",
    "> * Run the model locally with custom parameters\n",
    "> * Explore the results\n",
    "> * Register the best model\n",
    "\n",
    "If you don’t have an Azure subscription, create a [free account](https://aka.ms/AMLfree) before you begin. \n",
    "\n",
    "> Code in this article was tested with Azure Machine Learning SDK version 1.0.0\n",
    "\n",
    "\n",
    "## Prerequisites\n",
    "\n",
    "> * [Run the data preparation tutorial](regression-part1-data-prep.ipynb)\n",
    "\n",
    "> * Automated machine learning configured environment e.g. Azure notebooks, Local Python environment or Data Science Virtual Machine. [Setup](https://docs.microsoft.com/azure/machine-learning/service/samples-notebooks) automated machine learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import packages\n",
    "Import Python packages you need in this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.core\n",
    "import pandas as pd\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl.run import AutoMLRun\n",
    "import time\n",
    "import logging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configure workspace\n",
    "\n",
    "Create a workspace object from the existing workspace. A `Workspace` is a class that accepts your Azure subscription and resource information, and creates a cloud resource to monitor and track your model runs. `Workspace.from_config()` reads the file **aml_config/config.json** and loads the details into an object named `ws`.  `ws` is used throughout the rest of the code in this tutorial.\n",
    "\n",
    "Once you have a workspace object, specify a name for the experiment and create and register a local directory with the workspace. The history of all runs is recorded under the specified experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "# choose a name for the run history container in the workspace\n",
    "experiment_name = 'automated-ml-regression'\n",
    "# project folder\n",
    "project_folder = './automated-ml-regression'\n",
    "\n",
    "import os\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore data\n",
    "\n",
    "Utilize the data flow object created in the previous tutorial. Open and execute the data flow and review the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.dataprep as dprep\n",
    "package_saved = dprep.Package.open(\".\\dflow\")\n",
    "dflow_prepared = package_saved.dataflows[0]\n",
    "dflow_prepared.get_profile()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You prepare the data for the experiment by adding columns to `dflow_X` to be features for our model creation. You define `dflow_y` to be our prediction value; cost.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dflow_X = dflow_prepared.keep_columns(['pickup_weekday', 'dropoff_latitude', 'dropoff_longitude','pickup_hour','pickup_longitude','pickup_latitude','passengers'])\n",
    "dflow_y = dflow_prepared.keep_columns('cost')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split data into train and test sets\n",
    "\n",
    "Now you split the data into training and test sets using the `train_test_split` function in the `sklearn` library. This function segregates the data into the x (features) data set for model training and the y (values to predict) data set for testing. The `test_size` parameter determines the percentage of data to allocate to testing. The `random_state` parameter sets a seed to the random generator, so that your train-test splits are always deterministic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "x_df = dflow_X.to_pandas_dataframe()\n",
    "y_df = dflow_y.to_pandas_dataframe()\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_df, y_df, test_size=0.2, random_state=123)\n",
    "# flatten y_train to 1d array\n",
    "y_train.values.flatten()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You now have the necessary packages and data ready for auto training for your model. \n",
    "\n",
    "## Automatically train a model\n",
    "\n",
    "To automatically train a model:\n",
    "1. Define settings for the experiment run\n",
    "1. Submit the experiment for model tuning\n",
    "\n",
    "\n",
    "### Define settings for autogeneration and tuning\n",
    "\n",
    "Define the experiment parameters and models settings for autogeneration and tuning. View the full list of [settings](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train).\n",
    "\n",
    "\n",
    "|Property| Value in this tutorial |Description|\n",
    "|----|----|---|\n",
    "|**iteration_timeout_minutes**|10|Time limit in minutes for each iteration|\n",
    "|**iterations**|30|Number of iterations. In each iteration, the model trains with the data with a specific pipeline|\n",
    "|**primary_metric**|spearman_correlation | Metric that you want to optimize.|\n",
    "|**preprocess**| True | True enables experiment to perform preprocessing on the input.|\n",
    "|**verbosity**| logging.INFO | Controls the level of logging.|\n",
    "|**n_cross_validationss**|5|Number of cross validation splits\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_settings = {\n",
    "    \"iteration_timeout_minutes\" : 10,\n",
    "    \"iterations\" : 30,\n",
    "    \"primary_metric\" : 'spearman_correlation',\n",
    "    \"preprocess\" : True,\n",
    "    \"verbosity\" : logging.INFO,\n",
    "    \"n_cross_validations\": 5\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "configure automl"
    ]
   },
   "outputs": [],
   "source": [
    "from azureml.train.automl import AutoMLConfig\n",
    "\n",
    "# local compute \n",
    "automated_ml_config = AutoMLConfig(task = 'regression',\n",
    "                             debug_log = 'automated_ml_errors.log',\n",
    "                             path = project_folder,\n",
    "                             X = x_train.values,\n",
    "                             y = y_train.values.flatten(),\n",
    "                             **automl_settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the automatic regression model\n",
    "\n",
    "Start the experiment to run locally. Pass the defined `automated_ml_config` object to the experiment, and set the output to `true` to view progress during the experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "local submitted run",
     "automl"
    ]
   },
   "outputs": [],
   "source": [
    "from azureml.core.experiment import Experiment\n",
    "experiment=Experiment(ws, experiment_name)\n",
    "local_run = experiment.submit(automated_ml_config, show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the results\n",
    "\n",
    "Explore the results of automatic training with a Jupyter widget or by examining the experiment history.\n",
    "\n",
    "### Option 1: Add a Jupyter widget to see results\n",
    "\n",
    "Use the Jupyter notebook widget to see a graph and a table of all results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "use notebook widget"
    ]
   },
   "outputs": [],
   "source": [
    "from azureml.widgets import RunDetails\n",
    "RunDetails(local_run).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Option 2: Get and examine all run iterations in Python\n",
    "\n",
    "Alternatively, you can retrieve the history of each experiment and explore the individual metrics for each iteration run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "get metrics",
     "query history"
    ]
   },
   "outputs": [],
   "source": [
    "children = list(local_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "\n",
    "import pandas as pd\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieve the best model\n",
    "\n",
    "Select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last fit invocation. There are overloads on `get_output` that allow you to retrieve the best run and fitted model for any logged metric or a particular iteration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = local_run.get_output()\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Register the model\n",
    "\n",
    "Register the model in your Azure Machine Learning Workspace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "description = 'Automated Machine Learning Model'\n",
    "tags = None\n",
    "local_run.register_model(description=description, tags=tags)\n",
    "local_run.model_id # Use this id to deploy the model as a web service in Azure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test the best model accuracy\n",
    "\n",
    "Use the best model to run predictions on the test data set. The function `predict` uses the best model, and predicts the values of y (trip cost) from the `x_test` data set. Print the first 10 predicted cost values from `y_predict`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = fitted_model.predict(x_test.values) \n",
    "print(y_predict[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare the predicted cost values with the actual cost values. Use the `y_test` dataframe, and convert it to a list to compare to the predicted values. The function `mean_absolute_error` takes two arrays of values, and calculates the average absolute value error between them. In this example, a mean absolute error of 3.5 would mean that on average, the model predicts the cost within plus or minus 3.5 of the actual value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "y_actual = y_test.values.flatten().tolist()\n",
    "mean_absolute_error(y_actual, y_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following code to calculate MAPE (mean absolute percent error) using the full `y_actual` and `y_predict` data sets. This metric calculates an absolute difference between each predicted and actual value, sums all the differences, and then expresses that sum as a percent of the total of the actual values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum_actuals = sum_errors = 0\n",
    "\n",
    "for actual_val, predict_val in zip(y_actual, y_predict):\n",
    "    abs_error = actual_val - predict_val\n",
    "    if abs_error < 0:\n",
    "        abs_error = abs_error * -1\n",
    "    \n",
    "    sum_errors = sum_errors + abs_error\n",
    "    sum_actuals = sum_actuals + actual_val\n",
    "    \n",
    "mean_abs_percent_error = sum_errors / sum_actuals\n",
    "print(\"Model MAPE:\")\n",
    "print(mean_abs_percent_error)\n",
    "print()\n",
    "print(\"Model Accuracy:\")\n",
    "print(1 - mean_abs_percent_error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next steps\n",
    "\n",
    "In this automated machine learning tutorial, you:\n",
    "\n",
    "\n",
    "> * Configured a workspace and prepared data for an experiment\n",
    "> * Trained using an automated regression model locally with custom parameters\n",
    "> * Explored and reviewed training results\n",
    "> * Registered the best model\n",
    "\n",
    "[Deploy your model](02.deploy-models.ipynb) with Azure Machine Learning."
   ]
  }
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Author	SHA1	Message	Date
rastala	ce59d79133	Update 12/2 2	2018-12-02 19:21:15 -05:00
rastala	13a5d0baac	Update 12/2	2018-12-02 14:46:52 -05:00
rastala	74309f91f7	Update 12-1-19	2018-12-01 20:37:45 -05:00
Roope Astala	96523ec751	Notebook folder restructuring	2018-11-29 11:32:16 -05:00