update samples - test

update samples from Release-158 as a part of 1.0.74 SDK release

NBSETUP.md

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

README.md

@@ -1,68 +1,76 @@
-# Azure Machine Learning service example notebooks
-
-This repository contains example notebooks demonstrating the [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning-service/) Python SDK which allows you to build, train, deploy and manage machine learning solutions using Azure.  The AML SDK allows you the choice of using local or cloud compute resources, while managing and maintaining the complete data science workflow from the cloud.
-
-![Azure ML workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/service/media/overview-what-is-azure-ml/aml.png)
-
-## Quick installation
-```sh
-pip install azureml-sdk
-```
-Read more detailed instructions on [how to set up your environment](./NBSETUP.md) using Azure Notebook service, your own Jupyter notebook server, or Docker.
-
-## How to navigate and use the example notebooks?
-If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, you should always run the [Configuration](./configuration.ipynb) notebook first when setting up a notebook library on a new machine or in a new environment. It configures your notebook library to connect to an Azure Machine Learning workspace, and sets up your workspace and compute to be used by many of the other examples. 
-
-If you want to...
-
- * ...try out and explore Azure ML, start with image classification tutorials: [Part 1 (Training)](./tutorials/img-classification-part1-training.ipynb) and [Part 2 (Deployment)](./tutorials/img-classification-part2-deploy.ipynb).
- * ...prepare your data and do automated machine learning, start with regression tutorials: [Part 1 (Data Prep)](./tutorials/regression-part1-data-prep.ipynb) and [Part 2 (Automated ML)](./tutorials/regression-part2-automated-ml.ipynb).
- * ...learn about experimentation and tracking run history, first [train within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then try [training on remote VM](./how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb) and [using logging APIs](./how-to-use-azureml/training/logging-api/logging-api.ipynb).
- * ...train deep learning models at scale, first learn about [Machine Learning Compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and then try [distributed hyperparameter tuning](./how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) and [distributed training](./how-to-use-azureml/training-with-deep-learning/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb).
- * ...deploy models as a realtime scoring service, first learn the basics by [training within Notebook and deploying to Azure Container Instance](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then learn how to [register and manage models, and create Docker images](./how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb), and [production deploy models on Azure Kubernetes Cluster](./how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb).
- * ...deploy models as a batch scoring service, first [train a model within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), learn how to [register and manage models](./how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb), then [create Machine Learning Compute for scoring compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and [use Machine Learning Pipelines to deploy your model](https://aka.ms/pl-batch-scoring).
- * ...monitor your deployed models, learn about using [App Insights](./how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb) and [model data collection](./how-to-use-azureml/deployment/enable-data-collection-for-models-in-aks/enable-data-collection-for-models-in-aks.ipynb).
-
-## Tutorials
-
-The [Tutorials](./tutorials) folder contains notebooks for the tutorials described in the [Azure Machine Learning documentation](https://aka.ms/aml-docs).
-  
-## How to use Azure ML
-
-The [How to use Azure ML](./how-to-use-azureml) folder contains specific examples demonstrating the features of the Azure Machine Learning SDK
-
-- [Training](./how-to-use-azureml/training) - Examples of how to build models using Azure ML's logging and execution capabilities on local and remote compute targets
-- [Training with Deep Learning](./how-to-use-azureml/training-with-deep-learning) - Examples demonstrating how to build deep learning models using estimators and parameter sweeps
-- [Manage Azure ML Service](./how-to-use-azureml/manage-azureml-service) - Examples how to perform tasks, such as authenticate against Azure ML service in different ways.
-- [Automated Machine Learning](./how-to-use-azureml/automated-machine-learning) - Examples using Automated Machine Learning to automatically generate optimal machine learning pipelines and models
-- [Machine Learning Pipelines](./how-to-use-azureml/machine-learning-pipelines) - Examples showing how to create and use reusable pipelines for training and batch scoring
-- [Deployment](./how-to-use-azureml/deployment) - Examples showing how to deploy and manage machine learning models and solutions
-- [Azure Databricks](./how-to-use-azureml/azure-databricks) - Examples showing how to use Azure ML with Azure Databricks
-
----
-## Documentation
-
- * Quickstarts, end-to-end tutorials, and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/).
- * [Python SDK reference](https://docs.microsoft.com/en-us/python/api/overview/azure/ml/intro?view=azure-ml-py)
- * Azure ML Data Prep SDK [overview](https://aka.ms/data-prep-sdk), [Python SDK reference](https://aka.ms/aml-data-prep-apiref), and [tutorials and how-tos](https://aka.ms/aml-data-prep-notebooks).
-
----
-
-## Projects using Azure Machine Learning
-
-Visit following repos to see projects contributed by Azure ML users:
-
- - [Fine tune natural language processing models using Azure Machine Learning service](https://github.com/Microsoft/AzureML-BERT)
- - [Fashion MNIST with Azure ML SDK](https://github.com/amynic/azureml-sdk-fashion)
- 
-## Data/Telemetry 
-This repository collects usage data and sends it to Mircosoft to help improve our products and services. Read Microsoft's [privacy statement to learn more](https://privacy.microsoft.com/en-US/privacystatement)
-
-To opt out of tracking, please go to the raw markdown or .ipynb files and remove the following line of code:
-
-```sh
-    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/README.png)"
-```
-This URL will be slightly different depending on the file. 
-
- ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/README.png)
+# Azure Machine Learning service example notebooks
+
+This repository contains example notebooks demonstrating the [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning-service/) Python SDK which allows you to build, train, deploy and manage machine learning solutions using Azure.  The AML SDK allows you the choice of using local or cloud compute resources, while managing and maintaining the complete data science workflow from the cloud.
+
+![Azure ML Workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/service/media/concept-azure-machine-learning-architecture/workflow.png)
+
+
+## Quick installation
+```sh
+pip install azureml-sdk
+```
+Read more detailed instructions on [how to set up your environment](./NBSETUP.md) using Azure Notebook service, your own Jupyter notebook server, or Docker.
+
+## How to navigate and use the example notebooks?
+If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, you should always run the [Configuration](./configuration.ipynb) notebook first when setting up a notebook library on a new machine or in a new environment. It configures your notebook library to connect to an Azure Machine Learning workspace, and sets up your workspace and compute to be used by many of the other examples. 
+This [index](.index.md) should assist in navigating the Azure Machine Learning notebook samples and encourage efficient retrieval of topics and content. 
+
+If you want to...
+
+ * ...try out and explore Azure ML, start with image classification tutorials: [Part 1 (Training)](./tutorials/img-classification-part1-training.ipynb) and [Part 2 (Deployment)](./tutorials/img-classification-part2-deploy.ipynb).
+ * ...learn about experimentation and tracking run history, first [train within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then try [training on remote VM](./how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb) and [using logging APIs](./how-to-use-azureml/training/logging-api/logging-api.ipynb).
+ * ...train deep learning models at scale, first learn about [Machine Learning Compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and then try [distributed hyperparameter tuning](./how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) and [distributed training](./how-to-use-azureml/training-with-deep-learning/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb).
+ * ...deploy models as a realtime scoring service, first learn the basics by [training within Notebook and deploying to Azure Container Instance](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then learn how to [register and manage models, and create Docker images](./how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb), and [production deploy models on Azure Kubernetes Cluster](./how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb).
+ * ...deploy models as a batch scoring service, first [train a model within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), learn how to [register and manage models](./how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb), then [create Machine Learning Compute for scoring compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and [use Machine Learning Pipelines to deploy your model](https://aka.ms/pl-batch-scoring).
+ * ...monitor your deployed models, learn about using [App Insights](./how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb).
+
+## Tutorials
+
+The [Tutorials](./tutorials) folder contains notebooks for the tutorials described in the [Azure Machine Learning documentation](https://aka.ms/aml-docs).
+  
+## How to use Azure ML
+
+The [How to use Azure ML](./how-to-use-azureml) folder contains specific examples demonstrating the features of the Azure Machine Learning SDK
+
+- [Training](./how-to-use-azureml/training) - Examples of how to build models using Azure ML's logging and execution capabilities on local and remote compute targets
+- [Training with Deep Learning](./how-to-use-azureml/training-with-deep-learning) - Examples demonstrating how to build deep learning models using estimators and parameter sweeps
+- [Manage Azure ML Service](./how-to-use-azureml/manage-azureml-service) - Examples how to perform tasks, such as authenticate against Azure ML service in different ways.
+- [Automated Machine Learning](./how-to-use-azureml/automated-machine-learning) - Examples using Automated Machine Learning to automatically generate optimal machine learning pipelines and models
+- [Machine Learning Pipelines](./how-to-use-azureml/machine-learning-pipelines) - Examples showing how to create and use reusable pipelines for training and batch scoring
+- [Deployment](./how-to-use-azureml/deployment) - Examples showing how to deploy and manage machine learning models and solutions
+- [Azure Databricks](./how-to-use-azureml/azure-databricks) - Examples showing how to use Azure ML with Azure Databricks
+- [Monitor Models](./how-to-use-azureml/monitor-models) - Examples showing how to enable model monitoring services such as DataDrift
+
+---
+## Documentation
+
+ * Quickstarts, end-to-end tutorials, and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/).
+ * [Python SDK reference](https://docs.microsoft.com/en-us/python/api/overview/azure/ml/intro?view=azure-ml-py)
+ * Azure ML Data Prep SDK [overview](https://aka.ms/data-prep-sdk), [Python SDK reference](https://aka.ms/aml-data-prep-apiref), and [tutorials and how-tos](https://aka.ms/aml-data-prep-notebooks).
+
+---
+
+
+## Community Repository 
+Visit this [community repository](https://github.com/microsoft/MLOps/tree/master/examples) to find useful end-to-end sample notebooks. Also, please follow these [contribution guidelines](https://github.com/microsoft/MLOps/blob/master/contributing.md) when contributing to this repository.   
+
+## Projects using Azure Machine Learning
+
+Visit following repos to see projects contributed by Azure ML users:
+ - [AMLSamples](https://github.com/Azure/AMLSamples) Number of end-to-end examples, including face recognition, predictive maintenance, customer churn and sentiment analysis.
+ - [Learn about Natural Language Processing best practices using Azure Machine Learning service](https://github.com/microsoft/nlp)
+ - [Pre-Train BERT models using Azure Machine Learning service](https://github.com/Microsoft/AzureML-BERT)
+ - [Fashion MNIST with Azure ML SDK](https://github.com/amynic/azureml-sdk-fashion)
+ - [UMass Amherst Student Samples](https://github.com/katiehouse3/microsoft-azure-ml-notebooks) - A number of end-to-end machine learning notebooks, including machine translation, image classification, and customer churn, created by students in the 696DS course at UMass Amherst.
+ 
+## Data/Telemetry 
+This repository collects usage data and sends it to Mircosoft to help improve our products and services. Read Microsoft's [privacy statement to learn more](https://privacy.microsoft.com/en-US/privacystatement)
+
+To opt out of tracking, please go to the raw markdown or .ipynb files and remove the following line of code:
+
+```sh
+    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/README.png)"
+```
+This URL will be slightly different depending on the file. 
+
+ ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/README.png)

configuration.ipynb

@@ -1,383 +1,387 @@
-{
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "name": "python36",
-      "language": "python"
-    },
-    "authors": [
-      {
-        "name": "roastala"
-      }
-    ],
-    "language_info": {
-      "mimetype": "text/x-python",
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "pygments_lexer": "ipython3",
-      "name": "python",
-      "file_extension": ".py",
-      "nbconvert_exporter": "python",
-      "version": "3.6.5"
-    }
-  },
-  "nbformat": 4,
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/configuration.png)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Configuration\n",
-        "\n",
-        "_**Setting up your Azure Machine Learning services workspace and configuring your notebook library**_\n",
-        "\n",
-        "---\n",
-        "---\n",
-        "\n",
-        "## Table of Contents\n",
-        "\n",
-        "1. [Introduction](#Introduction)\n",
-        "    1. What is an Azure Machine Learning workspace\n",
-        "1. [Setup](#Setup)\n",
-        "    1. Azure subscription\n",
-        "    1. Azure ML SDK and other library installation\n",
-        "    1. Azure Container Instance registration\n",
-        "1. [Configure your Azure ML Workspace](#Configure%20your%20Azure%20ML%20workspace)\n",
-        "    1. Workspace parameters\n",
-        "    1. Access your workspace\n",
-        "    1. Create a new workspace\n",
-        "    1. Create compute resources\n",
-        "1. [Next steps](#Next%20steps)\n",
-        "\n",
-        "---\n",
-        "\n",
-        "## Introduction\n",
-        "\n",
-        "This notebook configures your library of notebooks to connect to an Azure Machine Learning (ML) workspace.  In this case, a library contains all of the notebooks in the current folder and any nested folders.  You can configure this notebook library to use an existing workspace or create a new workspace.\n",
-        "\n",
-        "Typically you will need to run this notebook only once per notebook library as all other notebooks will use connection information that is written here.  If you want to redirect your notebook library to work with a different workspace, then you should re-run this notebook.\n",
-        "\n",
-        "In this notebook you will\n",
-        "* Learn about getting an Azure subscription\n",
-        "* Specify your workspace parameters\n",
-        "* Access or create your workspace\n",
-        "* Add a default compute cluster for your workspace\n",
-        "\n",
-        "### What is an Azure Machine Learning workspace\n",
-        "\n",
-        "An Azure ML Workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an Azure ML Workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, deployment, inference, and the monitoring of deployed models."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Setup\n",
-        "\n",
-        "This section describes activities required before you can access any Azure ML services functionality."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### 1. Azure Subscription\n",
-        "\n",
-        "In order to create an Azure ML Workspace, first you need access to an Azure subscription.  An Azure subscription allows you to manage storage, compute, and other assets in the Azure cloud.  You can [create a new subscription](https://azure.microsoft.com/en-us/free/) or access existing subscription information from the [Azure portal](https://portal.azure.com).  Later in this notebook you will need information such as your subscription ID in order to create and access AML workspaces.\n",
-        "\n",
-        "### 2. Azure ML SDK and other library installation\n",
-        "\n",
-        "If you are running in your own environment, follow [SDK installation instructions](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment).  If you are running in Azure Notebooks or another Microsoft managed environment, the SDK is already installed.\n",
-        "\n",
-        "Also install following libraries to your environment. Many of the example notebooks depend on them\n",
-        "\n",
-        "```\n",
-        "(myenv) $ conda install -y matplotlib tqdm scikit-learn\n",
-        "```\n",
-        "\n",
-        "Once installation is complete, the following cell checks the Azure ML SDK version:"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {
-        "tags": [
-          "install"
-        ]
-      },
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "import azureml.core\n",
-        "\n",
-        "print(\"This notebook was created using version 1.0.48.post1 of the Azure ML SDK\")\n",
-        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "If you are using an older version of the SDK then this notebook was created using, you should upgrade your SDK.\n",
-        "\n",
-        "### 3. Azure Container Instance registration\n",
-        "Azure Machine Learning uses of [Azure Container Instance (ACI)](https://azure.microsoft.com/services/container-instances) to deploy dev/test web services. An Azure subscription needs to be registered to use ACI.  If you or the subscription owner have not yet registered ACI on your subscription, you will need to use the [Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest) and execute the following commands.  Note that if you ran through the AML [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) you have already registered ACI. \n",
-        "\n",
-        "```shell\n",
-        "# check to see if ACI is already registered\n",
-        "(myenv) $ az provider show -n Microsoft.ContainerInstance -o table\n",
-        "\n",
-        "# if ACI is not registered, run this command.\n",
-        "# note you need to be the subscription owner in order to execute this command successfully.\n",
-        "(myenv) $ az provider register -n Microsoft.ContainerInstance\n",
-        "```\n",
-        "\n",
-        "---"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Configure your Azure ML workspace\n",
-        "\n",
-        "### Workspace parameters\n",
-        "\n",
-        "To use an AML Workspace, you will need to import the Azure ML SDK and supply the following information:\n",
-        "* Your subscription id\n",
-        "* A resource group name\n",
-        "* (optional) The region that will host your workspace\n",
-        "* A name for your workspace\n",
-        "\n",
-        "You can get your subscription ID from the [Azure portal](https://portal.azure.com).\n",
-        "\n",
-        "You will also need access to a [_resource group_](https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-group-overview#resource-groups), which organizes Azure resources and provides a default region for the resources in a group.  You can see what resource groups to which you have access, or create a new one in the [Azure portal](https://portal.azure.com).  If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n",
-        "\n",
-        "The region to host your workspace will be used if you are creating a new workspace.  You do not need to specify this if you are using an existing workspace. You can find the list of supported regions [here](https://azure.microsoft.com/en-us/global-infrastructure/services/?products=machine-learning-service).  You should pick a region that is close to your location or that contains your data.\n",
-        "\n",
-        "The name for your workspace is unique within the subscription and should be descriptive enough to discern among other AML Workspaces.  The subscription may be used only by you, or it may be used by your department or your entire enterprise, so choose a name that makes sense for your situation.\n",
-        "\n",
-        "The following cell allows you to specify your workspace parameters.  This cell uses the python method `os.getenv` to read values from environment variables which is useful for automation.  If no environment variable exists, the parameters will be set to the specified default values.  \n",
-        "\n",
-        "If you ran the Azure Machine Learning [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) in Azure Notebooks, you already have a configured workspace!  You can go to your Azure Machine Learning Getting Started library, view *config.json* file, and copy-paste the values for subscription ID, resource group and workspace name below.\n",
-        "\n",
-        "Replace the default values in the cell below with your workspace parameters"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "import os\n",
-        "\n",
-        "subscription_id = os.getenv(\"SUBSCRIPTION_ID\", default=\"<my-subscription-id>\")\n",
-        "resource_group = os.getenv(\"RESOURCE_GROUP\", default=\"<my-resource-group>\")\n",
-        "workspace_name = os.getenv(\"WORKSPACE_NAME\", default=\"<my-workspace-name>\")\n",
-        "workspace_region = os.getenv(\"WORKSPACE_REGION\", default=\"eastus2\")"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Access your workspace\n",
-        "\n",
-        "The following cell uses the Azure ML SDK to attempt to load the workspace specified by your parameters.  If this cell succeeds, your notebook library will be configured to access the workspace from all notebooks using the `Workspace.from_config()` method.  The cell can fail if the specified workspace doesn't exist or you don't have permissions to access it. "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "try:\n",
-        "    ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name)\n",
-        "    # write the details of the workspace to a configuration file to the notebook library\n",
-        "    ws.write_config()\n",
-        "    print(\"Workspace configuration succeeded. Skip the workspace creation steps below\")\n",
-        "except:\n",
-        "    print(\"Workspace not accessible. Change your parameters or create a new workspace below\")"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create a new workspace\n",
-        "\n",
-        "If you don't have an existing workspace and are the owner of the subscription or resource group, you can create a new workspace.  If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n",
-        "\n",
-        "**Note**: As with other Azure services, there are limits on certain resources (for example AmlCompute quota) associated with the Azure ML service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n",
-        "\n",
-        "This cell will create an Azure ML workspace for you in a subscription provided you have the correct permissions.\n",
-        "\n",
-        "This will fail if:\n",
-        "* You do not have permission to create a workspace in the resource group\n",
-        "* You do not have permission to create a resource group if it's non-existing.\n",
-        "* You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription\n",
-        "\n",
-        "If workspace creation fails, please work with your IT admin to provide you with the appropriate permissions or to provision the required resources."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {
-        "tags": [
-          "create workspace"
-        ]
-      },
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "# Create the workspace using the specified parameters\n",
-        "ws = Workspace.create(name = workspace_name,\n",
-        "                      subscription_id = subscription_id,\n",
-        "                      resource_group = resource_group, \n",
-        "                      location = workspace_region,\n",
-        "                      create_resource_group = True,\n",
-        "                      exist_ok = True)\n",
-        "ws.get_details()\n",
-        "\n",
-        "# write the details of the workspace to a configuration file to the notebook library\n",
-        "ws.write_config()"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create compute resources for your training experiments\n",
-        "\n",
-        "Many of the sample notebooks use Azure ML managed compute (AmlCompute) to train models using a dynamically scalable pool of compute. In this section you will create default compute clusters for use by the other notebooks and any other operations you choose.\n",
-        "\n",
-        "To create a cluster, you need to specify a compute configuration that specifies the type of machine to be used and the scalability behaviors.  Then you choose a name for the cluster that is unique within the workspace that can be used to address the cluster later.\n",
-        "\n",
-        "The cluster parameters are:\n",
-        "* vm_size - this describes the virtual machine type and size used in the cluster.  All machines in the cluster are the same type.  You can get the list of vm sizes available in your region by using the CLI command\n",
-        "\n",
-        "```shell\n",
-        "az vm list-skus -o tsv\n",
-        "```\n",
-        "* min_nodes - this sets the minimum size of the cluster.  If you set the minimum to 0 the cluster will shut down all nodes while not in use.  Setting this number to a value higher than 0 will allow for faster start-up times, but you will also be billed when the cluster is not in use.\n",
-        "* max_nodes - this sets the maximum size of the cluster.  Setting this to a larger number allows for more concurrency and a greater distributed processing of scale-out jobs.\n",
-        "\n",
-        "\n",
-        "To create a **CPU** cluster now, run the cell below. The autoscale settings mean that the cluster will scale down to 0 nodes when inactive and up to 4 nodes when busy."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "# Choose a name for your CPU cluster\n",
-        "cpu_cluster_name = \"cpu-cluster\"\n",
-        "\n",
-        "# Verify that cluster does not exist already\n",
-        "try:\n",
-        "    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
-        "    print(\"Found existing cpu-cluster\")\n",
-        "except ComputeTargetException:\n",
-        "    print(\"Creating new cpu-cluster\")\n",
-        "    \n",
-        "    # Specify the configuration for the new cluster\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_D2_V2\",\n",
-        "                                                           min_nodes=0,\n",
-        "                                                           max_nodes=4)\n",
-        "\n",
-        "    # Create the cluster with the specified name and configuration\n",
-        "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
-        "    \n",
-        "    # Wait for the cluster to complete, show the output log\n",
-        "    cpu_cluster.wait_for_completion(show_output=True)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "To create a **GPU** cluster, run the cell below. Note that your subscription must have sufficient quota for GPU VMs or the command will fail. To increase quota, see [these instructions](https://docs.microsoft.com/en-us/azure/azure-supportability/resource-manager-core-quotas-request). "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "# Choose a name for your GPU cluster\n",
-        "gpu_cluster_name = \"gpu-cluster\"\n",
-        "\n",
-        "# Verify that cluster does not exist already\n",
-        "try:\n",
-        "    gpu_cluster = ComputeTarget(workspace=ws, name=gpu_cluster_name)\n",
-        "    print(\"Found existing gpu cluster\")\n",
-        "except ComputeTargetException:\n",
-        "    print(\"Creating new gpu-cluster\")\n",
-        "    \n",
-        "    # Specify the configuration for the new cluster\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_NC6\",\n",
-        "                                                           min_nodes=0,\n",
-        "                                                           max_nodes=4)\n",
-        "    # Create the cluster with the specified name and configuration\n",
-        "    gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, compute_config)\n",
-        "\n",
-        "    # Wait for the cluster to complete, show the output log\n",
-        "    gpu_cluster.wait_for_completion(show_output=True)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "---\n",
-        "\n",
-        "## Next steps\n",
-        "\n",
-        "In this notebook you configured this notebook library to connect easily to an Azure ML workspace.  You can copy this notebook to your own libraries to connect them to you workspace, or use it to bootstrap new workspaces completely.\n",
-        "\n",
-        "If you came here from another notebook, you can return there and complete that exercise, or you can try out the [Tutorials](./tutorials) or jump into \"how-to\" notebooks and start creating and deploying models.  A good place to start is the [train within notebook](./how-to-use-azureml/training/train-within-notebook) example that walks through a simplified but complete end to end machine learning process."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [],
-      "cell_type": "code"
-    }
-  ],
-  "nbformat_minor": 2
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/configuration.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Configuration\n",
+        "\n",
+        "_**Setting up your Azure Machine Learning services workspace and configuring your notebook library**_\n",
+        "\n",
+        "---\n",
+        "---\n",
+        "\n",
+        "## Table of Contents\n",
+        "\n",
+        "1. [Introduction](#Introduction)\n",
+        "    1. What is an Azure Machine Learning workspace\n",
+        "1. [Setup](#Setup)\n",
+        "    1. Azure subscription\n",
+        "    1. Azure ML SDK and other library installation\n",
+        "    1. Azure Container Instance registration\n",
+        "1. [Configure your Azure ML Workspace](#Configure%20your%20Azure%20ML%20workspace)\n",
+        "    1. Workspace parameters\n",
+        "    1. Access your workspace\n",
+        "    1. Create a new workspace\n",
+        "    1. Create compute resources\n",
+        "1. [Next steps](#Next%20steps)\n",
+        "\n",
+        "---\n",
+        "\n",
+        "## Introduction\n",
+        "\n",
+        "This notebook configures your library of notebooks to connect to an Azure Machine Learning (ML) workspace.  In this case, a library contains all of the notebooks in the current folder and any nested folders.  You can configure this notebook library to use an existing workspace or create a new workspace.\n",
+        "\n",
+        "Typically you will need to run this notebook only once per notebook library as all other notebooks will use connection information that is written here.  If you want to redirect your notebook library to work with a different workspace, then you should re-run this notebook.\n",
+        "\n",
+        "In this notebook you will\n",
+        "* Learn about getting an Azure subscription\n",
+        "* Specify your workspace parameters\n",
+        "* Access or create your workspace\n",
+        "* Add a default compute cluster for your workspace\n",
+        "\n",
+        "### What is an Azure Machine Learning workspace\n",
+        "\n",
+        "An Azure ML Workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an Azure ML Workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, deployment, inference, and the monitoring of deployed models."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup\n",
+        "\n",
+        "This section describes activities required before you can access any Azure ML services functionality."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 1. Azure Subscription\n",
+        "\n",
+        "In order to create an Azure ML Workspace, first you need access to an Azure subscription.  An Azure subscription allows you to manage storage, compute, and other assets in the Azure cloud.  You can [create a new subscription](https://azure.microsoft.com/en-us/free/) or access existing subscription information from the [Azure portal](https://portal.azure.com).  Later in this notebook you will need information such as your subscription ID in order to create and access AML workspaces.\n",
+        "\n",
+        "### 2. Azure ML SDK and other library installation\n",
+        "\n",
+        "If you are running in your own environment, follow [SDK installation instructions](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment).  If you are running in Azure Notebooks or another Microsoft managed environment, the SDK is already installed.\n",
+        "\n",
+        "Also install following libraries to your environment. Many of the example notebooks depend on them\n",
+        "\n",
+        "```\n",
+        "(myenv) $ conda install -y matplotlib tqdm scikit-learn\n",
+        "```\n",
+        "\n",
+        "Once installation is complete, the following cell checks the Azure ML SDK version:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "install"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "import azureml.core\n",
+        "\n",
+        "print(\"This notebook was created using version 1.0.74.1 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If you are using an older version of the SDK then this notebook was created using, you should upgrade your SDK.\n",
+        "\n",
+        "### 3. Azure Container Instance registration\n",
+        "Azure Machine Learning uses of [Azure Container Instance (ACI)](https://azure.microsoft.com/services/container-instances) to deploy dev/test web services. An Azure subscription needs to be registered to use ACI.  If you or the subscription owner have not yet registered ACI on your subscription, you will need to use the [Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest) and execute the following commands.  Note that if you ran through the AML [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) you have already registered ACI. \n",
+        "\n",
+        "```shell\n",
+        "# check to see if ACI is already registered\n",
+        "(myenv) $ az provider show -n Microsoft.ContainerInstance -o table\n",
+        "\n",
+        "# if ACI is not registered, run this command.\n",
+        "# note you need to be the subscription owner in order to execute this command successfully.\n",
+        "(myenv) $ az provider register -n Microsoft.ContainerInstance\n",
+        "```\n",
+        "\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Configure your Azure ML workspace\n",
+        "\n",
+        "### Workspace parameters\n",
+        "\n",
+        "To use an AML Workspace, you will need to import the Azure ML SDK and supply the following information:\n",
+        "* Your subscription id\n",
+        "* A resource group name\n",
+        "* (optional) The region that will host your workspace\n",
+        "* A name for your workspace\n",
+        "\n",
+        "You can get your subscription ID from the [Azure portal](https://portal.azure.com).\n",
+        "\n",
+        "You will also need access to a [_resource group_](https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-group-overview#resource-groups), which organizes Azure resources and provides a default region for the resources in a group.  You can see what resource groups to which you have access, or create a new one in the [Azure portal](https://portal.azure.com).  If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n",
+        "\n",
+        "The region to host your workspace will be used if you are creating a new workspace.  You do not need to specify this if you are using an existing workspace. You can find the list of supported regions [here](https://azure.microsoft.com/en-us/global-infrastructure/services/?products=machine-learning-service).  You should pick a region that is close to your location or that contains your data.\n",
+        "\n",
+        "The name for your workspace is unique within the subscription and should be descriptive enough to discern among other AML Workspaces.  The subscription may be used only by you, or it may be used by your department or your entire enterprise, so choose a name that makes sense for your situation.\n",
+        "\n",
+        "The following cell allows you to specify your workspace parameters.  This cell uses the python method `os.getenv` to read values from environment variables which is useful for automation.  If no environment variable exists, the parameters will be set to the specified default values.  \n",
+        "\n",
+        "If you ran the Azure Machine Learning [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) in Azure Notebooks, you already have a configured workspace!  You can go to your Azure Machine Learning Getting Started library, view *config.json* file, and copy-paste the values for subscription ID, resource group and workspace name below.\n",
+        "\n",
+        "Replace the default values in the cell below with your workspace parameters"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "subscription_id = os.getenv(\"SUBSCRIPTION_ID\", default=\"<my-subscription-id>\")\n",
+        "resource_group = os.getenv(\"RESOURCE_GROUP\", default=\"<my-resource-group>\")\n",
+        "workspace_name = os.getenv(\"WORKSPACE_NAME\", default=\"<my-workspace-name>\")\n",
+        "workspace_region = os.getenv(\"WORKSPACE_REGION\", default=\"eastus2\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Access your workspace\n",
+        "\n",
+        "The following cell uses the Azure ML SDK to attempt to load the workspace specified by your parameters.  If this cell succeeds, your notebook library will be configured to access the workspace from all notebooks using the `Workspace.from_config()` method.  The cell can fail if the specified workspace doesn't exist or you don't have permissions to access it. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace\n",
+        "\n",
+        "try:\n",
+        "    ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name)\n",
+        "    # write the details of the workspace to a configuration file to the notebook library\n",
+        "    ws.write_config()\n",
+        "    print(\"Workspace configuration succeeded. Skip the workspace creation steps below\")\n",
+        "except:\n",
+        "    print(\"Workspace not accessible. Change your parameters or create a new workspace below\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a new workspace\n",
+        "\n",
+        "If you don't have an existing workspace and are the owner of the subscription or resource group, you can create a new workspace.  If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n",
+        "\n",
+        "**Note**: As with other Azure services, there are limits on certain resources (for example AmlCompute quota) associated with the Azure ML service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n",
+        "\n",
+        "This cell will create an Azure ML workspace for you in a subscription provided you have the correct permissions.\n",
+        "\n",
+        "This will fail if:\n",
+        "* You do not have permission to create a workspace in the resource group\n",
+        "* You do not have permission to create a resource group if it's non-existing.\n",
+        "* You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription\n",
+        "\n",
+        "If workspace creation fails, please work with your IT admin to provide you with the appropriate permissions or to provision the required resources.\n",
+        "\n",
+        "**Note**: A Basic workspace is created by default. If you would like to create an Enterprise workspace, please specify sku = 'enterprise'.\n",
+        "Please visit our [pricing page](https://azure.microsoft.com/en-us/pricing/details/machine-learning/) for more details on our Enterprise edition.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "create workspace"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace\n",
+        "\n",
+        "# Create the workspace using the specified parameters\n",
+        "ws = Workspace.create(name = workspace_name,\n",
+        "                      subscription_id = subscription_id,\n",
+        "                      resource_group = resource_group, \n",
+        "                      location = workspace_region,\n",
+        "                      create_resource_group = True,\n",
+        "                      sku = 'basic',\n",
+        "                      exist_ok = True)\n",
+        "ws.get_details()\n",
+        "\n",
+        "# write the details of the workspace to a configuration file to the notebook library\n",
+        "ws.write_config()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create compute resources for your training experiments\n",
+        "\n",
+        "Many of the sample notebooks use Azure ML managed compute (AmlCompute) to train models using a dynamically scalable pool of compute. In this section you will create default compute clusters for use by the other notebooks and any other operations you choose.\n",
+        "\n",
+        "To create a cluster, you need to specify a compute configuration that specifies the type of machine to be used and the scalability behaviors.  Then you choose a name for the cluster that is unique within the workspace that can be used to address the cluster later.\n",
+        "\n",
+        "The cluster parameters are:\n",
+        "* vm_size - this describes the virtual machine type and size used in the cluster.  All machines in the cluster are the same type.  You can get the list of vm sizes available in your region by using the CLI command\n",
+        "\n",
+        "```shell\n",
+        "az vm list-skus -o tsv\n",
+        "```\n",
+        "* min_nodes - this sets the minimum size of the cluster.  If you set the minimum to 0 the cluster will shut down all nodes while not in use.  Setting this number to a value higher than 0 will allow for faster start-up times, but you will also be billed when the cluster is not in use.\n",
+        "* max_nodes - this sets the maximum size of the cluster.  Setting this to a larger number allows for more concurrency and a greater distributed processing of scale-out jobs.\n",
+        "\n",
+        "\n",
+        "To create a **CPU** cluster now, run the cell below. The autoscale settings mean that the cluster will scale down to 0 nodes when inactive and up to 4 nodes when busy."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# Choose a name for your CPU cluster\n",
+        "cpu_cluster_name = \"cpu-cluster\"\n",
+        "\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
+        "    print(\"Found existing cpu-cluster\")\n",
+        "except ComputeTargetException:\n",
+        "    print(\"Creating new cpu-cluster\")\n",
+        "    \n",
+        "    # Specify the configuration for the new cluster\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_D2_V2\",\n",
+        "                                                           min_nodes=0,\n",
+        "                                                           max_nodes=4)\n",
+        "\n",
+        "    # Create the cluster with the specified name and configuration\n",
+        "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
+        "    \n",
+        "    # Wait for the cluster to complete, show the output log\n",
+        "    cpu_cluster.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To create a **GPU** cluster, run the cell below. Note that your subscription must have sufficient quota for GPU VMs or the command will fail. To increase quota, see [these instructions](https://docs.microsoft.com/en-us/azure/azure-supportability/resource-manager-core-quotas-request). "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# Choose a name for your GPU cluster\n",
+        "gpu_cluster_name = \"gpu-cluster\"\n",
+        "\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    gpu_cluster = ComputeTarget(workspace=ws, name=gpu_cluster_name)\n",
+        "    print(\"Found existing gpu cluster\")\n",
+        "except ComputeTargetException:\n",
+        "    print(\"Creating new gpu-cluster\")\n",
+        "    \n",
+        "    # Specify the configuration for the new cluster\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_NC6\",\n",
+        "                                                           min_nodes=0,\n",
+        "                                                           max_nodes=4)\n",
+        "    # Create the cluster with the specified name and configuration\n",
+        "    gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, compute_config)\n",
+        "\n",
+        "    # Wait for the cluster to complete, show the output log\n",
+        "    gpu_cluster.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "\n",
+        "## Next steps\n",
+        "\n",
+        "In this notebook you configured this notebook library to connect easily to an Azure ML workspace.  You can copy this notebook to your own libraries to connect them to you workspace, or use it to bootstrap new workspaces completely.\n",
+        "\n",
+        "If you came here from another notebook, you can return there and complete that exercise, or you can try out the [Tutorials](./tutorials) or jump into \"how-to\" notebooks and start creating and deploying models.  A good place to start is the [train within notebook](./how-to-use-azureml/training/train-within-notebook) example that walks through a simplified but complete end to end machine learning process."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "ninhu"
+      }
+    ],
+    "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
 }

configuration.yml

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

contrib/RAPIDS/README.md

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

contrib/RAPIDS/process_data.py

@@ -15,21 +15,6 @@ from glob import glob
 import os
 import argparse
 
-def initialize_rmm_pool():
-    from librmm_cffi import librmm_config as rmm_cfg
-
-    rmm_cfg.use_pool_allocator = True
-    #rmm_cfg.initial_pool_size = 2<<30 # set to 2GiB. Default is 1/2 total GPU memory
-    import cudf
-    return cudf._gdf.rmm_initialize()
-
-def initialize_rmm_no_pool():
-    from librmm_cffi import librmm_config as rmm_cfg
-    
-    rmm_cfg.use_pool_allocator = False
-    import cudf
-    return cudf._gdf.rmm_initialize()
-
 def run_dask_task(func, **kwargs):
     task = func(**kwargs)
     return task
@@ -207,26 +192,26 @@ def gpu_load_names(col_path):
 
 def create_ever_features(gdf, **kwargs):
     everdf = gdf[['loan_id', 'current_loan_delinquency_status']]
-    everdf = everdf.groupby('loan_id', method='hash').max()
+    everdf = everdf.groupby('loan_id', method='hash').max().reset_index()
     del(gdf)
-    everdf['ever_30'] = (everdf['max_current_loan_delinquency_status'] >= 1).astype('int8')
-    everdf['ever_90'] = (everdf['max_current_loan_delinquency_status'] >= 3).astype('int8')
-    everdf['ever_180'] = (everdf['max_current_loan_delinquency_status'] >= 6).astype('int8')
-    everdf.drop_column('max_current_loan_delinquency_status')
+    everdf['ever_30'] = (everdf['current_loan_delinquency_status'] >= 1).astype('int8')
+    everdf['ever_90'] = (everdf['current_loan_delinquency_status'] >= 3).astype('int8')
+    everdf['ever_180'] = (everdf['current_loan_delinquency_status'] >= 6).astype('int8')
+    everdf.drop_column('current_loan_delinquency_status')
     return everdf
 
 def create_delinq_features(gdf, **kwargs):
     delinq_gdf = gdf[['loan_id', 'monthly_reporting_period', 'current_loan_delinquency_status']]
     del(gdf)
-    delinq_30 = delinq_gdf.query('current_loan_delinquency_status >= 1')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min()
-    delinq_30['delinquency_30'] = delinq_30['min_monthly_reporting_period']
-    delinq_30.drop_column('min_monthly_reporting_period')
-    delinq_90 = delinq_gdf.query('current_loan_delinquency_status >= 3')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min()
-    delinq_90['delinquency_90'] = delinq_90['min_monthly_reporting_period']
-    delinq_90.drop_column('min_monthly_reporting_period')
-    delinq_180 = delinq_gdf.query('current_loan_delinquency_status >= 6')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min()
-    delinq_180['delinquency_180'] = delinq_180['min_monthly_reporting_period']
-    delinq_180.drop_column('min_monthly_reporting_period')
+    delinq_30 = delinq_gdf.query('current_loan_delinquency_status >= 1')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min().reset_index()
+    delinq_30['delinquency_30'] = delinq_30['monthly_reporting_period']
+    delinq_30.drop_column('monthly_reporting_period')
+    delinq_90 = delinq_gdf.query('current_loan_delinquency_status >= 3')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min().reset_index()
+    delinq_90['delinquency_90'] = delinq_90['monthly_reporting_period']
+    delinq_90.drop_column('monthly_reporting_period')
+    delinq_180 = delinq_gdf.query('current_loan_delinquency_status >= 6')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min().reset_index()
+    delinq_180['delinquency_180'] = delinq_180['monthly_reporting_period']
+    delinq_180.drop_column('monthly_reporting_period')
     del(delinq_gdf)
     delinq_merge = delinq_30.merge(delinq_90, how='left', on=['loan_id'], type='hash')
     delinq_merge['delinquency_90'] = delinq_merge['delinquency_90'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]'))
@@ -279,16 +264,15 @@ def create_joined_df(gdf, everdf, **kwargs):
 def create_12_mon_features(joined_df, **kwargs):
     testdfs = []
     n_months = 12
+
     for y in range(1, n_months + 1):
         tmpdf = joined_df[['loan_id', 'timestamp_year', 'timestamp_month', 'delinquency_12', 'upb_12']]
         tmpdf['josh_months'] = tmpdf['timestamp_year'] * 12 + tmpdf['timestamp_month']
         tmpdf['josh_mody_n'] = ((tmpdf['josh_months'].astype('float64') - 24000 - y) / 12).floor()
-        tmpdf = tmpdf.groupby(['loan_id', 'josh_mody_n'], method='hash').agg({'delinquency_12': 'max','upb_12': 'min'})
-        tmpdf['delinquency_12'] = (tmpdf['max_delinquency_12']>3).astype('int32')
-        tmpdf['delinquency_12'] +=(tmpdf['min_upb_12']==0).astype('int32')
-        tmpdf.drop_column('max_delinquency_12')
-        tmpdf['upb_12'] = tmpdf['min_upb_12']
-        tmpdf.drop_column('min_upb_12')
+        tmpdf = tmpdf.groupby(['loan_id', 'josh_mody_n'], method='hash').agg({'delinquency_12': 'max','upb_12': 'min'}).reset_index()
+        tmpdf['delinquency_12'] = (tmpdf['delinquency_12']>3).astype('int32')
+        tmpdf['delinquency_12'] +=(tmpdf['upb_12']==0).astype('int32')
+        tmpdf['upb_12'] = tmpdf['upb_12']
         tmpdf['timestamp_year'] = (((tmpdf['josh_mody_n'] * n_months) + 24000 + (y - 1)) / 12).floor().astype('int16')
         tmpdf['timestamp_month'] = np.int8(y)
         tmpdf.drop_column('josh_mody_n')
@@ -329,6 +313,7 @@ def last_mile_cleaning(df, **kwargs):
         'delinquency_30', 'delinquency_90', 'delinquency_180', 'upb_12',
         'zero_balance_effective_date','foreclosed_after', 'disposition_date','timestamp'
     ]
+
     for column in drop_list:
         df.drop_column(column)
     for col, dtype in df.dtypes.iteritems():
@@ -342,7 +327,6 @@ def last_mile_cleaning(df, **kwargs):
     return df.to_arrow(preserve_index=False)
 
 def main():
-    #print('XGBOOST_BUILD_DOC is ' + os.environ['XGBOOST_BUILD_DOC'])
     parser = argparse.ArgumentParser("rapidssample")
     parser.add_argument("--data_dir", type=str, help="location of data")
     parser.add_argument("--num_gpu", type=int, help="Number of GPUs to use", default=1)
@@ -364,7 +348,6 @@ def main():
     print('data_dir = {0}'.format(data_dir))
     print('num_gpu = {0}'.format(num_gpu))
     print('part_count = {0}'.format(part_count))
-    #part_count = part_count + 1 # adding one because the usage below is not inclusive
     print('end_year = {0}'.format(end_year))
     print('cpu_predictor = {0}'.format(cpu_predictor))
     
@@ -380,19 +363,17 @@ def main():
     client
     print(client.ncores())
 
-# to download data for this notebook, visit https://rapidsai.github.io/demos/datasets/mortgage-data and update the following paths accordingly
+    # to download data for this notebook, visit https://rapidsai.github.io/demos/datasets/mortgage-data and update the following paths accordingly
     acq_data_path = "{0}/acq".format(data_dir) #"/rapids/data/mortgage/acq"
     perf_data_path = "{0}/perf".format(data_dir) #"/rapids/data/mortgage/perf"
     col_names_path = "{0}/names.csv".format(data_dir) # "/rapids/data/mortgage/names.csv"
     start_year = 2000
-#end_year = 2000 # end_year is inclusive -- converted to parameter
-#part_count = 2 # the number of data files to train against -- converted to parameter
 
-    client.run(initialize_rmm_pool)
     client
-    print(client.ncores())
-# NOTE: The ETL calculates additional features which are then dropped before creating the XGBoost DMatrix.
-# This can be optimized to avoid calculating the dropped features.
+    print('--->>> Workers used: {0}'.format(client.ncores()))
+
+    # NOTE: The ETL calculates additional features which are then dropped before creating the XGBoost DMatrix.
+    # This can be optimized to avoid calculating the dropped features.
     print("Reading ...")
     t1 = datetime.datetime.now()
     gpu_dfs = []
@@ -414,14 +395,9 @@ def main():
             
     wait(gpu_dfs)
     t2 = datetime.datetime.now()
-    print("Reading time ...")
-    print(t2-t1)
-    print('len(gpu_dfs) is {0}'.format(len(gpu_dfs)))
-    
-    client.run(cudf._gdf.rmm_finalize)
-    client.run(initialize_rmm_no_pool)
-    client
-    print(client.ncores())
+    print("Reading time: {0}".format(str(t2-t1)))
+    print('--->>> Number of data parts: {0}'.format(len(gpu_dfs)))
+
     dxgb_gpu_params = {
         'nround':            100,
         'max_depth':         8,
@@ -438,7 +414,7 @@ def main():
         'n_gpus':            1, 
         'distributed_dask':  True,
         'loss':              'ls',
-        'objective':         'gpu:reg:linear',
+        'objective':         'reg:squarederror',
         'max_features':      'auto',
         'criterion':         'friedman_mse',
         'grow_policy':       'lossguide',
@@ -446,13 +422,13 @@ def main():
     }
       
     if cpu_predictor:
-        print('Training using CPUs')
+        print('\n---->>>> Training using CPUs <<<<----\n')
         dxgb_gpu_params['predictor'] = 'cpu_predictor'
         dxgb_gpu_params['tree_method'] = 'hist'
         dxgb_gpu_params['objective'] = 'reg:linear'
         
     else:
-        print('Training using GPUs')
+        print('\n---->>>> Training using GPUs <<<<----\n')
     
     print('Training parameters are {0}'.format(dxgb_gpu_params))
     
@@ -481,14 +457,13 @@ def main():
     gpu_dfs = [gpu_df.persist() for gpu_df in gpu_dfs]
     gc.collect()
     wait(gpu_dfs)
-    
+
+    # TRAIN THE MODEL
     labels = None
     t1 = datetime.datetime.now()
     bst = dxgb_gpu.train(client, dxgb_gpu_params, gpu_dfs, labels, num_boost_round=dxgb_gpu_params['nround'])
     t2 = datetime.datetime.now()
-    print("Training time ...")
-    print(t2-t1)
-    print('str(bst) is {0}'.format(str(bst)))
+    print('\n---->>>> Training time: {0} <<<<----\n'.format(str(t2-t1)))
     print('Exiting script')
 
 if __name__ == '__main__':

contrib/batch_inferencing/Code/digit_identification.py

@@ -0,0 +1,41 @@
+
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+import os
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+from azureml.core import Model
+
+
+def init():
+    global g_tf_sess
+
+    # pull down model from workspace
+    model_path = Model.get_model_path("mnist")
+
+    # contruct graph to execute
+    tf.reset_default_graph()
+    saver = tf.train.import_meta_graph(os.path.join(model_path, 'mnist-tf.model.meta'))
+    g_tf_sess = tf.Session(config=tf.ConfigProto(device_count={'GPU': 0}))
+    saver.restore(g_tf_sess, os.path.join(model_path, 'mnist-tf.model'))
+
+
+def run(mini_batch):
+    print(f'run method start: {__file__}, run({mini_batch})')
+    resultList = []
+    in_tensor = g_tf_sess.graph.get_tensor_by_name("network/X:0")
+    output = g_tf_sess.graph.get_tensor_by_name("network/output/MatMul:0")
+
+    for image in mini_batch:
+        # prepare each image
+        data = Image.open(image)
+        np_im = np.array(data).reshape((1, 784))
+        # perform inference
+        inference_result = output.eval(feed_dict={in_tensor: np_im}, session=g_tf_sess)
+        # find best probability, and add to result list
+        best_result = np.argmax(inference_result)
+        resultList.append("{}: {}".format(os.path.basename(image), best_result))
+
+    return resultList

contrib/batch_inferencing/Code/iris_score.py

@@ -0,0 +1,31 @@
+import io
+import pickle
+import argparse
+import numpy as np
+
+from azureml.core.model import Model
+from sklearn.linear_model import LogisticRegression
+
+
+def init():
+    global iris_model
+
+    parser = argparse.ArgumentParser(description="Iris model serving")
+    parser.add_argument('--model_name', dest="model_name", required=True)
+    args, unknown_args = parser.parse_known_args()
+
+    model_path = Model.get_model_path(args.model_name)
+    with open(model_path, 'rb') as model_file:
+        iris_model = pickle.load(model_file)
+
+
+def run(input_data):
+    # make inference
+    num_rows, num_cols = input_data.shape
+    pred = iris_model.predict(input_data).reshape((num_rows, 1))
+
+    # cleanup output
+    result = input_data.drop(input_data.columns[4:], axis=1)
+    result['variety'] = pred
+
+    return result

contrib/batch_inferencing/README.md

@@ -0,0 +1,127 @@
+# Azure Machine Learning Batch Inference
+
+Azure Machine Learning Batch Inference targets large inference jobs that are not time-sensitive. Batch Inference provides cost-effective inference compute scaling, with unparalleled throughput for asynchronous applications. It is optimized for high-throughput, fire-and-forget inference over large collections of data.
+
+# Getting Started with Batch Inference Public Preview
+
+Batch inference public preview offers a platform in which to do large inference or generic parallel map-style operations. Below introduces the major steps to use this new functionality. For a quick try, please follow the prerequisites and simply run the sample notebooks provided in this directory.
+
+## Prerequisites
+
+### Python package installation
+Following the convention of most AzureML Public Preview features, Batch Inference SDK is currently available as a contrib package.
+
+If you're unfamiliar with creating a new Python environment, you may follow this example for [creating a conda environment](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#local).  Batch Inference package can be installed through the following pip command.
+```
+pip install azureml-contrib-pipeline-steps
+```
+
+### Creation of Azure Machine Learning Workspace
+If you do not already have a Azure ML Workspace, please run the [configuration Notebook](../../configuration.ipynb).
+
+## Configure a Batch Inference job
+
+To run a Batch Inference job, you will need to gather some configuration data.
+
+1. **ParallelRunConfig**
+    - **entry_script**: the local file path to the scoring script. If source_directory is specified, use relative path, otherwise use any path accessible on machine.
+    - **error_threshold**: the number of record failures for TabularDataset and file failures for FileDataset that should be ignored during processing. If the aggregated error count (across all workers) goes above this value, then the job will be aborted. Set to -1 to ignore all failures during processing.
+    - **output_action**: one of the following values
+        - **"append_row"**: all values output by run() method invocations will be aggregated into one unique file named parallel_run_step.txt that is created in the output location.
+        - **"summary_only"** – scoring script will handle the output by itself.  The script still needs to return one output row per successfully-processed input item. This is used for error threshold calculation (the actual value of the output row is ignored).
+    - **source_directory**: supporting files for scoring (optional)
+    - **compute_target**: only **AmlCompute** is supported currently
+    - **node_count**: number of compute nodes to use.
+    - **process_count_per_node**: number of processes per node (optional, default value is 1).
+    - **mini_batch_size**: the approximate amount of input data passed to each run() invocation.  For FileDataset input, this is number of files user script can process in one run() call. For TabularDataset input it is approximate size of data user script can process in one run() call. E.g. 1024, 1024KB, 10MB, 1GB (optional, default value 10 files for FileDataset and 1MB for TabularDataset.)
+    - **logging_level**: log verbosity. Values in increasing verbosity are: 'WARNING', 'INFO', 'DEBUG' (optional, default value is 'INFO').
+    - **run_invocation_timeout**: run method invocation timeout period in seconds (optional, default value is 60).
+    - **environment**: The environment definition. This field configures the Python environment. It can be configured to use an existing Python environment or to set up a temp environment for the experiment. The definition is also responsible for setting the required application dependencies.
+    - **description**: name given to batch service.
+
+2. **Scoring (entry) script**: entry point for execution, scoring script should contain two functions:
+    - **init()**: this function should be used for any costly or common preparation for subsequent inferences, e.g., deserializing and loading the model into a global object.
+    - **run(mini_batch)**: The method to be parallelized. Each invocation will have one minibatch.
+        - **mini_batch**: Batch inference will invoke run method and pass either a list or Pandas DataFrame as an argument to the method. Each entry in min_batch will be - a filepath if input is a FileDataset, a Pandas DataFrame if input is a TabularDataset.
+        - **return value**: run() method should return a Pandas DataFrame or an array. For append_row output_action, these returned elements are appended into the common output file. For summary_only, the contents of the elements are ignored. For all output actions, each returned output element indicates one successful inference of input element in the input mini-batch.
+
+3. **Base image** (optional)
+    - if GPU is required, use DEFAULT_GPU_IMAGE as base image in environment. [Example GPU environment](./file-dataset-image-inference-mnist.ipynb#specify-the-environment-to-run-the-script)
+
+Example image pull:
+```python
+from azureml.core.runconfig import ContainerRegistry
+
+# use an image available in public Container Registry without authentication
+public_base_image = "mcr.microsoft.com/azureml/o16n-sample-user-base/ubuntu-miniconda"
+
+# or use an image available in a private Container Registry
+base_image = "myregistry.azurecr.io/mycustomimage:1.0"
+base_image_registry = ContainerRegistry()
+base_image_registry.address = "myregistry.azurecr.io"
+base_image_registry.username = "username"
+base_image_registry.password = "password"
+```
+
+
+## Create a batch inference job
+
+**ParallelRunStep** is a newly added step in the azureml.contrib.pipeline.steps package. You will use it to add a step to create a batch inference job with your Azure machine learning pipeline. (Use batch inference without an Azure machine learning pipeline is not supported yet). ParallelRunStep has all the following parameters:
+  - **name**: this name will be used to register batch inference service, has the following naming restrictions: (unique, 3-32 chars and regex ^\[a-z\]([-a-z0-9]*[a-z0-9])?$)
+  - **models**: zero or more model names already registered in Azure Machine Learning model registry.
+  - **parallel_run_config**: ParallelRunConfig as defined above.
+  - **inputs**: one or more Dataset objects.
+  - **output**: this should be a PipelineData object encapsulating an Azure BLOB container path. 
+  - **arguments**: list of custom arguments passed to scoring script (optional)
+  - **allow_reuse**: optional, default value is True. If the inputs remain the same as a previous run, it will make the previous run results immediately available (skips re-computing the step).
+
+## Passing arguments from pipeline submission to script
+
+Many tasks require arguments to be passed from job submission to the distributed runs. Below is an example to pass such information.
+```
+# from script which creates pipeline job
+parallelrun_step = ParallelRunStep(
+  ...
+  arguments=["--model_name", "mosaic"]     # name of the model we want to use, in case we have more than one option
+)
+```
+```
+# from driver.py/score.py/task.py
+import argparse
+
+parser.add_argument('--model_name', dest="model_name")
+
+args, unknown_args = parser.parse_known_args()
+
+# to access values
+args.model_name # "mosaic"
+```
+
+## Submit a batch inference job
+
+You can submit a batch inference job by pipeline_run, or through REST calls with a published pipeline. To control node count using REST API/experiment, please use aml_node_count(special) pipeline parameter. A typical use case follows:
+
+```python
+pipeline = Pipeline(workspace=ws, steps=[parallelrun_step])
+pipeline_run = Experiment(ws, 'name_of_pipeline_run').submit(pipeline)
+```
+
+## Monitor your batch inference job
+
+A batch inference job can take a long time to finish. You can monitor your job's progress from Azure portal, using Azure ML widgets, view console output through SDK, or check out overview.txt in log/azureml directory.
+
+```python
+# view with widgets (will display GUI inside a browser)
+from azureml.widgets import RunDetails
+RunDetails(pipeline_run).show()
+
+# simple console output
+pipeline_run.wait_for_completion(show_output=True)
+```
+
+# Sample notebooks
+
+-  [file-dataset-image-inference-mnist.ipynb](./file-dataset-image-inference-mnist.ipynb) demonstrates how to run batch inference on an MNIST dataset.
+-  [tabular-dataset-inference-iris.ipynb](./tabular-dataset-inference-iris.ipynb) demonstrates how to run batch inference on an IRIS dataset.
+
+![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/batch_inferencing/README.png) 

contrib/batch_inferencing/file-dataset-image-inference-mnist.ipynb

@@ -0,0 +1,537 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved. \n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Using Azure Machine Learning Pipelines for Batch Inference\n",
+        "\n",
+        "In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n",
+        "\n",
+        "> **Tip**\n",
+        "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n",
+        "\n",
+        "In this example will be take a digit identification model already-trained on MNIST dataset using the [AzureML training with deep learning example notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb), and run that trained model on some of the MNIST test images in batch.  \n",
+        "\n",
+        "The input dataset used for this notebook differs from a standard MNIST dataset in that it has been converted to PNG images to demonstrate use of files as inputs to Batch Inference. A sample of PNG-converted images of the MNIST dataset were take from [this repository](https://github.com/myleott/mnist_png). \n",
+        "\n",
+        "The outline of this notebook is as follows:\n",
+        "\n",
+        "- Create a DataStore referencing MNIST images stored in a blob container.\n",
+        "- Register the pretrained MNIST model into the model registry. \n",
+        "- Use the registered model to do batch inference on the images in the data blob container.\n",
+        "\n",
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/contrib/batch_inferencing/file-dataset-image-inference-mnist.png)\n",
+        "\n",
+        "## Prerequisites\n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first. This sets you up with a working config file that has information on your workspace, subscription id, etc. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Connect to workspace\n",
+        "Create a workspace object from the existing workspace. Workspace.from_config() reads the file config.json and loads the details into an object named ws."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print('Workspace name: ' + ws.name, \n",
+        "      'Azure region: ' + ws.location, \n",
+        "      'Subscription id: ' + ws.subscription_id, \n",
+        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create or Attach existing compute resource\n",
+        "By using Azure Machine Learning Compute, a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you create Azure Machine Learning Compute as your training environment. The code below creates the compute clusters for you if they don't already exist in your workspace.\n",
+        "\n",
+        "**Creation of compute takes approximately 5 minutes. If the AmlCompute with that name is already in your workspace the code will skip the creation process.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from azureml.core.compute import AmlCompute, ComputeTarget\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# choose a name for your cluster\n",
+        "compute_name = os.environ.get(\"AML_COMPUTE_CLUSTER_NAME\", \"cpu-cluster\")\n",
+        "compute_min_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MIN_NODES\", 0)\n",
+        "compute_max_nodes = os.environ.get(\"AML_COMPUTE_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",
+        "\n",
+        "\n",
+        "if compute_name in ws.compute_targets:\n",
+        "    compute_target = ws.compute_targets[compute_name]\n",
+        "    if compute_target and type(compute_target) is AmlCompute:\n",
+        "        print('found compute target. just use it. ' + compute_name)\n",
+        "else:\n",
+        "    print('creating a new compute target...')\n",
+        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = vm_size,\n",
+        "                                                                min_nodes = compute_min_nodes, \n",
+        "                                                                max_nodes = compute_max_nodes)\n",
+        "\n",
+        "    # create the cluster\n",
+        "    compute_target = ComputeTarget.create(ws, compute_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 AmlCompute status, use get_status()\n",
+        "    print(compute_target.get_status().serialize())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a datastore containing sample images\n",
+        "The input dataset used for this notebook differs from a standard MNIST dataset in that it has been converted to PNG images to demonstrate use of files as inputs to Batch Inference. A sample of PNG-converted images of the MNIST dataset were take from [this repository](https://github.com/myleott/mnist_png).\n",
+        "\n",
+        "We have created a public blob container `sampledata` on an account named `pipelinedata`, containing these images from the MNIST dataset. In the next step, we create a datastore with the name `images_datastore`, which points to this blob container. In the call to `register_azure_blob_container` below, setting the `overwrite` flag to `True` overwrites any datastore that was created previously with that name. \n",
+        "\n",
+        "This step can be changed to point to your blob container by providing your own `datastore_name`, `container_name`, and `account_name`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.datastore import Datastore\n",
+        "\n",
+        "account_name = \"pipelinedata\"\n",
+        "datastore_name = \"mnist_datastore\"\n",
+        "container_name = \"sampledata\"\n",
+        "\n",
+        "mnist_data = Datastore.register_azure_blob_container(ws, \n",
+        "                      datastore_name=datastore_name, \n",
+        "                      container_name= container_name, \n",
+        "                      account_name=account_name,\n",
+        "                      overwrite=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Next, let's specify the default datastore for the outputs."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def_data_store = ws.get_default_datastore()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a FileDataset\n",
+        "A [FileDataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.filedataset?view=azure-ml-py) references single or multiple files in your datastores or public urls. The files can be of any format. FileDataset provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.dataset import Dataset\n",
+        "\n",
+        "mnist_ds_name = 'mnist_sample_data'\n",
+        "\n",
+        "path_on_datastore = mnist_data.path('mnist')\n",
+        "input_mnist_ds = Dataset.File.from_files(path=path_on_datastore, validate=False)\n",
+        "registered_mnist_ds = input_mnist_ds.register(ws, mnist_ds_name, create_new_version=True)\n",
+        "named_mnist_ds = registered_mnist_ds.as_named_input(mnist_ds_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Intermediate/Output Data\n",
+        "Intermediate data (or output of a Step) is represented by [PipelineData](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py) object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps.\n",
+        "\n",
+        "**Constructing PipelineData**\n",
+        "- name: [Required] Name of the data item within the pipeline graph\n",
+        "- datastore_name: Name of the Datastore to write this output to\n",
+        "- output_name: Name of the output\n",
+        "- output_mode: Specifies \"upload\" or \"mount\" modes for producing output (default: mount)\n",
+        "- output_path_on_compute: For \"upload\" mode, the path to which the module writes this output during execution\n",
+        "- output_overwrite: Flag to overwrite pre-existing data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import Pipeline, PipelineData\n",
+        "\n",
+        "output_dir = PipelineData(name=\"inferences\", \n",
+        "                          datastore=def_data_store, \n",
+        "                          output_path_on_compute=\"mnist/results\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Download the Model\n",
+        "\n",
+        "Download and extract the model from https://pipelinedata.blob.core.windows.net/mnist-model/mnist-tf.tar.gz to \"models\" directory"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import tarfile\n",
+        "import urllib.request\n",
+        "\n",
+        "# create directory for model\n",
+        "model_dir = 'models'\n",
+        "if not os.path.isdir(model_dir):\n",
+        "    os.mkdir(model_dir)\n",
+        "\n",
+        "url=\"https://pipelinedata.blob.core.windows.net/mnist-model/mnist-tf.tar.gz\"\n",
+        "response = urllib.request.urlretrieve(url, \"model.tar.gz\")\n",
+        "tar = tarfile.open(\"model.tar.gz\", \"r:gz\")\n",
+        "tar.extractall(model_dir)\n",
+        "\n",
+        "os.listdir(model_dir)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Register the model with Workspace\n",
+        "A registered model is a logical container for one or more files that make up your model. For example, if you have a model that's stored in multiple files, you can register them as a single model in the workspace. After you register the files, you can then download or deploy the registered model and receive all the files that you registered.\n",
+        "\n",
+        "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric. Learn more about registering models [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where#registermodel) "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.model import Model\n",
+        "\n",
+        "# register downloaded model \n",
+        "model = Model.register(model_path = \"models/\",\n",
+        "                       model_name = \"mnist\", # this is the name the model is registered as\n",
+        "                       tags = {'pretrained': \"mnist\"},\n",
+        "                       description = \"Mnist trained tensorflow model\",\n",
+        "                       workspace = ws)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Using your model to make batch predictions\n",
+        "To use the model to make batch predictions, you need an **entry script** and a list of **dependencies**:\n",
+        "\n",
+        "#### An entry script\n",
+        "This script accepts requests, scores the requests by using the model, and returns the results.\n",
+        "- __init()__ - Typically this function loads the model into a global object. This function is run only once at the start of batch processing per worker node/process. Init method can make use of following environment variables (ParallelRunStep input):\n",
+        "    1.\tAZUREML_BI_OUTPUT_PATH \u00e2\u20ac\u201c output folder path\n",
+        "- __run(mini_batch)__ - The method to be parallelized. Each invocation will have one minibatch.<BR>\n",
+        "__mini_batch__: Batch inference will invoke run method and pass either a list or Pandas DataFrame as an argument to the method. Each entry in min_batch will be - a filepath if input is a FileDataset, a Pandas DataFrame if input is a TabularDataset.<BR>\n",
+        "__run__ method response: run() method should return a Pandas DataFrame or an array. For append_row output_action, these returned elements are appended into the common output file. For summary_only, the contents of the elements are ignored. For all output actions, each returned output element indicates one successful inference of input element in the input mini-batch.\n",
+        "    User should make sure that enough data is included in inference result to map input to inference. Inference output will be written in output file and not guaranteed to be in order, user should use some key in the output to map it to input.\n",
+        "    \n",
+        "\n",
+        "#### Dependencies\n",
+        "Helper scripts or Python/Conda packages required to run the entry script or model.\n",
+        "\n",
+        "The deployment configuration for the compute target that hosts the deployed model. This configuration describes things like memory and CPU requirements needed to run the model.\n",
+        "\n",
+        "These items are encapsulated into an inference configuration and a deployment configuration. The inference configuration references the entry script and other dependencies. You define these configurations programmatically when you use the SDK to perform the deployment. You define them in JSON files when you use the CLI."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "scripts_folder = \"Code\"\n",
+        "script_file = \"digit_identification.py\"\n",
+        "\n",
+        "# peek at contents\n",
+        "with open(os.path.join(scripts_folder, script_file)) as inference_file:\n",
+        "    print(inference_file.read())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Build and run the batch inference pipeline\n",
+        "The data, models, and compute resource are now available. Let's put all these together in a pipeline."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "###  Specify the environment to run the script\n",
+        "Specify the conda dependencies for your script. This will allow us to install pip packages as well as configure the inference environment."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Environment\n",
+        "from azureml.core.runconfig import CondaDependencies, DEFAULT_CPU_IMAGE\n",
+        "\n",
+        "batch_conda_deps = CondaDependencies.create(pip_packages=[\"tensorflow==1.13.1\", \"pillow\"])\n",
+        "\n",
+        "batch_env = Environment(name=\"batch_environment\")\n",
+        "batch_env.python.conda_dependencies = batch_conda_deps\n",
+        "batch_env.docker.enabled = True\n",
+        "batch_env.docker.base_image = DEFAULT_CPU_IMAGE"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "###  Create the configuration to wrap the inference script"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.pipeline.steps import ParallelRunStep, ParallelRunConfig\n",
+        "\n",
+        "parallel_run_config = ParallelRunConfig(\n",
+        "    source_directory=scripts_folder,\n",
+        "    entry_script=script_file,\n",
+        "    mini_batch_size=\"5\",\n",
+        "    error_threshold=10,\n",
+        "    output_action=\"append_row\",\n",
+        "    environment=batch_env,\n",
+        "    compute_target=compute_target,\n",
+        "    node_count=2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create the pipeline step\n",
+        "Create the pipeline step using the script, environment configuration, and parameters. Specify the compute target you already attached to your workspace as the target of execution of the script. We will use ParallelRunStep to create the pipeline step."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "parallelrun_step = ParallelRunStep(\n",
+        "    name=\"predict-digits-mnist\",\n",
+        "    parallel_run_config=parallel_run_config,\n",
+        "    inputs=[ named_mnist_ds ],\n",
+        "    output=output_dir,\n",
+        "    models=[ model ],\n",
+        "    arguments=[ ],\n",
+        "    allow_reuse=True\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Run the pipeline\n",
+        "At this point you can run the pipeline and examine the output it produced. The Experiment object is used to track the run of the pipeline"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "\n",
+        "pipeline = Pipeline(workspace=ws, steps=[parallelrun_step])\n",
+        "experiment = Experiment(ws, 'digit_identification')\n",
+        "pipeline_run = experiment.submit(pipeline)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Monitor the run"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.widgets import RunDetails\n",
+        "RunDetails(pipeline_run).show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Optional: View detailed logs (streaming) "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### View the prediction results per input image\n",
+        "In the score.py file above you can see that the ResultList with the filename and the prediction result gets returned. These are written to the DataStore specified in the PipelineData object as the output data, which in this case is called *inferences*. This containers the outputs from  all of the worker nodes used in the compute cluster. You can download this data to view the results ... below just filters to the first 10 rows"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import shutil\n",
+        "\n",
+        "# remove previous run results, if present\n",
+        "shutil.rmtree(\"mnist_results\", ignore_errors=True)\n",
+        "\n",
+        "batch_run = next(pipeline_run.get_children())\n",
+        "batch_output = batch_run.get_output_data(\"inferences\")\n",
+        "batch_output.download(local_path=\"mnist_results\")\n",
+        "\n",
+        "for root, dirs, files in os.walk(\"mnist_results\"):\n",
+        "    for file in files:\n",
+        "        if file.endswith('parallel_run_step.txt'):\n",
+        "            result_file = os.path.join(root,file)\n",
+        "\n",
+        "df = pd.read_csv(result_file, delimiter=\":\", header=None)\n",
+        "df.columns = [\"Filename\", \"Prediction\"]\n",
+        "print(\"Prediction has \", df.shape[0], \" rows\")\n",
+        "df.head(10) "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Cleanup Compute resources\n",
+        "\n",
+        "For re-occuring jobs, it may be wise to keep compute the compute resources and allow compute nodes to scale down to 0. However, since this is just a single-run job, we are free to release the allocated compute resources."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# uncomment below and run if compute resources are no longer needed \n",
+        "# compute_target.delete() "
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "joringer"
+      },
+      {
+        "name": "asraniwa"
+      },
+      {
+        "name": "pansav"
+      },
+      {
+        "name": "tracych"
+      }
+    ],
+    "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": "3.6.9"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

contrib/batch_inferencing/file-dataset-image-inference-mnist.yml

@@ -0,0 +1,6 @@
+name: file-dataset-image-inference-mnist
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-contrib-pipeline-steps
+  - azureml-widgets

contrib/batch_inferencing/tabular-dataset-inference-iris.ipynb

@@ -0,0 +1,513 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Using Azure Machine Learning Pipelines for Batch Inference for CSV Files\n",
+        "\n",
+        "In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n",
+        "\n",
+        "> **Tip**\n",
+        "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n",
+        "\n",
+        "In this example we will take use a machine learning model already trained to predict different types of iris flowers and run that trained model on some of the data in a CSV file which has characteristics of different iris flowers.  However, the same example can be extended to manipulating data to any embarrassingly-parallel processing through a python script.\n",
+        "\n",
+        "The outline of this notebook is as follows:\n",
+        "\n",
+        "- Create a DataStore referencing the CSV files stored in a blob container.\n",
+        "- Register the pretrained model into the model registry. \n",
+        "- Use the registered model to do batch inference on the CSV files in the data blob container.\n",
+        "\n",
+        "## Prerequisites\n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n",
+        "\n",
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/contrib/batch_inferencing/tabular-dataset-inference-iris.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Connect to workspace\n",
+        "Create a workspace object from the existing workspace. Workspace.from_config() reads the file config.json and loads the details into an object named ws."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print('Workspace name: ' + ws.name, \n",
+        "      'Azure region: ' + ws.location, \n",
+        "      'Subscription id: ' + ws.subscription_id, \n",
+        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create or Attach existing compute resource\n",
+        "By using Azure Machine Learning Compute, a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you create Azure Machine Learning Compute as your training environment. The code below creates the compute clusters for you if they don't already exist in your workspace.\n",
+        "\n",
+        "**Creation of compute takes approximately 5 minutes. If the AmlCompute with that name is already in your workspace the code will skip the creation process.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from azureml.core.compute import AmlCompute, ComputeTarget\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# choose a name for your cluster\n",
+        "compute_name = os.environ.get(\"AML_COMPUTE_CLUSTER_NAME\", \"cpu-cluster\")\n",
+        "compute_min_nodes = os.environ.get(\"AML_COMPUTE_CLUSTER_MIN_NODES\", 0)\n",
+        "compute_max_nodes = os.environ.get(\"AML_COMPUTE_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",
+        "\n",
+        "\n",
+        "if compute_name in ws.compute_targets:\n",
+        "    compute_target = ws.compute_targets[compute_name]\n",
+        "    if compute_target and type(compute_target) is AmlCompute:\n",
+        "        print('found compute target. just use it. ' + compute_name)\n",
+        "else:\n",
+        "    print('creating a new compute target...')\n",
+        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = vm_size,\n",
+        "                                                                min_nodes = compute_min_nodes, \n",
+        "                                                                max_nodes = compute_max_nodes)\n",
+        "\n",
+        "    # create the cluster\n",
+        "    compute_target = ComputeTarget.create(ws, compute_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 AmlCompute status, use get_status()\n",
+        "    print(compute_target.get_status().serialize())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a datastore containing sample images\n",
+        "The input dataset used for this notebook is CSV data which has attributes of different iris flowers. We have created a public blob container `sampledata` on an account named `pipelinedata`, containing iris data set. In the next step, we create a datastore with the name `iris_datastore`, which points to this container. In the call to `register_azure_blob_container` below, setting the `overwrite` flag to `True` overwrites any datastore that was created previously with that name. \n",
+        "\n",
+        "This step can be changed to point to your blob container by providing your own `datastore_name`, `container_name`, and `account_name`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.datastore import Datastore\n",
+        "\n",
+        "account_name = \"pipelinedata\"\n",
+        "datastore_name=\"iris_datastore_data\"\n",
+        "container_name=\"sampledata\"\n",
+        "\n",
+        "iris_data = Datastore.register_azure_blob_container(ws, \n",
+        "                      datastore_name=datastore_name, \n",
+        "                      container_name= container_name, \n",
+        "                      account_name=account_name, \n",
+        "                      overwrite=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a TabularDataset\n",
+        "A [TabularDataSet](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) references single or multiple files which contain data in a tabular structure (ie like CSV files) in your datastores or public urls. TabularDatasets provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.dataset import Dataset\n",
+        "\n",
+        "iris_ds_name = 'iris_data'\n",
+        "\n",
+        "path_on_datastore = iris_data.path('iris/')\n",
+        "input_iris_ds = Dataset.Tabular.from_delimited_files(path=path_on_datastore, validate=False)\n",
+        "registered_iris_ds = input_iris_ds.register(ws, iris_ds_name, create_new_version=True)\n",
+        "named_iris_ds = registered_iris_ds.as_named_input(iris_ds_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Intermediate/Output Data\n",
+        "Intermediate data (or output of a Step) is represented by [PipelineData](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py) object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps.\n",
+        "\n",
+        "**Constructing PipelineData**\n",
+        "- name: [Required] Name of the data item within the pipeline graph\n",
+        "- datastore_name: Name of the Datastore to write this output to\n",
+        "- output_name: Name of the output\n",
+        "- output_mode: Specifies \"upload\" or \"mount\" modes for producing output (default: mount)\n",
+        "- output_path_on_compute: For \"upload\" mode, the path to which the module writes this output during execution\n",
+        "- output_overwrite: Flag to overwrite pre-existing data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import PipelineData\n",
+        "\n",
+        "datastore = ws.get_default_datastore()\n",
+        "output_folder = PipelineData(name='inferences', datastore=datastore)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Registering the Model with the Workspace\n",
+        "Get the pretrained model from a publicly available Azure Blob container, then register it to use in your workspace"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model_container_name=\"iris-model\"\n",
+        "model_datastore_name=\"iris_model_datastore\"\n",
+        "\n",
+        "model_datastore = Datastore.register_azure_blob_container(ws, \n",
+        "                      datastore_name=model_datastore_name, \n",
+        "                      container_name= model_container_name, \n",
+        "                      account_name=account_name, \n",
+        "                      overwrite=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.model import Model\n",
+        "\n",
+        "model_datastore.download('iris_model.pkl')\n",
+        "\n",
+        "# register downloaded model\n",
+        "model = Model.register(model_path = \"iris_model.pkl/iris_model.pkl\",\n",
+        "                       model_name = \"iris\", # this is the name the model is registered as\n",
+        "                       tags = {'pretrained': \"iris\"},\n",
+        "                       workspace = ws)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Using your model to make batch predictions\n",
+        "To use the model to make batch predictions, you need an **entry script** and a list of **dependencies**:\n",
+        "\n",
+        "#### An entry script\n",
+        "This script accepts requests, scores the requests by using the model, and returns the results.\n",
+        "- __init()__ - Typically this function loads the model into a global object. This function is run only once at the start of batch processing per worker node/process. init method can make use of following environment variables (ParallelRunStep input):\n",
+        "    1.\tAZUREML_BI_OUTPUT_PATH \u00e2\u20ac\u201c output folder path\n",
+        "- __run(mini_batch)__ - The method to be parallelized. Each invocation will have one minibatch.<BR>\n",
+        "__mini_batch__: Batch inference will invoke run method and pass either a list or Pandas DataFrame as an argument to the method. Each entry in min_batch will be - a filepath if input is a FileDataset, a Pandas DataFrame if input is a TabularDataset.<BR>\n",
+        "__run__ method response: run() method should return a Pandas DataFrame or an array. For append_row output_action, these returned elements are appended into the common output file. For summary_only, the contents of the elements are ignored. For all output actions, each returned output element indicates one successful inference of input element in the input mini-batch.\n",
+        "    User should make sure that enough data is included in inference result to map input to inference. Inference output will be written in output file and not guaranteed to be in order, user should use some key in the output to map it to input.\n",
+        "    \n",
+        "\n",
+        "#### Dependencies\n",
+        "Helper scripts or Python/Conda packages required to run the entry script or model.\n",
+        "\n",
+        "The deployment configuration for the compute target that hosts the deployed model. This configuration describes things like memory and CPU requirements needed to run the model.\n",
+        "\n",
+        "These items are encapsulated into an inference configuration and a deployment configuration. The inference configuration references the entry script and other dependencies. You define these configurations programmatically when you use the SDK to perform the deployment. You define them in JSON files when you use the CLI.\n",
+        "\n",
+        "## Print inferencing script"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "scripts_folder = \"Code\"\n",
+        "script_file = \"iris_score.py\"\n",
+        "\n",
+        "# peek at contents\n",
+        "with open(os.path.join(scripts_folder, script_file)) as inference_file:\n",
+        "    print(inference_file.read())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Build and run the batch inference pipeline\n",
+        "The data, models, and compute resource are now available. Let's put all these together in a pipeline."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "###  Specify the environment to run the script\n",
+        "Specify the conda dependencies for your script. This will allow us to install pip packages as well as configure the inference environment."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Environment\n",
+        "from azureml.core.runconfig import CondaDependencies\n",
+        "\n",
+        "predict_conda_deps = CondaDependencies.create(pip_packages=[ \"scikit-learn==0.20.3\" ])\n",
+        "\n",
+        "predict_env = Environment(name=\"predict_environment\")\n",
+        "predict_env.python.conda_dependencies = predict_conda_deps\n",
+        "predict_env.docker.enabled = True\n",
+        "predict_env.spark.precache_packages = False"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "###  Create the configuration to wrap the inference script"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.contrib.pipeline.steps import ParallelRunStep, ParallelRunConfig\n",
+        "\n",
+        "# In a real-world scenario, you'll want to shape your process per node and nodes to fit your problem domain.\n",
+        "parallel_run_config = ParallelRunConfig(\n",
+        "                    source_directory=scripts_folder,\n",
+        "                    entry_script=script_file,  # the user script to run against each input\n",
+        "                    mini_batch_size='5MB',\n",
+        "                    error_threshold=5,\n",
+        "                    output_action='append_row',\n",
+        "                    environment=predict_env,\n",
+        "                    compute_target=compute_target, \n",
+        "                    node_count=3,\n",
+        "                    run_invocation_timeout=600)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create the pipeline step\n",
+        "Create the pipeline step using the script, environment configuration, and parameters. Specify the compute target you already attached to your workspace as the target of execution of the script. We will use ParallelRunStep to create the pipeline step."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "distributed_csv_iris_step = ParallelRunStep(\n",
+        "    name='example-iris',\n",
+        "    inputs=[named_iris_ds],\n",
+        "    output=output_folder,\n",
+        "    parallel_run_config=parallel_run_config,\n",
+        "    models=[model],\n",
+        "    arguments=['--model_name', 'iris'],\n",
+        "    allow_reuse=True\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Run the pipeline\n",
+        "At this point you can run the pipeline and examine the output it produced. The Experiment object is used to track the run of the pipeline"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "from azureml.pipeline.core import Pipeline\n",
+        "\n",
+        "pipeline = Pipeline(workspace=ws, steps=[distributed_csv_iris_step])\n",
+        "\n",
+        "pipeline_run = Experiment(ws, 'iris').submit(pipeline)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# this will output a table with link to the run details in azure portal\n",
+        "pipeline_run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## View progress of Pipeline run\n",
+        "\n",
+        "The progress of the pipeline is able to be viewed either through azureml.widgets or a console feed from PipelineRun.wait_for_completion()."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# GUI\n",
+        "from azureml.widgets import RunDetails\n",
+        "RunDetails(pipeline_run).show() "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Console logs\n",
+        "pipeline_run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## View Results\n",
+        "In the iris_score.py file above you can see that the Result with the prediction of the iris variety gets returned and then appended to the original input of the row from the csv file. These results are written to the DataStore specified in the PipelineData object as the output data, which in this case is called *inferences*. This contains the outputs from  all of the worker nodes used in the compute cluster. You can download this data to view the results ... below just filters to a random 20 rows"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import shutil\n",
+        "\n",
+        "shutil.rmtree(\"iris_results\", ignore_errors=True)\n",
+        "\n",
+        "prediction_run = next(pipeline_run.get_children())\n",
+        "prediction_output = prediction_run.get_output_data(\"inferences\")\n",
+        "prediction_output.download(local_path=\"iris_results\")\n",
+        "\n",
+        "\n",
+        "for root, dirs, files in os.walk(\"iris_results\"):\n",
+        "    for file in files:\n",
+        "        if file.endswith('parallel_run_step.txt'):\n",
+        "            result_file = os.path.join(root,file)\n",
+        "\n",
+        "# cleanup output format\n",
+        "df = pd.read_csv(result_file, delimiter=\" \", header=None)\n",
+        "df.columns = [\"sepal.length\", \"sepal.width\", \"petal.length\", \"petal.width\", \"variety\"]\n",
+        "print(\"Prediction has \", df.shape[0], \" rows\")\n",
+        "\n",
+        "random_subset = df.sample(n=20)\n",
+        "random_subset.head(20)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Cleanup compute resources\n",
+        "For re-occuring jobs, it may be wise to keep compute the compute resources and allow compute nodes to scale down to 0. However, since this is just a single run job, we are free to release the allocated compute resources."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# uncomment below and run if compute resources are no longer needed \n",
+        "# compute_target.delete()"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "joringer"
+      },
+      {
+        "name": "asraniwa"
+      },
+      {
+        "name": "pansav"
+      },
+      {
+        "name": "tracych"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.2"
+    },
+    "notice": "Copyright (c) Microsoft Corporation. All rights reserved.\u00e2\u20ac\u00afLicensed under the MIT License."
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

contrib/batch_inferencing/tabular-dataset-inference-iris.yml

@@ -0,0 +1,6 @@
+name: tabular-dataset-inference-iris
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-contrib-pipeline-steps
+  - azureml-widgets

contrib/datadrift/azure-ml-datadrift.ipynb

@@ -1,723 +0,0 @@
-{
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "name": "python36",
-      "language": "python"
-    },
-    "authors": [
-      {
-        "name": "rafarmah"
-      }
-    ],
-    "language_info": {
-      "mimetype": "text/x-python",
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "pygments_lexer": "ipython3",
-      "name": "python",
-      "file_extension": ".py",
-      "nbconvert_exporter": "python",
-      "version": "3.6.6"
-    }
-  },
-  "nbformat": 4,
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "# Track Data Drift between Training and Inference Data in Production \n",
-        "\n",
-        "With this notebook, you will learn how to enable the DataDrift service to automatically track and determine whether your inference data is drifting from the data your model was initially trained on. The DataDrift service provides metrics and visualizations to help stakeholders identify which specific features cause the concept drift to occur.\n",
-        "\n",
-        "Please email driftfeedback@microsoft.com with any issues. A member from the DataDrift team will respond shortly. \n",
-        "\n",
-        "The DataDrift Public Preview API can be found [here](https://docs.microsoft.com/en-us/python/api/azureml-contrib-datadrift/?view=azure-ml-py). "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/datadrift/azureml-datadrift.png)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Prerequisites and Setup"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Install the DataDrift package\n",
-        "\n",
-        "Install the azureml-contrib-datadrift, azureml-opendatasets and lightgbm packages before running this notebook.\n",
-        "```\n",
-        "pip install azureml-contrib-datadrift\n",
-        "pip install lightgbm\n",
-        "```"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Import Dependencies"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "import json\n",
-        "import os\n",
-        "import time\n",
-        "from datetime import datetime, timedelta\n",
-        "\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "import requests\n",
-        "from azureml.contrib.datadrift import DataDriftDetector, AlertConfiguration\n",
-        "from azureml.opendatasets import NoaaIsdWeather\n",
-        "from azureml.core import Dataset, Workspace, Run\n",
-        "from azureml.core.compute import AksCompute, ComputeTarget\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.core.image import ContainerImage\n",
-        "from azureml.core.model import Model\n",
-        "from azureml.core.webservice import Webservice, AksWebservice\n",
-        "from azureml.widgets import RunDetails\n",
-        "from sklearn.externals import joblib\n",
-        "from sklearn.model_selection import train_test_split\n"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Set up Configuraton and Create Azure ML Workspace\n",
-        "\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) first if you haven't already to establish your connection to the AzureML Workspace."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "# Please type in your initials/alias. The prefix is prepended to the names of resources created by this notebook. \n",
-        "prefix = \"dd\"\n",
-        "\n",
-        "# NOTE: Please do not change the model_name, as it's required by the score.py file\n",
-        "model_name = \"driftmodel\"\n",
-        "image_name = \"{}driftimage\".format(prefix)\n",
-        "service_name = \"{}driftservice\".format(prefix)\n",
-        "\n",
-        "# optionally, set email address to receive an email alert for DataDrift\n",
-        "email_address = \"\""
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Generate Train/Testing Data\n",
-        "\n",
-        "For this demo, we will use NOAA weather data from [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/). You may replace this step with your own dataset. "
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "usaf_list = ['725724', '722149', '723090', '722159', '723910', '720279',\n",
-        "             '725513', '725254', '726430', '720381', '723074', '726682',\n",
-        "             '725486', '727883', '723177', '722075', '723086', '724053',\n",
-        "             '725070', '722073', '726060', '725224', '725260', '724520',\n",
-        "             '720305', '724020', '726510', '725126', '722523', '703333',\n",
-        "             '722249', '722728', '725483', '722972', '724975', '742079',\n",
-        "             '727468', '722193', '725624', '722030', '726380', '720309',\n",
-        "             '722071', '720326', '725415', '724504', '725665', '725424',\n",
-        "             '725066']\n",
-        "\n",
-        "columns = ['usaf', 'wban', 'datetime', 'latitude', 'longitude', 'elevation', 'windAngle', 'windSpeed', 'temperature', 'stationName', 'p_k']\n",
-        "\n",
-        "\n",
-        "def enrich_weather_noaa_data(noaa_df):\n",
-        "    hours_in_day = 23\n",
-        "    week_in_year = 52\n",
-        "    \n",
-        "    noaa_df[\"hour\"] = noaa_df[\"datetime\"].dt.hour\n",
-        "    noaa_df[\"weekofyear\"] = noaa_df[\"datetime\"].dt.week\n",
-        "    \n",
-        "    noaa_df[\"sine_weekofyear\"] = noaa_df['datetime'].transform(lambda x: np.sin((2*np.pi*x.dt.week-1)/week_in_year))\n",
-        "    noaa_df[\"cosine_weekofyear\"] = noaa_df['datetime'].transform(lambda x: np.cos((2*np.pi*x.dt.week-1)/week_in_year))\n",
-        "\n",
-        "    noaa_df[\"sine_hourofday\"] = noaa_df['datetime'].transform(lambda x: np.sin(2*np.pi*x.dt.hour/hours_in_day))\n",
-        "    noaa_df[\"cosine_hourofday\"] = noaa_df['datetime'].transform(lambda x: np.cos(2*np.pi*x.dt.hour/hours_in_day))\n",
-        "    \n",
-        "    return noaa_df\n",
-        "\n",
-        "def add_window_col(input_df):\n",
-        "    shift_interval = pd.Timedelta('-7 days') # your X days interval\n",
-        "    df_shifted = input_df.copy()\n",
-        "    df_shifted['datetime'] = df_shifted['datetime'] - shift_interval\n",
-        "    df_shifted.drop(list(input_df.columns.difference(['datetime', 'usaf', 'wban', 'sine_hourofday', 'temperature'])), axis=1, inplace=True)\n",
-        "\n",
-        "    # merge, keeping only observations where -1 lag is present\n",
-        "    df2 = pd.merge(input_df,\n",
-        "                   df_shifted,\n",
-        "                   on=['datetime', 'usaf', 'wban', 'sine_hourofday'],\n",
-        "                   how='inner',  # use 'left' to keep observations without lags\n",
-        "                   suffixes=['', '-7'])\n",
-        "    return df2\n",
-        "\n",
-        "def get_noaa_data(start_time, end_time, cols, station_list):\n",
-        "    isd = NoaaIsdWeather(start_time, end_time, cols=cols)\n",
-        "    # Read into Pandas data frame.\n",
-        "    noaa_df = isd.to_pandas_dataframe()\n",
-        "    noaa_df = noaa_df.rename(columns={\"stationName\": \"station_name\"})\n",
-        "    \n",
-        "    df_filtered = noaa_df[noaa_df[\"usaf\"].isin(station_list)]\n",
-        "    df_filtered.reset_index(drop=True)\n",
-        "    \n",
-        "    # Enrich with time features\n",
-        "    df_enriched = enrich_weather_noaa_data(df_filtered)\n",
-        "    \n",
-        "    return df_enriched\n",
-        "\n",
-        "def get_featurized_noaa_df(start_time, end_time, cols, station_list):\n",
-        "    df_1 = get_noaa_data(start_time - timedelta(days=7), start_time - timedelta(seconds=1), cols, station_list)\n",
-        "    df_2 = get_noaa_data(start_time, end_time, cols, station_list)\n",
-        "    noaa_df = pd.concat([df_1, df_2])\n",
-        "    \n",
-        "    print(\"Adding window feature\")\n",
-        "    df_window = add_window_col(noaa_df)\n",
-        "    \n",
-        "    cat_columns = df_window.dtypes == object\n",
-        "    cat_columns = cat_columns[cat_columns == True]\n",
-        "    \n",
-        "    print(\"Encoding categorical columns\")\n",
-        "    df_encoded = pd.get_dummies(df_window, columns=cat_columns.keys().tolist())\n",
-        "    \n",
-        "    print(\"Dropping unnecessary columns\")\n",
-        "    df_featurized = df_encoded.drop(['windAngle', 'windSpeed', 'datetime', 'elevation'], axis=1).dropna().drop_duplicates()\n",
-        "    \n",
-        "    return df_featurized"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "# Train model on Jan 1 - 14, 2009 data\n",
-        "df = get_featurized_noaa_df(datetime(2009, 1, 1), datetime(2009, 1, 14, 23, 59, 59), columns, usaf_list)\n",
-        "df.head()"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "label = \"temperature\"\n",
-        "x_df = df.drop(label, axis=1)\n",
-        "y_df = df[[label]]\n",
-        "x_train, x_test, y_train, y_test = train_test_split(df, y_df, test_size=0.2, random_state=223)\n",
-        "print(x_train.shape, x_test.shape, y_train.shape, y_test.shape)\n",
-        "\n",
-        "training_dir = 'outputs/training'\n",
-        "training_file = \"training.csv\"\n",
-        "\n",
-        "# Generate training dataframe to register as Training Dataset\n",
-        "os.makedirs(training_dir, exist_ok=True)\n",
-        "training_df = pd.merge(x_train.drop(label, axis=1), y_train, left_index=True, right_index=True)\n",
-        "training_df.to_csv(training_dir + \"/\" + training_file)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Create/Register Training Dataset"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "dataset_name = \"dataset\"\n",
-        "name_suffix = datetime.utcnow().strftime(\"%Y-%m-%d-%H-%M-%S\")\n",
-        "snapshot_name = \"snapshot-{}\".format(name_suffix)\n",
-        "\n",
-        "dstore = ws.get_default_datastore()\n",
-        "dstore.upload(training_dir, \"data/training\", show_progress=True)\n",
-        "dpath = dstore.path(\"data/training/training.csv\")\n",
-        "trainingDataset = Dataset.auto_read_files(dpath, include_path=True)\n",
-        "trainingDataset = trainingDataset.register(workspace=ws, name=dataset_name, description=\"dset\", exist_ok=True)\n",
-        "\n",
-        "datasets = [(Dataset.Scenario.TRAINING, trainingDataset)]\n",
-        "print(\"dataset registration done.\\n\")\n",
-        "datasets"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Train and Save Model"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "import lightgbm as lgb\n",
-        "\n",
-        "train = lgb.Dataset(data=x_train, \n",
-        "                    label=y_train)\n",
-        "\n",
-        "test = lgb.Dataset(data=x_test, \n",
-        "                   label=y_test,\n",
-        "                   reference=train)\n",
-        "\n",
-        "params = {'learning_rate'    : 0.1,\n",
-        "          'boosting'         : 'gbdt',\n",
-        "          'metric'           : 'rmse',\n",
-        "          'feature_fraction' : 1,\n",
-        "          'bagging_fraction' : 1,\n",
-        "          'max_depth': 6,\n",
-        "          'num_leaves'       : 31,\n",
-        "          'objective'        : 'regression',\n",
-        "          'bagging_freq'     : 1,\n",
-        "          \"verbose\": -1,\n",
-        "          'min_data_per_leaf': 100}\n",
-        "\n",
-        "model = lgb.train(params, \n",
-        "                  num_boost_round=500,\n",
-        "                  train_set=train,\n",
-        "                  valid_sets=[train, test],\n",
-        "                  verbose_eval=50,\n",
-        "                  early_stopping_rounds=25)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "model_file = 'outputs/{}.pkl'.format(model_name)\n",
-        "\n",
-        "os.makedirs('outputs', exist_ok=True)\n",
-        "joblib.dump(model, model_file)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Register Model"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "model = Model.register(model_path=model_file,\n",
-        "                       model_name=model_name,\n",
-        "                       workspace=ws,\n",
-        "                       datasets=datasets)\n",
-        "\n",
-        "print(model_name, image_name, service_name, model)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Deploy Model To AKS"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Prepare Environment"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn', 'joblib', 'lightgbm', 'pandas'],\n",
-        "                                 pip_packages=['azureml-monitoring', 'azureml-sdk[automl]'])\n",
-        "\n",
-        "with open(\"myenv.yml\",\"w\") as f:\n",
-        "    f.write(myenv.serialize_to_string())"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Create Image"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "# Image creation may take up to 15 minutes.\n",
-        "\n",
-        "image_name = image_name + str(model.version)\n",
-        "\n",
-        "if not image_name in ws.images:\n",
-        "    # Use the score.py defined in this directory as the execution script\n",
-        "    # NOTE: The Model Data Collector must be enabled in the execution script for DataDrift to run correctly\n",
-        "    image_config = ContainerImage.image_configuration(execution_script=\"score.py\",\n",
-        "                                                      runtime=\"python\",\n",
-        "                                                      conda_file=\"myenv.yml\",\n",
-        "                                                      description=\"Image with weather dataset model\")\n",
-        "    image = ContainerImage.create(name=image_name,\n",
-        "                                  models=[model],\n",
-        "                                  image_config=image_config,\n",
-        "                                  workspace=ws)\n",
-        "\n",
-        "    image.wait_for_creation(show_output=True)\n",
-        "else:\n",
-        "    image = ws.images[image_name]"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Create Compute Target"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "aks_name = 'dd-demo-e2e'\n",
-        "prov_config = AksCompute.provisioning_configuration()\n",
-        "\n",
-        "if not aks_name in ws.compute_targets:\n",
-        "    aks_target = ComputeTarget.create(workspace=ws,\n",
-        "                                      name=aks_name,\n",
-        "                                      provisioning_configuration=prov_config)\n",
-        "\n",
-        "    aks_target.wait_for_completion(show_output=True)\n",
-        "    print(aks_target.provisioning_state)\n",
-        "    print(aks_target.provisioning_errors)\n",
-        "else:\n",
-        "    aks_target=ws.compute_targets[aks_name]"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Deploy Service"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "aks_service_name = service_name\n",
-        "\n",
-        "if not aks_service_name in ws.webservices:\n",
-        "    aks_config = AksWebservice.deploy_configuration(collect_model_data=True, enable_app_insights=True)\n",
-        "    aks_service = Webservice.deploy_from_image(workspace=ws,\n",
-        "                                               name=aks_service_name,\n",
-        "                                               image=image,\n",
-        "                                               deployment_config=aks_config,\n",
-        "                                               deployment_target=aks_target)\n",
-        "    aks_service.wait_for_deployment(show_output=True)\n",
-        "    print(aks_service.state)\n",
-        "else:\n",
-        "    aks_service = ws.webservices[aks_service_name]"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Run DataDrift Analysis"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Send Scoring Data to Service"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Download Scoring Data"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "# Score Model on March 15, 2016 data\n",
-        "scoring_df = get_noaa_data(datetime(2016, 3, 15) - timedelta(days=7), datetime(2016, 3, 16),  columns, usaf_list)\n",
-        "# Add the window feature column\n",
-        "scoring_df = add_window_col(scoring_df)\n",
-        "\n",
-        "# Drop features not used by the model\n",
-        "print(\"Dropping unnecessary columns\")\n",
-        "scoring_df = scoring_df.drop(['windAngle', 'windSpeed', 'datetime', 'elevation'], axis=1).dropna()\n",
-        "scoring_df.head()"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "# One Hot Encode the scoring dataset to match the training dataset schema\n",
-        "columns_dict = model.datasets[\"training\"][0].get_profile().columns\n",
-        "extra_cols = ('Path', 'Column1')\n",
-        "for k in extra_cols:\n",
-        "    columns_dict.pop(k, None)\n",
-        "training_columns = list(columns_dict.keys())\n",
-        "\n",
-        "categorical_columns = scoring_df.dtypes == object\n",
-        "categorical_columns = categorical_columns[categorical_columns == True]\n",
-        "\n",
-        "test_df = pd.get_dummies(scoring_df[categorical_columns.keys().tolist()])\n",
-        "encoded_df = scoring_df.join(test_df)\n",
-        "\n",
-        "# Populate missing OHE columns with 0 values to match traning dataset schema\n",
-        "difference = list(set(training_columns) - set(encoded_df.columns.tolist()))\n",
-        "for col in difference:\n",
-        "    encoded_df[col] = 0\n",
-        "encoded_df.head()"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "# Serialize dataframe to list of row dictionaries\n",
-        "encoded_dict = encoded_df.to_dict('records')"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Submit Scoring Data to Service"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "%%time\n",
-        "\n",
-        "# retreive the API keys. AML generates two keys.\n",
-        "key1, key2 = aks_service.get_keys()\n",
-        "\n",
-        "total_count = len(scoring_df)\n",
-        "i = 0\n",
-        "load = []\n",
-        "for row in encoded_dict:\n",
-        "    load.append(row)\n",
-        "    i = i + 1\n",
-        "    if i % 100 == 0:\n",
-        "        payload = json.dumps({\"data\": load})\n",
-        "        \n",
-        "        # construct raw HTTP request and send to the service\n",
-        "        payload_binary = bytes(payload,encoding = 'utf8')\n",
-        "        headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n",
-        "        resp = requests.post(aks_service.scoring_uri, payload_binary, headers=headers)\n",
-        "        \n",
-        "        print(\"prediction:\", resp.content, \"Progress: {}/{}\".format(i, total_count))   \n",
-        "\n",
-        "        load = []\n",
-        "        time.sleep(3)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "We need to wait up to 10 minutes for the Model Data Collector to dump the model input and inference data to storage in the Workspace, where it's used by the DataDriftDetector job."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "time.sleep(600)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Configure DataDrift"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "services = [service_name]\n",
-        "start = datetime.now() - timedelta(days=2)\n",
-        "end = datetime(year=2020, month=1, day=22, hour=15, minute=16)\n",
-        "feature_list = ['usaf', 'wban', 'latitude', 'longitude', 'station_name', 'p_k',  'sine_hourofday', 'cosine_hourofday', 'temperature-7']\n",
-        "alert_config = AlertConfiguration([email_address]) if email_address else None\n",
-        "\n",
-        "# there will be an exception indicating using get() method if DataDrift object already exist\n",
-        "try:\n",
-        "    datadrift = DataDriftDetector.create(ws, model.name, model.version, services, frequency=\"Day\", alert_config=alert_config)\n",
-        "except KeyError:\n",
-        "    datadrift = DataDriftDetector.get(ws, model.name, model.version)\n",
-        "    \n",
-        "print(\"Details of DataDrift Object:\\n{}\".format(datadrift))"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Run an Adhoc DataDriftDetector Run"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "target_date = datetime.today()\n",
-        "run = datadrift.run(target_date, services, feature_list=feature_list, create_compute_target=True)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "exp = Experiment(ws, datadrift._id)\n",
-        "dd_run = Run(experiment=exp, run_id=run)\n",
-        "RunDetails(dd_run).show()"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Get Drift Analysis Results"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "children = list(dd_run.get_children())\n",
-        "for child in children:\n",
-        "    child.wait_for_completion()\n",
-        "\n",
-        "drift_metrics = datadrift.get_output(start_time=start, end_time=end)\n",
-        "drift_metrics"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "# Show all drift figures, one per serivice.\n",
-        "# If setting with_details is False (by default), only drift will be shown; if it's True, all details will be shown.\n",
-        "\n",
-        "drift_figures = datadrift.show(with_details=True)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Enable DataDrift Schedule"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "datadrift.enable_schedule()"
-      ],
-      "cell_type": "code"
-    }
-  ],
-  "nbformat_minor": 2
-}

contrib/datadrift/score.py

@@ -1,58 +0,0 @@
-import pickle
-import json
-import numpy
-import azureml.train.automl
-from sklearn.externals import joblib
-from sklearn.linear_model import Ridge
-from azureml.core.model import Model
-from azureml.core.run import Run
-from azureml.monitoring import ModelDataCollector
-import time
-import pandas as pd
-
-
-def init():
-    global model, inputs_dc, prediction_dc, feature_names, categorical_features
-
-    print("Model is initialized" + time.strftime("%H:%M:%S"))
-    model_path = Model.get_model_path(model_name="driftmodel")
-    model = joblib.load(model_path)
-
-    feature_names = ["usaf", "wban", "latitude", "longitude", "station_name", "p_k",
-                     "sine_weekofyear", "cosine_weekofyear", "sine_hourofday", "cosine_hourofday",
-                     "temperature-7"]
-
-    categorical_features = ["usaf", "wban", "p_k", "station_name"]
-
-    inputs_dc = ModelDataCollector(model_name="driftmodel",
-                                   identifier="inputs",
-                                   feature_names=feature_names)
-
-    prediction_dc = ModelDataCollector("driftmodel",
-                                       identifier="predictions",
-                                       feature_names=["temperature"])
-
-
-def run(raw_data):
-    global inputs_dc, prediction_dc
-
-    try:
-        data = json.loads(raw_data)["data"]
-        data = pd.DataFrame(data)
-
-        # Remove the categorical features as the model expects OHE values
-        input_data = data.drop(categorical_features, axis=1)
-
-        result = model.predict(input_data)
-
-        # Collect the non-OHE dataframe
-        collected_df = data[feature_names]
-
-        inputs_dc.collect(collected_df.values)
-        prediction_dc.collect(result)
-        return result.tolist()
-    except Exception as e:
-        error = str(e)
-
-        print(error + time.strftime("%H:%M:%S"))
-        return error

contrib/gbdt/lightgbm/binary0.test

@@ -0,0 +1,500 @@
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+0	1.171	-0.962	0.521	0.841	-0.315	1.196	-0.744	-0.882	2.173	0.726	-1.305	1.377	1.107	0.643	-1.790	-1.264	0.000	1.257	0.222	0.817	0.000	0.862	0.911	0.987	0.846	1.293	0.899	0.756
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+1	0.765	-0.500	-0.603	1.843	-0.560	1.068	0.007	0.746	2.173	1.154	-0.017	1.329	0.000	1.165	1.791	-1.585	0.000	1.116	0.441	-0.886	0.000	0.774	0.982	0.989	1.102	0.633	1.178	1.021
+1	1.407	1.293	-1.418	0.502	-1.527	2.005	-2.122	0.622	0.000	1.699	1.508	-0.649	2.215	1.665	0.748	-0.755	0.000	2.555	0.811	1.423	1.551	7.531	5.520	0.985	1.115	1.881	4.487	3.379
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contrib/gbdt/lightgbm/binary1.test

@@ -0,0 +1,500 @@
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+0	0.660	-0.017	-1.138	0.453	1.002	0.645	0.518	0.703	2.173	0.751	0.705	-0.592	2.215	0.744	-0.909	-1.596	0.000	0.410	-1.135	0.481	0.000	0.592	0.922	0.989	0.897	0.948	0.777	0.701
+1	0.718	0.518	0.225	1.710	-0.022	1.888	-0.424	1.092	0.000	4.134	0.185	-1.366	0.000	1.415	1.293	0.242	2.548	2.351	0.264	-0.057	3.102	0.830	1.630	0.976	1.215	0.890	1.422	1.215
+1	1.160	0.203	0.941	0.594	0.212	0.636	-0.556	0.679	2.173	1.089	-0.481	-1.008	1.107	1.245	-0.056	-1.357	0.000	0.587	1.007	0.056	0.000	1.106	0.901	0.987	0.786	1.224	0.914	0.837
+1	0.697	0.542	0.619	0.985	1.481	0.745	0.415	1.644	2.173	0.903	0.495	-0.958	2.215	1.165	1.195	0.346	0.000	1.067	-0.881	-0.264	0.000	0.830	1.025	0.987	0.690	0.863	0.894	0.867
+0	1.430	0.190	-0.700	0.246	0.518	1.302	0.660	-0.247	2.173	1.185	-0.539	1.504	0.000	1.976	-0.401	1.079	0.000	0.855	-0.958	-1.110	3.102	0.886	0.953	0.993	0.889	1.400	1.376	1.119
+1	1.122	-0.795	0.202	0.397	-1.553	0.597	-1.459	-0.734	2.173	0.522	1.044	1.027	2.215	0.783	-1.243	1.701	0.000	0.371	1.737	0.199	0.000	1.719	1.176	0.988	0.723	1.583	1.063	0.914
+0	1.153	0.526	1.236	0.266	0.001	1.139	-1.236	-0.585	2.173	1.337	-0.215	-1.356	2.215	1.780	1.129	0.902	0.000	1.608	-0.391	-0.161	0.000	1.441	1.633	0.990	1.838	1.516	1.635	1.373
+1	0.760	1.012	0.758	0.937	0.051	0.941	0.687	-1.247	2.173	1.288	-0.743	0.822	0.000	1.552	1.782	-1.533	0.000	0.767	1.349	0.168	0.000	0.716	0.862	0.988	0.595	0.359	0.697	0.623
+1	1.756	-1.469	1.395	1.345	-1.595	0.817	0.017	-0.741	2.173	0.483	-0.008	0.293	0.000	1.768	-0.663	0.438	1.274	1.202	-1.387	-0.222	0.000	1.022	1.058	0.992	1.407	1.427	1.356	1.133
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+0	0.515	-0.984	0.425	1.114	-0.439	1.999	0.818	1.561	0.000	1.407	0.009	-0.380	0.000	1.332	0.230	0.397	0.000	1.356	-0.616	-1.057	3.102	0.978	1.017	0.990	1.118	0.862	0.835	0.919
+1	1.368	-0.921	-0.866	0.842	-0.598	0.456	-1.176	1.219	1.087	0.419	-1.974	-0.819	0.000	0.791	-1.640	0.881	0.000	1.295	-0.782	0.442	3.102	0.945	0.761	0.974	0.915	0.535	0.733	0.651
+0	2.276	0.134	0.399	2.525	0.376	1.111	-1.078	-1.571	0.000	0.657	2.215	-0.900	0.000	1.183	-0.662	-0.508	2.548	1.436	-0.517	0.960	3.102	0.569	0.931	0.993	1.170	0.967	0.879	1.207
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+1	2.105	0.248	-0.797	0.530	0.206	1.957	-2.175	0.797	0.000	1.193	0.637	-1.646	2.215	0.881	1.111	-1.046	0.000	0.872	-0.185	1.085	1.551	0.986	1.343	1.151	1.069	0.714	2.063	1.951
+1	1.838	1.060	1.637	1.017	1.370	0.913	0.461	-0.609	1.087	0.766	-0.461	0.303	2.215	0.724	-0.061	0.886	0.000	0.941	1.123	-0.745	0.000	0.858	0.847	0.979	1.313	1.083	1.094	0.910
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+1	0.503	1.343	-0.031	1.134	-1.204	0.590	-0.309	0.174	2.173	0.408	2.372	-0.628	0.000	1.850	0.400	1.147	2.548	0.664	-0.458	-0.885	0.000	1.445	1.283	0.989	1.280	1.118	1.127	1.026
+0	1.873	0.258	0.103	2.491	0.530	1.678	0.644	-1.738	2.173	1.432	0.848	-1.340	0.000	0.621	1.323	-1.316	0.000	0.628	0.789	-0.206	1.551	0.426	0.802	1.125	0.688	1.079	1.338	1.239
+1	0.826	-0.732	1.587	0.582	-1.236	0.495	0.757	-0.741	2.173	0.940	1.474	0.354	2.215	0.474	1.055	-1.657	0.000	0.415	1.758	0.841	0.000	0.451	0.578	0.984	0.757	0.922	0.860	0.696
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+0	0.876	-0.463	-1.224	2.458	-1.689	1.007	-0.752	0.398	0.000	2.456	-1.285	-0.152	1.107	1.641	1.838	1.717	0.000	0.458	0.194	0.488	3.102	4.848	2.463	0.986	1.981	0.974	2.642	2.258
+1	0.384	-0.275	0.387	1.403	-0.994	0.620	-1.529	1.685	0.000	1.091	-1.644	1.078	0.000	0.781	-1.311	0.326	2.548	1.228	-0.728	-0.633	1.551	0.920	0.854	0.987	0.646	0.609	0.740	0.884
+0	0.318	-1.818	-1.008	0.977	1.268	0.457	2.451	-1.522	0.000	0.881	1.351	0.461	2.215	0.929	0.239	-0.380	2.548	0.382	-0.613	1.330	0.000	1.563	1.193	0.994	0.829	0.874	0.901	1.026
+1	0.612	-1.120	1.098	0.402	-0.480	0.818	0.188	1.511	0.000	0.800	-0.253	0.977	0.000	1.175	0.271	-1.289	1.274	2.531	0.226	-0.409	3.102	0.889	0.947	0.979	1.486	0.940	1.152	1.119
+1	0.587	-0.737	-0.228	0.970	1.119	0.823	0.184	1.594	0.000	1.104	0.301	-0.818	2.215	0.819	0.712	-0.560	0.000	2.240	-0.419	0.340	3.102	1.445	1.103	0.988	0.715	1.363	1.019	0.926
+0	1.030	-0.694	-1.638	0.893	-1.074	1.160	-0.766	0.485	0.000	1.632	-0.698	-1.142	2.215	1.050	-1.092	0.952	0.000	1.475	0.286	0.125	3.102	0.914	1.075	0.982	0.732	1.493	1.219	1.079
+1	2.142	0.617	1.517	0.387	-0.862	0.345	1.203	-1.014	2.173	0.609	1.092	0.275	0.000	1.331	0.582	-0.183	2.548	0.557	1.540	-1.642	0.000	0.801	0.737	1.060	0.715	0.626	0.749	0.674
+0	1.076	0.240	-0.246	0.871	-1.241	0.496	0.282	0.746	2.173	1.095	-0.648	1.100	2.215	0.446	-1.756	0.764	0.000	0.434	0.788	-0.991	0.000	1.079	0.868	1.047	0.818	0.634	0.795	0.733
+0	1.400	0.901	-1.617	0.625	-0.163	0.661	-0.411	-1.616	2.173	0.685	0.524	0.425	0.000	0.881	-0.766	0.312	0.000	0.979	0.255	-0.667	3.102	0.898	1.105	1.253	0.730	0.716	0.738	0.795
+0	3.302	1.132	1.051	0.658	0.768	1.308	0.251	-0.374	1.087	1.673	0.015	-0.898	0.000	0.688	-0.535	1.363	1.274	0.871	1.325	-1.583	0.000	1.646	1.249	0.995	1.919	1.288	1.330	1.329
+0	1.757	0.202	0.750	0.767	-0.362	0.932	-1.033	-1.366	0.000	1.529	-1.012	-0.771	0.000	1.161	-0.287	0.059	0.000	2.185	1.147	1.099	3.102	0.795	0.529	1.354	1.144	1.491	1.319	1.161
+0	1.290	0.905	-1.711	1.017	-0.695	1.008	-1.038	0.693	2.173	1.202	-0.595	0.187	0.000	1.011	0.139	-1.607	0.000	0.789	-0.613	-1.041	3.102	1.304	0.895	1.259	1.866	0.955	1.211	1.200
+1	1.125	-0.004	1.694	0.373	0.329	0.978	0.640	-0.391	0.000	1.122	-0.376	1.521	2.215	0.432	2.413	-1.259	0.000	0.969	0.730	0.512	3.102	0.716	0.773	0.991	0.624	0.977	0.981	0.875
+0	1.081	0.861	1.252	1.621	1.474	1.293	0.600	0.630	0.000	1.991	-0.090	-0.675	2.215	0.861	1.105	-0.201	0.000	1.135	2.489	-1.659	0.000	1.089	0.657	0.991	2.179	0.412	1.334	1.071
+1	0.652	-0.294	1.241	1.034	0.490	1.033	0.551	-0.963	2.173	0.661	1.031	-1.654	2.215	1.376	-0.018	0.843	0.000	0.943	-0.329	-0.269	0.000	1.085	1.067	0.991	1.504	0.773	1.135	0.993
+1	1.408	-1.028	-1.018	0.252	-0.242	0.465	-0.364	-0.200	0.000	1.466	0.669	0.739	1.107	1.031	0.415	-1.468	2.548	0.457	-1.091	-1.722	0.000	0.771	0.811	0.979	1.459	1.204	1.041	0.866
+1	0.781	-1.143	-0.659	0.961	1.266	1.183	-0.686	0.119	2.173	1.126	-0.064	1.447	0.000	0.730	1.430	-1.535	0.000	1.601	0.513	1.658	0.000	0.871	1.345	1.184	1.058	0.620	1.107	0.978
+1	1.300	-0.616	1.032	0.751	-0.731	0.961	-0.716	1.592	0.000	2.079	-1.063	-0.271	2.215	0.475	0.518	1.695	1.274	0.395	-2.204	0.349	0.000	1.350	0.983	1.369	1.265	1.428	1.135	0.982
+1	0.833	0.809	1.657	1.637	1.019	0.705	1.077	-0.968	2.173	1.261	0.114	-0.298	1.107	1.032	0.017	0.236	0.000	0.640	-0.026	-1.598	0.000	0.894	0.982	0.981	1.250	1.054	1.018	0.853
+1	1.686	-1.090	-0.301	0.890	0.557	1.304	-0.284	-1.393	2.173	0.388	2.118	0.513	0.000	0.514	-0.015	0.891	0.000	0.460	0.547	0.627	3.102	0.942	0.524	1.186	1.528	0.889	1.015	1.122
+1	0.551	0.911	0.879	0.379	-0.796	1.154	-0.808	-0.966	0.000	1.168	-0.513	0.355	2.215	0.646	-1.309	0.773	0.000	0.544	-0.283	1.301	3.102	0.847	0.705	0.990	0.772	0.546	0.790	0.719
+1	1.597	0.793	-1.119	0.691	-1.455	0.370	0.337	1.354	0.000	0.646	-1.005	0.732	2.215	1.019	0.040	0.209	0.000	0.545	0.958	0.239	3.102	0.962	0.793	0.994	0.719	0.745	0.812	0.739
+0	1.033	-1.193	-0.452	0.247	0.970	0.503	-1.424	1.362	0.000	1.062	-0.416	-1.156	2.215	0.935	-0.023	0.555	2.548	0.410	-1.766	0.379	0.000	0.590	0.953	0.991	0.717	1.081	0.763	0.690
+1	0.859	-1.004	1.521	0.781	-0.993	0.677	0.643	-0.338	2.173	0.486	0.409	1.283	0.000	0.679	0.110	0.285	0.000	0.715	-0.735	-0.157	1.551	0.702	0.773	0.984	0.627	0.633	0.694	0.643
+0	0.612	-1.127	1.074	1.225	-0.426	0.927	-2.141	-0.473	0.000	1.290	-0.927	-1.085	2.215	1.183	1.981	-1.687	0.000	2.176	0.406	-1.581	0.000	0.945	0.651	1.170	0.895	1.604	1.179	1.142
+1	0.535	0.321	-1.095	0.281	-0.960	0.876	-0.709	-0.076	0.000	1.563	-0.666	1.536	2.215	0.773	-0.321	0.435	0.000	0.682	-0.801	-0.952	3.102	0.711	0.667	0.985	0.888	0.741	0.872	0.758
+1	0.745	1.586	1.578	0.863	-1.423	0.530	1.714	1.085	0.000	1.174	0.679	1.015	0.000	1.158	0.609	-1.186	2.548	1.851	0.832	-0.248	3.102	0.910	1.164	0.983	0.947	0.858	0.928	0.823
+0	0.677	-1.014	-1.648	1.455	1.461	0.596	-2.358	0.517	0.000	0.800	0.849	-0.743	2.215	1.024	-0.282	-1.004	0.000	1.846	-0.977	0.378	3.102	2.210	1.423	0.982	1.074	1.623	1.417	1.258
+1	0.815	-1.263	0.057	1.018	-0.208	0.339	-0.347	-1.646	2.173	1.223	0.600	-1.658	2.215	1.435	0.042	0.926	0.000	0.777	1.698	-0.698	0.000	1.022	1.058	1.000	0.784	0.477	0.886	0.836
+0	3.512	-1.094	-0.220	0.338	-0.328	1.962	-1.099	1.544	1.087	1.461	-1.305	-0.922	2.215	1.219	-1.289	0.400	0.000	0.731	0.155	1.249	0.000	1.173	1.366	0.993	2.259	2.000	1.626	1.349
+0	0.904	1.248	0.325	0.317	-1.624	0.685	-0.538	1.665	2.173	0.685	-2.145	-1.106	0.000	0.632	-1.460	1.017	0.000	1.085	-0.182	0.162	3.102	0.885	0.801	0.989	0.930	0.904	1.012	0.961

contrib/gbdt/lightgbm/lightgbm-example.ipynb

@@ -0,0 +1,270 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/contrib/gbdt/lightgbm/lightgbm-example.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Use LightGBM Estimator in Azure Machine Learning\n",
+        "In this notebook we will demonstrate how to run a training job using LightGBM Estimator. [LightGBM](https://lightgbm.readthedocs.io/en/latest/) is a gradient boosting framework that uses tree based learning algorithms. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prerequisites\n",
+        "This notebook uses azureml-contrib-gbdt package, if you don't already have the package, please install by uncommenting below cell."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#!pip install azureml-contrib-gbdt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace, Run, Experiment\n",
+        "import shutil, os\n",
+        "from azureml.widgets import RunDetails\n",
+        "from azureml.contrib.gbdt import LightGBM\n",
+        "from azureml.train.dnn import Mpi\n",
+        "from azureml.core.compute import AmlCompute, ComputeTarget\n",
+        "from azureml.core.compute_target import ComputeTargetException"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If you are using an AzureML Compute Instance, you are all set. Otherwise, go through the [configuration.ipynb](../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML Workspace"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Set up machine learning resources"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "\n",
+        "print('Workspace name: ' + ws.name, \n",
+        "      'Azure region: ' + ws.location, \n",
+        "      'Subscription id: ' + ws.subscription_id, \n",
+        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "cluster_vm_size = \"STANDARD_DS14_V2\"\n",
+        "cluster_min_nodes = 0\n",
+        "cluster_max_nodes = 20\n",
+        "cpu_cluster_name = 'TrainingCompute2' \n",
+        "\n",
+        "try:\n",
+        "    cpu_cluster = AmlCompute(ws, cpu_cluster_name)\n",
+        "    if cpu_cluster and type(cpu_cluster) is AmlCompute:\n",
+        "        print('found compute target: ' + cpu_cluster_name)\n",
+        "except ComputeTargetException:\n",
+        "    print('creating a new compute target...')\n",
+        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = cluster_vm_size, \n",
+        "                                                                vm_priority = 'lowpriority', \n",
+        "                                                                min_nodes = cluster_min_nodes, \n",
+        "                                                                max_nodes = cluster_max_nodes)\n",
+        "    cpu_cluster = ComputeTarget.create(ws, cpu_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",
+        "    cpu_cluster.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
+        "    \n",
+        "     # For a more detailed view of current Azure Machine Learning Compute  status, use get_status()\n",
+        "    print(cpu_cluster.get_status().serialize())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "From this point, you can either upload training data file directly or use Datastore for training data storage\n",
+        "## Upload training file from local"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "scripts_folder = \"scripts_folder\"\n",
+        "if not os.path.isdir(scripts_folder):\n",
+        "    os.mkdir(scripts_folder)\n",
+        "shutil.copy('./train.conf', os.path.join(scripts_folder, 'train.conf'))\n",
+        "shutil.copy('./binary0.train', os.path.join(scripts_folder, 'binary0.train'))\n",
+        "shutil.copy('./binary1.train', os.path.join(scripts_folder, 'binary1.train'))\n",
+        "shutil.copy('./binary0.test', os.path.join(scripts_folder, 'binary0.test'))\n",
+        "shutil.copy('./binary1.test', os.path.join(scripts_folder, 'binary1.test'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "training_data_list=[\"binary0.train\", \"binary1.train\"]\n",
+        "validation_data_list = [\"binary0.test\", \"binary1.test\"]\n",
+        "lgbm = LightGBM(source_directory=scripts_folder, \n",
+        "                compute_target=cpu_cluster, \n",
+        "                distributed_training=Mpi(),\n",
+        "                node_count=2,\n",
+        "                lightgbm_config='train.conf',\n",
+        "                data=training_data_list,\n",
+        "                valid=validation_data_list\n",
+        "               )\n",
+        "experiment_name = 'lightgbm-estimator-test'\n",
+        "experiment = Experiment(ws, name=experiment_name)\n",
+        "run = experiment.submit(lgbm, tags={\"test public docker image\": None})\n",
+        "RunDetails(run).show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Use data reference"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.datastore import Datastore\n",
+        "from azureml.data.data_reference import DataReference\n",
+        "datastore = ws.get_default_datastore()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "datastore.upload(src_dir='.',\n",
+        "                 target_path='.',\n",
+        "                 show_progress=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "training_data_list=[\"binary0.train\", \"binary1.train\"]\n",
+        "validation_data_list = [\"binary0.test\", \"binary1.test\"]\n",
+        "lgbm = LightGBM(source_directory='.', \n",
+        "                compute_target=cpu_cluster, \n",
+        "                distributed_training=Mpi(),\n",
+        "                node_count=2,\n",
+        "                inputs=[datastore.as_mount()],\n",
+        "                lightgbm_config='train.conf',\n",
+        "                data=training_data_list,\n",
+        "                valid=validation_data_list\n",
+        "               )\n",
+        "experiment_name = 'lightgbm-estimator-test'\n",
+        "experiment = Experiment(ws, name=experiment_name)\n",
+        "run = experiment.submit(lgbm, tags={\"use datastore.as_mount()\": None})\n",
+        "RunDetails(run).show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# uncomment below and run if compute resources are no longer needed\n",
+        "# cpu_cluster.delete() "
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "jingywa"
+      }
+    ],
+    "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": "3.6.9"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

contrib/gbdt/lightgbm/lightgbm-example.yml

@@ -0,0 +1,7 @@
+name: lightgbm-example
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-contrib-gbdt
+  - azureml-widgets
+  - azureml-core

contrib/gbdt/lightgbm/train.conf

@@ -0,0 +1,111 @@
+# task type, support train and predict
+task = train
+
+# boosting type, support gbdt for now, alias: boosting, boost
+boosting_type = gbdt
+
+# application type, support following application
+# regression , regression task
+# binary , binary classification task
+# lambdarank , lambdarank task
+# alias: application, app
+objective = binary
+
+# eval metrics, support multi metric, delimite by ',' , support following metrics
+# l1 
+# l2 , default metric for regression
+# ndcg , default metric for lambdarank
+# auc 
+# binary_logloss , default metric for binary
+# binary_error
+metric = binary_logloss,auc
+
+# frequence for metric output
+metric_freq = 1
+
+# true if need output metric for training data, alias: tranining_metric, train_metric
+is_training_metric = true
+
+# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. 
+max_bin = 255
+
+# training data
+# if exsting weight file, should name to "binary.train.weight"
+# alias: train_data, train
+data = binary.train
+
+# validation data, support multi validation data, separated by ','
+# if exsting weight file, should name to "binary.test.weight"
+# alias: valid, test, test_data, 
+valid_data = binary.test
+
+# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds
+num_trees = 100
+
+# shrinkage rate , alias: shrinkage_rate
+learning_rate = 0.1
+
+# number of leaves for one tree, alias: num_leaf
+num_leaves = 63
+
+# type of tree learner, support following types:
+# serial , single machine version
+# feature , use feature parallel to train
+# data , use data parallel to train
+# voting , use voting based parallel to train
+# alias: tree
+tree_learner = feature
+
+# number of threads for multi-threading. One thread will use one CPU, defalut is setted to #cpu. 
+# num_threads = 8
+
+# feature sub-sample, will random select 80% feature to train on each iteration 
+# alias: sub_feature
+feature_fraction = 0.8
+
+# Support bagging (data sub-sample), will perform bagging every 5 iterations
+bagging_freq = 5
+
+# Bagging farction, will random select 80% data on bagging
+# alias: sub_row
+bagging_fraction = 0.8
+
+# minimal number data for one leaf, use this to deal with over-fit
+# alias : min_data_per_leaf, min_data
+min_data_in_leaf = 50
+
+# minimal sum hessians for one leaf, use this to deal with over-fit
+min_sum_hessian_in_leaf = 5.0
+
+# save memory and faster speed for sparse feature, alias: is_sparse
+is_enable_sparse = true
+
+# when data is bigger than memory size, set this to true. otherwise set false will have faster speed
+# alias: two_round_loading, two_round
+use_two_round_loading = false
+
+# true if need to save data to binary file and application will auto load data from binary file next time
+# alias: is_save_binary, save_binary
+is_save_binary_file = false
+
+# output model file
+output_model = LightGBM_model.txt
+
+# support continuous train from trained gbdt model
+# input_model= trained_model.txt
+
+# output prediction file for predict task
+# output_result= prediction.txt
+
+# support continuous train from initial score file
+# input_init_score= init_score.txt
+
+
+# number of machines in parallel training, alias: num_machine
+num_machines = 2
+
+# local listening port in parallel training, alias: local_port
+local_listen_port = 12400
+
+# machines list file for parallel training, alias: mlist
+machine_list_file = mlist.txt

how-to-use-azureml/README.md

@@ -8,10 +8,9 @@ As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) not
 * [train-on-local](./training/train-on-local): Learn how to submit a run to local computer and use Azure ML managed run configuration.
 * [train-on-amlcompute](./training/train-on-amlcompute): Use a 1-n node Azure ML managed compute cluster for remote runs on Azure CPU or GPU infrastructure.
 * [train-on-remote-vm](./training/train-on-remote-vm): Use Data Science Virtual Machine as a target for remote runs.
-* [logging-api](./training/logging-api): Learn about the details of logging metrics to run history.
+* [logging-api](./track-and-monitor-experiments/logging-api): Learn about the details of logging metrics to run history.
 * [register-model-create-image-deploy-service](./deployment/register-model-create-image-deploy-service): Learn about the details of model management.
 * [production-deploy-to-aks](./deployment/production-deploy-to-aks) Deploy a model to production at scale on Azure Kubernetes Service.
-* [enable-data-collection-for-models-in-aks](./deployment/enable-data-collection-for-models-in-aks) Learn about data collection APIs for deployed model.
 * [enable-app-insights-in-production-service](./deployment/enable-app-insights-in-production-service) Learn how to use App Insights with production web service.
  
 Find quickstarts, end-to-end tutorials, and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/).

how-to-use-azureml/automated-machine-learning/README.md

@@ -1,290 +1,251 @@
-# Table of Contents
-1. [Automated ML Introduction](#introduction)
-1. [Setup using Azure Notebooks](#jupyter)
-1. [Setup using Azure Databricks](#databricks)
-1. [Setup using a Local Conda environment](#localconda)
-1. [Automated ML SDK Sample Notebooks](#samples)
-1. [Documentation](#documentation)
-1. [Running using python command](#pythoncommand)
-1. [Troubleshooting](#troubleshooting)
-
-<a name="introduction"></a>
-# Automated ML introduction
-Automated machine learning (automated ML) builds high quality machine learning models for you by automating model and hyperparameter selection. Bring a labelled dataset that you want to build a model for, automated ML will give you a high quality machine learning model that you can use for predictions.
-
-
-If you are new to Data Science, automated ML will help you get jumpstarted by simplifying machine learning model building. It abstracts you from needing to perform model selection, hyperparameter selection and in one step creates a high quality trained model for you to use.
-
-If you are an experienced data scientist, automated ML will help increase your productivity by intelligently performing the model and hyperparameter selection for your training and generates high quality models much quicker than manually specifying several combinations of the parameters and running training jobs. Automated ML provides visibility and access to all the training jobs and the performance characteristics of the models to help you further tune the pipeline if you desire.
-
-Below are the three execution environments supported by automated ML.
-
-
- <a name="jupyter"></a>
-## Setup using Azure Notebooks - Jupyter based notebooks in the Azure cloud
-
-1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
-[Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks.
-1. Follow the instructions in the [configuration](../../configuration.ipynb) notebook to create and connect to a workspace.
-1. Open one of the sample notebooks.
-
- <a name="databricks"></a>
-## Setup using Azure Databricks
-
-**NOTE**: Please create your Azure Databricks cluster as v4.x (high concurrency preferred) with **Python 3** (dropdown).
-**NOTE**: You should at least have contributor access to your Azure subcription to run the notebook.
-- Please remove the previous SDK version if there is any and install the latest SDK by installing **azureml-sdk[automl_databricks]** as a PyPi library in Azure Databricks workspace.
-- You can find the detail Readme instructions at [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks).
-- Download the sample notebook automl-databricks-local-01.ipynb from [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks) and import into the Azure databricks workspace.
-- Attach the notebook to the cluster.
-
-<a name="localconda"></a>
-## Setup using a Local Conda environment
-
-To run these notebook on your own notebook server, use these installation instructions.
-The instructions below will install everything you need and then start a Jupyter notebook.
-
-### 1. Install mini-conda from [here](https://conda.io/miniconda.html), choose 64-bit Python 3.7 or higher.
-- **Note**: if you already have conda installed, you can keep using it but it should be version 4.4.10 or later (as shown by: conda -V).  If you have a previous version installed, you can update it using the command: conda update conda.
-There's no need to install mini-conda specifically.
-
-### 2. Downloading the sample notebooks
-- Download the sample notebooks from [GitHub](https://github.com/Azure/MachineLearningNotebooks) as zip and extract the contents to a local directory.  The automated ML sample notebooks are in the "automated-machine-learning" folder.
-
-### 3. Setup a new conda environment
-The **automl_setup** script creates a new conda environment, installs the necessary packages, configures the widget and starts a jupyter notebook. It takes the conda environment name as an optional parameter.  The default conda environment name is azure_automl.  The exact command depends on the operating system.  See the specific sections below for Windows, Mac and Linux.  It can take about 10 minutes to execute.
-
-Packages installed by the **automl_setup** script:
-    <ul><li>python</li><li>nb_conda</li><li>matplotlib</li><li>numpy</li><li>cython</li><li>urllib3</li><li>scipy</li><li>scikit-learn</li><li>pandas</li><li>tensorflow</li><li>py-xgboost</li><li>azureml-sdk</li><li>azureml-widgets</li><li>pandas-ml</li></ul>
-
-For more details refer to the [automl_env.yml](./automl_env.yml)
-## Windows
-Start an **Anaconda Prompt** window, cd to the **how-to-use-azureml/automated-machine-learning** folder where the sample notebooks were extracted and then run:
-```
-automl_setup
-```
-## Mac
-Install "Command line developer tools" if it is not already installed (you can use the command: `xcode-select --install`).
-
-Start a Terminal windows, cd to the **how-to-use-azureml/automated-machine-learning** folder where the sample notebooks were extracted and then run:
-
-```
-bash automl_setup_mac.sh
-```
-
-## Linux
-cd to the **how-to-use-azureml/automated-machine-learning** folder where the sample notebooks were extracted and then run:
-
-```
-bash automl_setup_linux.sh
-```
-
-### 4. Running configuration.ipynb
-- Before running any samples you next need to run the configuration notebook. Click on [configuration](../../configuration.ipynb) notebook
-- Execute the cells in the notebook to Register Machine Learning Services Resource Provider and create a workspace. (*instructions in notebook*)
-
-### 5. Running Samples
-- Please make sure you use the Python [conda env:azure_automl] kernel when trying the sample Notebooks.
-- Follow the instructions in the individual notebooks to explore various features in automated ML.
-
-### 6. Starting jupyter notebook manually
-To start your Jupyter notebook manually, use:
-
-```
-conda activate azure_automl
-jupyter notebook
-```
-
-or on Mac or Linux:
-
-```
-source activate azure_automl
-jupyter notebook
-```
-
-<a name="samples"></a>
-# Automated ML SDK Sample Notebooks
-
-- [auto-ml-classification.ipynb](classification/auto-ml-classification.ipynb)
-    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
-    - Simple example of using automated ML for classification
-    - Uses local compute for training
-
-- [auto-ml-regression.ipynb](regression/auto-ml-regression.ipynb)
-    - Dataset: scikit learn's [diabetes dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html)
-    - Simple example of using automated ML for regression
-    - Uses local compute for training
-
-- [auto-ml-remote-amlcompute.ipynb](remote-amlcompute/auto-ml-remote-amlcompute.ipynb)
-    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
-    - Example of using automated ML for classification using remote AmlCompute for training
-    - Parallel execution of iterations
-    - Async tracking of progress
-    - Cancelling individual iterations or entire run
-    - Retrieving models for any iteration or logged metric
-    - Specify automated ML settings as kwargs
-
-- [auto-ml-missing-data-blacklist-early-termination.ipynb](missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.ipynb)
-    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
-    - Blacklist certain pipelines
-    - Specify a target metrics to indicate stopping criteria
-    - Handling Missing Data in the input
-
-- [auto-ml-sparse-data-train-test-split.ipynb](sparse-data-train-test-split/auto-ml-sparse-data-train-test-split.ipynb)
-    - Dataset: Scikit learn's [20newsgroup](http://scikit-learn.org/stable/datasets/twenty_newsgroups.html)
-    - Handle sparse datasets
-    - Specify custom train and validation set
-
-- [auto-ml-exploring-previous-runs.ipynb](exploring-previous-runs/auto-ml-exploring-previous-runs.ipynb)
-    - List all projects for the workspace
-    - List all automated ML Runs for a given project
-    - Get details for a automated ML Run. (automated ML settings, run widget & all metrics)
-    - Download fitted pipeline for any iteration
-
-- [auto-ml-classification-with-deployment.ipynb](classification-with-deployment/auto-ml-classification-with-deployment.ipynb)
-    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
-    - Simple example of using automated ML for classification
-    - Registering the model
-    - Creating Image and creating aci service
-    - Testing the aci service
-
-- [auto-ml-sample-weight.ipynb](sample-weight/auto-ml-sample-weight.ipynb)
-    - How to specifying sample_weight
-    - The difference that it makes to test results
-
-- [auto-ml-subsampling-local.ipynb](subsampling/auto-ml-subsampling-local.ipynb)
-    - How to enable subsampling
-
-- [auto-ml-dataprep.ipynb](dataprep/auto-ml-dataprep.ipynb)
-    - Using DataPrep for reading data
-
-- [auto-ml-dataprep-remote-execution.ipynb](dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb)
-    - Using DataPrep for reading data with remote execution
-
-- [auto-ml-classification-with-whitelisting.ipynb](classification-with-whitelisting/auto-ml-classification-with-whitelisting.ipynb)
-    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
-    - Simple example of using automated ML for classification with whitelisting tensorflow models.
-    - Uses local compute for training
-
-- [auto-ml-forecasting-energy-demand.ipynb](forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb)
-    - Dataset: [NYC energy demand data](forecasting-a/nyc_energy.csv)
-    - Example of using automated ML for training a forecasting model
-
-- [auto-ml-forecasting-orange-juice-sales.ipynb](forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb)
-    - Dataset: [Dominick's grocery sales of orange juice](forecasting-b/dominicks_OJ.csv)
-    - Example of training an automated ML forecasting model on multiple time-series
-
-- [auto-ml-classification-with-onnx.ipynb](classification-with-onnx/auto-ml-classification-with-onnx.ipynb)
-    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
-    - Simple example of using automated ML for classification with ONNX models
-    - Uses local compute for training
-
-- [auto-ml-bank-marketing-subscribers-with-deployment.ipynb](bank-marketing-subscribers-with-deployment/auto-ml-bank-marketing-with-deployment.ipynb)
-    - Dataset: UCI's [bank marketing dataset](https://www.kaggle.com/janiobachmann/bank-marketing-dataset)
-    - Simple example of using automated ML for classification to predict term deposit subscriptions for a bank
-    - Uses azure compute for training
-
-- [auto-ml-creditcard-with-deployment.ipynb](credit-card-fraud-detection-with-deployment/auto-ml-creditcard-with-deployment.ipynb)
-    - Dataset: Kaggle's [credit card fraud detection dataset](https://www.kaggle.com/mlg-ulb/creditcardfraud)
-    - Simple example of using automated ML for classification to fraudulent credit card transactions
-    - Uses azure compute for training
-
-- [auto-ml-hardware-performance-with-deployment.ipynb](hardware-performance-prediction-with-deployment/auto-ml-hardware-performance-with-deployment.ipynb)
-    - Dataset: UCI's [computer hardware dataset](https://archive.ics.uci.edu/ml/datasets/Computer+Hardware)
-    - Simple example of using automated ML for regression to predict the performance of certain combinations of hardware components
-    - Uses azure compute for training
-
-- [auto-ml-concrete-strength-with-deployment.ipynb](predicting-concrete-strength-with-deployment/auto-ml-concrete-strength-with-deployment.ipynb)
-    - Dataset: UCI's [concrete compressive strength dataset](https://www.kaggle.com/pavanraj159/concrete-compressive-strength-data-set)
-    - Simple example of using automated ML for regression to predict the strength  predict the compressive strength of concrete based off of different ingredient combinations and quantities of those ingredients
-    - Uses azure compute for training
-
-<a name="documentation"></a>
-See [Configure automated machine learning experiments](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-auto-train) to learn how more about the the settings and features available for automated machine learning experiments.
-
-<a name="pythoncommand"></a>
-# Running using python command
-Jupyter notebook provides a File / Download as / Python (.py) option for saving the notebook as a Python file.
-You can then run this file using the python command.
-However, on Windows the file needs to be modified before it can be run.
-The following condition must be added to the main code in the file:
-
-    if __name__ == "__main__":
-
-The main code of the file must be indented so that it is under this condition.
-
-<a name="troubleshooting"></a>
-# Troubleshooting
-## automl_setup fails
-1. On Windows, make sure that you are running automl_setup from an Anconda Prompt window rather than a regular cmd window.  You can launch the "Anaconda Prompt" window by hitting the Start button and typing "Anaconda Prompt".  If you don't see the application "Anaconda Prompt", you might not have conda or mini conda installed.  In that case, you can install it [here](https://conda.io/miniconda.html)
-2. Check that you have conda 64-bit installed rather than 32-bit.  You can check this with the command `conda info`.  The `platform` should be `win-64` for Windows or `osx-64` for Mac.
-3. Check that you have conda 4.4.10 or later.  You can check the version with the command `conda -V`.  If you have a previous version installed, you can update it using the command: `conda update conda`.
-4. On Linux, if the error is `gcc: error trying to exec 'cc1plus': execvp: No such file or directory`, install build essentials using the command `sudo apt-get install build-essential`.
-5. Pass a new name as the first parameter to automl_setup so that it creates a new conda environment. You can view existing conda environments using `conda env list` and remove them with `conda env remove -n <environmentname>`. 
-
-## automl_setup_linux.sh fails
-If automl_setup_linux.sh fails on Ubuntu Linux with the error: `unable to execute 'gcc': No such file or directory`
-1. Make sure that outbound ports 53 and 80 are enabled.  On an Azure VM, you can do this from the Azure Portal by selecting the VM and clicking on Networking.
-2. Run the command: `sudo apt-get update`
-3. Run the command: `sudo apt-get install build-essential --fix-missing`
-4. Run `automl_setup_linux.sh` again.
-
-## configuration.ipynb fails
-1) For local conda, make sure that you have susccessfully run automl_setup first.
-2) Check that the subscription_id is correct.  You can find the subscription_id in the Azure Portal by selecting All Service and then Subscriptions. The characters "<" and ">" should not be included in the subscription_id value.  For example, `subscription_id = "12345678-90ab-1234-5678-1234567890abcd"` has the valid format.
-3) Check that you have Contributor or Owner access to the Subscription.
-4) Check that the region is one of the supported regions: `eastus2`, `eastus`, `westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`
-5) Check that you have access to the region using the Azure Portal.
-
-## workspace.from_config fails
-If the call `ws = Workspace.from_config()` fails:
-1) Make sure that you have run the `configuration.ipynb` notebook successfully.
-2) If you are running a notebook from a folder that is not under the folder where you ran `configuration.ipynb`, copy the folder aml_config and the file config.json that it contains to the new folder.  Workspace.from_config reads the config.json for the notebook folder or it parent folder.
-3) If you are switching to a new subscription, resource group, workspace or region, make sure that you run the `configuration.ipynb` notebook again.  Changing config.json directly will only work if the workspace already exists in the specified resource group under the specified subscription.
-4) If you want to change the region, please change the workspace, resource group or subscription.  `Workspace.create` will not create or update a workspace if it already exists, even if the region specified is different.
-
-## Sample notebook fails
-If a sample notebook fails with an error that property, method or library does not exist:
-1) Check that you have selected correct kernel in jupyter notebook.  The kernel is displayed in the top right of the notebook page.  It can be changed using the `Kernel | Change Kernel` menu option.  For Azure Notebooks, it should be `Python 3.6`.  For local conda environments, it should be the conda envioronment name that you specified in automl_setup.  The default is azure_automl.  Note that the kernel is saved as part of the notebook.  So, if you switch to a new conda environment, you will have to select the new kernel in the notebook.
-2) Check that the notebook is for the SDK version that you are using.  You can check the SDK version by executing `azureml.core.VERSION` in a jupyter notebook cell.  You can download previous version of the sample notebooks from GitHub by clicking the `Branch` button, selecting the `Tags` tab and then selecting the version.
-
-## Numpy import fails on Windows
-Some Windows environments see an error loading numpy with the latest Python version 3.6.8.  If you see this issue, try with Python version 3.6.7.
-
-## Numpy import fails
-Check the tensorflow version in the automated ml conda environment. Supported versions are < 1.13. Uninstall tensorflow from the environment if version is >= 1.13
-You may check the version of tensorflow and uninstall as follows
-1) start a command shell, activate conda environment where automated ml packages are installed
-2) enter `pip freeze` and look for `tensorflow` , if found, the version listed should be < 1.13 
-3) If the listed version is a not a supported version,  `pip uninstall tensorflow` in the command shell and enter y for confirmation. 
-
-## Remote run: DsvmCompute.create fails 
-There are several reasons why the DsvmCompute.create can fail.  The reason is usually in the error message but you have to look at the end of the error message for the detailed reason.  Some common reasons are:
-1) `Compute name is invalid, it should start with a letter, be between 2 and 16 character, and only include letters (a-zA-Z), numbers (0-9) and \'-\'.`  Note that underscore is not allowed in the name.
-2) `The requested VM size xxxxx is not available in the current region.`  You can select a different region or vm_size. 
-
-## Remote run: Unable to establish SSH connection
-Automated ML uses the SSH protocol to communicate with remote DSVMs.  This defaults to port 22.  Possible causes for this error are:
-1) The DSVM is not ready for SSH connections.  When DSVM creation completes, the DSVM might still not be ready to acceept SSH connections.  The sample notebooks have a one minute delay to allow for this.
-2) Your Azure Subscription may restrict the IP address ranges that can access the DSVM on port 22.  You can check this in the Azure Portal by selecting the Virtual Machine and then clicking Networking.  The Virtual Machine name is the name that you provided in the notebook plus 10 alpha numeric characters to make the name unique.  The Inbound Port Rules define what can access the VM on specific ports.  Note that there is a priority priority order.  So, a Deny entry with a low priority number will override a Allow entry with a higher priority number.
-
-## Remote run: setup iteration fails
-This is often an issue with the `get_data` method.
-1) Check that the `get_data` method is valid by running it locally.
-2) Make sure that `get_data` isn't referring to any local files.  `get_data` is executed on the remote DSVM.  So, it doesn't have direct access to local data files.  Instead you can store the data files with DataStore.  See [auto-ml-remote-execution-with-datastore.ipynb](remote-execution-with-datastore/auto-ml-remote-execution-with-datastore.ipynb)
-3) You can get to the error log for the setup iteration by clicking the `Click here to see the run in Azure portal` link, click `Back to Experiment`, click on the highest run number and then click on Logs.
-
-## Remote run: disk full
-Automated ML creates files under /tmp/azureml_runs for each iteration that it runs.  It creates a folder with the iteration id.  For example: AutoML_9a038a18-77cc-48f1-80fb-65abdbc33abe_93.  Under this, there is a azureml-logs folder, which contains logs.  If you run too many iterations on the same DSVM, these files can fill the disk.
-You can delete the files under /tmp/azureml_runs or just delete the VM and create a new one.
-If your get_data downloads files, make sure the delete them or they can use disk space as well.
-When using DataStore, it is good to specify an absolute path for the files so that they are downloaded just once.  If you specify a relative path, it will download a file for each iteration.
-
-## Remote run: Iterations fail and the log contains "MemoryError"
-This can be caused by insufficient memory on the DSVM.  Automated ML loads all training data into memory.  So, the available memory should be more than the training data size.
-If you are using a remote DSVM, memory is needed for each concurrent iteration.  The max_concurrent_iterations setting specifies the maximum concurrent iterations.  For example, if the training data size is 8Gb and max_concurrent_iterations is set to 10, the minimum memory required is at least 80Gb.
-To resolve this issue, allocate a DSVM with more memory or reduce the value specified for max_concurrent_iterations.
-
-## Remote run: Iterations show as "Not Responding" in the RunDetails widget.
-This can be caused by too many concurrent iterations for a remote DSVM.  Each concurrent iteration usually takes 100% of a core when it is running.  Some iterations can use multiple cores.  So, the max_concurrent_iterations setting should always be less than the number of cores of the DSVM.
-To resolve this issue, try reducing the value specified for the max_concurrent_iterations setting.
+# Table of Contents
+1. [Automated ML Introduction](#introduction)
+1. [Setup using Azure Notebooks](#jupyter)
+1. [Setup using Azure Databricks](#databricks)
+1. [Setup using a Local Conda environment](#localconda)
+1. [Automated ML SDK Sample Notebooks](#samples)
+1. [Documentation](#documentation)
+1. [Running using python command](#pythoncommand)
+1. [Troubleshooting](#troubleshooting)
+
+<a name="introduction"></a>
+# Automated ML introduction
+Automated machine learning (automated ML) builds high quality machine learning models for you by automating model and hyperparameter selection. Bring a labelled dataset that you want to build a model for, automated ML will give you a high quality machine learning model that you can use for predictions.
+
+
+If you are new to Data Science, automated ML will help you get jumpstarted by simplifying machine learning model building. It abstracts you from needing to perform model selection, hyperparameter selection and in one step creates a high quality trained model for you to use.
+
+If you are an experienced data scientist, automated ML will help increase your productivity by intelligently performing the model and hyperparameter selection for your training and generates high quality models much quicker than manually specifying several combinations of the parameters and running training jobs. Automated ML provides visibility and access to all the training jobs and the performance characteristics of the models to help you further tune the pipeline if you desire.
+
+Below are the three execution environments supported by automated ML.
+
+
+ <a name="jupyter"></a>
+## Setup using Notebook VMs - Jupyter based notebooks from a Azure VM
+
+1. Open the [ML Azure portal](https://ml.azure.com)
+1. Select Compute
+1. Select Notebook VMs
+1. Click New
+1. Type a name for the Vm and select a VM type
+1. Click Create
+
+<a name="localconda"></a>
+## Setup using a Local Conda environment
+
+To run these notebook on your own notebook server, use these installation instructions.
+The instructions below will install everything you need and then start a Jupyter notebook.
+
+### 1. Install mini-conda from [here](https://conda.io/miniconda.html), choose 64-bit Python 3.7 or higher.
+- **Note**: if you already have conda installed, you can keep using it but it should be version 4.4.10 or later (as shown by: conda -V).  If you have a previous version installed, you can update it using the command: conda update conda.
+There's no need to install mini-conda specifically.
+
+### 2. Downloading the sample notebooks
+- Download the sample notebooks from [GitHub](https://github.com/Azure/MachineLearningNotebooks) as zip and extract the contents to a local directory.  The automated ML sample notebooks are in the "automated-machine-learning" folder.
+
+### 3. Setup a new conda environment
+The **automl_setup** script creates a new conda environment, installs the necessary packages, configures the widget and starts a jupyter notebook. It takes the conda environment name as an optional parameter.  The default conda environment name is azure_automl.  The exact command depends on the operating system.  See the specific sections below for Windows, Mac and Linux.  It can take about 10 minutes to execute.
+
+Packages installed by the **automl_setup** script:
+    <ul><li>python</li><li>nb_conda</li><li>matplotlib</li><li>numpy</li><li>cython</li><li>urllib3</li><li>scipy</li><li>scikit-learn</li><li>pandas</li><li>tensorflow</li><li>py-xgboost</li><li>azureml-sdk</li><li>azureml-widgets</li><li>pandas-ml</li></ul>
+
+For more details refer to the [automl_env.yml](./automl_env.yml)
+## Windows
+Start an **Anaconda Prompt** window, cd to the **how-to-use-azureml/automated-machine-learning** folder where the sample notebooks were extracted and then run:
+```
+automl_setup
+```
+## Mac
+Install "Command line developer tools" if it is not already installed (you can use the command: `xcode-select --install`).
+
+Start a Terminal windows, cd to the **how-to-use-azureml/automated-machine-learning** folder where the sample notebooks were extracted and then run:
+
+```
+bash automl_setup_mac.sh
+```
+
+## Linux
+cd to the **how-to-use-azureml/automated-machine-learning** folder where the sample notebooks were extracted and then run:
+
+```
+bash automl_setup_linux.sh
+```
+
+### 4. Running configuration.ipynb
+- Before running any samples you next need to run the configuration notebook. Click on [configuration](../../configuration.ipynb) notebook
+- Execute the cells in the notebook to Register Machine Learning Services Resource Provider and create a workspace. (*instructions in notebook*)
+
+### 5. Running Samples
+- Please make sure you use the Python [conda env:azure_automl] kernel when trying the sample Notebooks.
+- Follow the instructions in the individual notebooks to explore various features in automated ML.
+
+### 6. Starting jupyter notebook manually
+To start your Jupyter notebook manually, use:
+
+```
+conda activate azure_automl
+jupyter notebook
+```
+
+or on Mac or Linux:
+
+```
+source activate azure_automl
+jupyter notebook
+```
+
+ <a name="databricks"></a>
+## Setup using Azure Databricks
+
+**NOTE**: Please create your Azure Databricks cluster as v6.0 (high concurrency preferred) with **Python 3** (dropdown).
+**NOTE**: You should at least have contributor access to your Azure subcription to run the notebook.
+- Please remove the previous SDK version if there is any and install the latest SDK by installing **azureml-sdk[automl]** as a PyPi library in Azure Databricks workspace.
+- You can find the detail Readme instructions at [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks).
+- Download the sample notebook automl-databricks-local-01.ipynb from [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks) and import into the Azure databricks workspace.
+- Attach the notebook to the cluster.
+
+<a name="samples"></a>
+# Automated ML SDK Sample Notebooks
+
+- [auto-ml-classification-credit-card-fraud.ipynb](classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb)
+    - Dataset: Kaggle's [credit card fraud detection dataset](https://www.kaggle.com/mlg-ulb/creditcardfraud)
+    - Simple example of using automated ML for classification to fraudulent credit card transactions
+    - Uses azure compute for training
+
+- [auto-ml-regression.ipynb](regression/auto-ml-regression.ipynb)
+    - Dataset: Hardware Performance Dataset
+    - Simple example of using automated ML for regression
+    - Uses azure compute for training
+
+- [auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb](regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb)
+    - Dataset: Hardware Performance Dataset
+    - Shows featurization and excplanation
+    - Uses azure compute for training
+
+- [auto-ml-forecasting-energy-demand.ipynb](forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb)
+    - Dataset: [NYC energy demand data](forecasting-a/nyc_energy.csv)
+    - Example of using automated ML for training a forecasting model
+
+- [auto-ml-classification-credit-card-fraud-local.ipynb](local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb)
+    - Dataset: Kaggle's [credit card fraud detection dataset](https://www.kaggle.com/mlg-ulb/creditcardfraud)
+    - Simple example of using automated ML for classification to fraudulent credit card transactions
+    - Uses local compute for training
+
+- [auto-ml-classification-bank-marketing-all-features.ipynb](classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb)
+    - Dataset: UCI's [bank marketing dataset](https://www.kaggle.com/janiobachmann/bank-marketing-dataset)
+    - Simple example of using automated ML for classification to predict term deposit subscriptions for a bank
+    - Uses azure compute for training
+
+- [auto-ml-forecasting-orange-juice-sales.ipynb](forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb)
+    - Dataset: [Dominick's grocery sales of orange juice](forecasting-b/dominicks_OJ.csv)
+    - Example of training an automated ML forecasting model on multiple time-series
+
+- [auto-ml-forecasting-bike-share.ipynb](forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb)
+    - Dataset: forecasting for a bike-sharing
+    - Example of training an automated ML forecasting model on multiple time-series
+
+- [automl-forecasting-function.ipynb](forecasting-high-frequency/automl-forecasting-function.ipynb)
+    - Example of training an automated ML forecasting model on multiple time-series
+
+- [auto-ml-forecasting-beer-remote.ipynb](forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb)
+    - Example of training an automated ML forecasting model on multiple time-series
+    - Beer Production Forecasting
+
+- [auto-ml-continuous-retraining.ipynb](continuous-retraining/auto-ml-continuous-retraining.ipynb)
+    - Continous retraining using Pipelines and Time-Series TabularDataset
+
+- [auto-ml-classification-text-dnn.ipynb](classification-text-dnn/auto-ml-classification-text-dnn.ipynb)
+    - Classification with text data using deep learning in AutoML
+    - AutoML highlights here include using deep neural networks (DNNs) to create embedded features from text data.
+    - Depending on the compute cluster the user provides, AutoML tried out Bidirectional Encoder Representations from Transformers (BERT) when a GPU compute is used.
+    - Bidirectional Long-Short Term neural network (BiLSTM) when a CPU compute is used, thereby optimizing the choice of DNN for the uesr's setup.
+
+<a name="documentation"></a>
+See [Configure automated machine learning experiments](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-auto-train) to learn how more about the the settings and features available for automated machine learning experiments.
+
+<a name="pythoncommand"></a>
+# Running using python command
+Jupyter notebook provides a File / Download as / Python (.py) option for saving the notebook as a Python file.
+You can then run this file using the python command.
+However, on Windows the file needs to be modified before it can be run.
+The following condition must be added to the main code in the file:
+
+    if __name__ == "__main__":
+
+The main code of the file must be indented so that it is under this condition.
+
+<a name="troubleshooting"></a>
+# Troubleshooting
+## automl_setup fails
+1. On Windows, make sure that you are running automl_setup from an Anconda Prompt window rather than a regular cmd window.  You can launch the "Anaconda Prompt" window by hitting the Start button and typing "Anaconda Prompt".  If you don't see the application "Anaconda Prompt", you might not have conda or mini conda installed.  In that case, you can install it [here](https://conda.io/miniconda.html)
+2. Check that you have conda 64-bit installed rather than 32-bit.  You can check this with the command `conda info`.  The `platform` should be `win-64` for Windows or `osx-64` for Mac.
+3. Check that you have conda 4.4.10 or later.  You can check the version with the command `conda -V`.  If you have a previous version installed, you can update it using the command: `conda update conda`.
+4. On Linux, if the error is `gcc: error trying to exec 'cc1plus': execvp: No such file or directory`, install build essentials using the command `sudo apt-get install build-essential`.
+5. Pass a new name as the first parameter to automl_setup so that it creates a new conda environment. You can view existing conda environments using `conda env list` and remove them with `conda env remove -n <environmentname>`.
+
+## automl_setup_linux.sh fails
+If automl_setup_linux.sh fails on Ubuntu Linux with the error: `unable to execute 'gcc': No such file or directory`
+1. Make sure that outbound ports 53 and 80 are enabled.  On an Azure VM, you can do this from the Azure Portal by selecting the VM and clicking on Networking.
+2. Run the command: `sudo apt-get update`
+3. Run the command: `sudo apt-get install build-essential --fix-missing`
+4. Run `automl_setup_linux.sh` again.
+
+## configuration.ipynb fails
+1) For local conda, make sure that you have susccessfully run automl_setup first.
+2) Check that the subscription_id is correct.  You can find the subscription_id in the Azure Portal by selecting All Service and then Subscriptions. The characters "<" and ">" should not be included in the subscription_id value.  For example, `subscription_id = "12345678-90ab-1234-5678-1234567890abcd"` has the valid format.
+3) Check that you have Contributor or Owner access to the Subscription.
+4) Check that the region is one of the supported regions: `eastus2`, `eastus`, `westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`
+5) Check that you have access to the region using the Azure Portal.
+
+## workspace.from_config fails
+If the call `ws = Workspace.from_config()` fails:
+1) Make sure that you have run the `configuration.ipynb` notebook successfully.
+2) If you are running a notebook from a folder that is not under the folder where you ran `configuration.ipynb`, copy the folder aml_config and the file config.json that it contains to the new folder.  Workspace.from_config reads the config.json for the notebook folder or it parent folder.
+3) If you are switching to a new subscription, resource group, workspace or region, make sure that you run the `configuration.ipynb` notebook again.  Changing config.json directly will only work if the workspace already exists in the specified resource group under the specified subscription.
+4) If you want to change the region, please change the workspace, resource group or subscription.  `Workspace.create` will not create or update a workspace if it already exists, even if the region specified is different.
+
+## Sample notebook fails
+If a sample notebook fails with an error that property, method or library does not exist:
+1) Check that you have selected correct kernel in jupyter notebook.  The kernel is displayed in the top right of the notebook page.  It can be changed using the `Kernel | Change Kernel` menu option.  For Azure Notebooks, it should be `Python 3.6`.  For local conda environments, it should be the conda envioronment name that you specified in automl_setup.  The default is azure_automl.  Note that the kernel is saved as part of the notebook.  So, if you switch to a new conda environment, you will have to select the new kernel in the notebook.
+2) Check that the notebook is for the SDK version that you are using.  You can check the SDK version by executing `azureml.core.VERSION` in a jupyter notebook cell.  You can download previous version of the sample notebooks from GitHub by clicking the `Branch` button, selecting the `Tags` tab and then selecting the version.
+
+## Numpy import fails on Windows
+Some Windows environments see an error loading numpy with the latest Python version 3.6.8.  If you see this issue, try with Python version 3.6.7.
+
+## Numpy import fails
+Check the tensorflow version in the automated ml conda environment. Supported versions are < 1.13. Uninstall tensorflow from the environment if version is >= 1.13
+You may check the version of tensorflow and uninstall as follows
+1) start a command shell, activate conda environment where automated ml packages are installed
+2) enter `pip freeze` and look for `tensorflow` , if found, the version listed should be < 1.13
+3) If the listed version is a not a supported version,  `pip uninstall tensorflow` in the command shell and enter y for confirmation.
+
+## Remote run: DsvmCompute.create fails
+There are several reasons why the DsvmCompute.create can fail.  The reason is usually in the error message but you have to look at the end of the error message for the detailed reason.  Some common reasons are:
+1) `Compute name is invalid, it should start with a letter, be between 2 and 16 character, and only include letters (a-zA-Z), numbers (0-9) and \'-\'.`  Note that underscore is not allowed in the name.
+2) `The requested VM size xxxxx is not available in the current region.`  You can select a different region or vm_size.
+
+## Remote run: Unable to establish SSH connection
+Automated ML uses the SSH protocol to communicate with remote DSVMs.  This defaults to port 22.  Possible causes for this error are:
+1) The DSVM is not ready for SSH connections.  When DSVM creation completes, the DSVM might still not be ready to acceept SSH connections.  The sample notebooks have a one minute delay to allow for this.
+2) Your Azure Subscription may restrict the IP address ranges that can access the DSVM on port 22.  You can check this in the Azure Portal by selecting the Virtual Machine and then clicking Networking.  The Virtual Machine name is the name that you provided in the notebook plus 10 alpha numeric characters to make the name unique.  The Inbound Port Rules define what can access the VM on specific ports.  Note that there is a priority priority order.  So, a Deny entry with a low priority number will override a Allow entry with a higher priority number.
+
+## Remote run: setup iteration fails
+This is often an issue with the `get_data` method.
+1) Check that the `get_data` method is valid by running it locally.
+2) Make sure that `get_data` isn't referring to any local files.  `get_data` is executed on the remote DSVM.  So, it doesn't have direct access to local data files.  Instead you can store the data files with DataStore.  See [auto-ml-remote-execution-with-datastore.ipynb](remote-execution-with-datastore/auto-ml-remote-execution-with-datastore.ipynb)
+3) You can get to the error log for the setup iteration by clicking the `Click here to see the run in Azure portal` link, click `Back to Experiment`, click on the highest run number and then click on Logs.
+
+## Remote run: disk full
+Automated ML creates files under /tmp/azureml_runs for each iteration that it runs.  It creates a folder with the iteration id.  For example: AutoML_9a038a18-77cc-48f1-80fb-65abdbc33abe_93.  Under this, there is a azureml-logs folder, which contains logs.  If you run too many iterations on the same DSVM, these files can fill the disk.
+You can delete the files under /tmp/azureml_runs or just delete the VM and create a new one.
+If your get_data downloads files, make sure the delete them or they can use disk space as well.
+When using DataStore, it is good to specify an absolute path for the files so that they are downloaded just once.  If you specify a relative path, it will download a file for each iteration.
+
+## Remote run: Iterations fail and the log contains "MemoryError"
+This can be caused by insufficient memory on the DSVM.  Automated ML loads all training data into memory.  So, the available memory should be more than the training data size.
+If you are using a remote DSVM, memory is needed for each concurrent iteration.  The max_concurrent_iterations setting specifies the maximum concurrent iterations.  For example, if the training data size is 8Gb and max_concurrent_iterations is set to 10, the minimum memory required is at least 80Gb.
+To resolve this issue, allocate a DSVM with more memory or reduce the value specified for max_concurrent_iterations.
+
+## Remote run: Iterations show as "Not Responding" in the RunDetails widget.
+This can be caused by too many concurrent iterations for a remote DSVM.  Each concurrent iteration usually takes 100% of a core when it is running.  Some iterations can use multiple cores.  So, the max_concurrent_iterations setting should always be less than the number of cores of the DSVM.
+To resolve this issue, try reducing the value specified for the max_concurrent_iterations setting.

how-to-use-azureml/automated-machine-learning/automl_env.yml

@@ -1,22 +1,38 @@
-name: azure_automl
-dependencies:
-  # The python interpreter version.
-  # Currently Azure ML only supports 3.5.2 and later.
-- pip
-- python>=3.5.2,<3.6.8
-- nb_conda
-- matplotlib==2.1.0
-- numpy>=1.11.0,<=1.16.2
-- cython
-- urllib3<1.24
-- scipy>=1.0.0,<=1.1.0
-- scikit-learn>=0.19.0,<=0.20.3
-- pandas>=0.22.0,<=0.23.4
-- py-xgboost<=0.80
-
-- pip:
-  # Required packages for AzureML execution, history, and data preparation.
-  - azureml-sdk[automl,explain]
-  - azureml-widgets
-  - pandas_ml
-  
+name: azure_automl
+dependencies:
+  # The python interpreter version.
+  # Currently Azure ML only supports 3.5.2 and later.
+- pip
+- python>=3.5.2,<3.6.8
+- nb_conda
+- matplotlib==2.1.0
+- numpy>=1.16.0,<=1.16.2
+- cython
+- urllib3<1.24
+- scipy>=1.0.0,<=1.1.0
+- scikit-learn>=0.19.0,<=0.20.3
+- pandas>=0.22.0,<=0.23.4
+- py-xgboost<=0.80
+- pyarrow>=0.11.0
+- fbprophet==0.5
+- pytorch=1.1.0
+- cudatoolkit=9.0
+
+- pip:
+  # Required packages for AzureML execution, history, and data preparation.
+  - azureml-defaults
+  - azureml-train-automl
+  - azureml-train
+  - azureml-widgets
+  - azureml-explain-model
+  - azureml-pipeline
+  - azureml-contrib-interpret
+  - pandas_ml
+  - pytorch-transformers==1.0.0
+  - spacy==2.1.8
+  - joblib
+  - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
+
+channels:
+- conda-forge
+- pytorch

how-to-use-azureml/automated-machine-learning/automl_env_mac.yml

@@ -0,0 +1,39 @@
+name: azure_automl
+dependencies:
+  # The python interpreter version.
+  # Currently Azure ML only supports 3.5.2 and later.
+- pip
+- nomkl
+- python>=3.5.2,<3.6.8
+- nb_conda
+- matplotlib==2.1.0
+- numpy>=1.16.0,<=1.16.2
+- cython
+- urllib3<1.24
+- scipy>=1.0.0,<=1.1.0
+- scikit-learn>=0.19.0,<=0.20.3
+- pandas>=0.22.0,<0.23.0
+- py-xgboost<=0.80
+- pyarrow>=0.11.0
+- fbprophet==0.5
+- pytorch=1.1.0
+- cudatoolkit=9.0
+
+- pip:
+  # Required packages for AzureML execution, history, and data preparation.
+  - azureml-defaults
+  - azureml-train-automl
+  - azureml-train
+  - azureml-widgets
+  - azureml-explain-model
+  - azureml-pipeline
+  - azureml-contrib-interpret
+  - pandas_ml
+  - pytorch-transformers==1.0.0
+  - spacy==2.1.8
+  - joblib
+  - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz  
+
+channels:
+- conda-forge
+- pytorch

how-to-use-azureml/automated-machine-learning/automl_setup.cmd

@@ -1,62 +1,62 @@
-@echo off
-set conda_env_name=%1
-set automl_env_file=%2
-set options=%3
-set PIP_NO_WARN_SCRIPT_LOCATION=0
-
-IF "%conda_env_name%"=="" SET conda_env_name="azure_automl"
-IF "%automl_env_file%"=="" SET automl_env_file="automl_env.yml"
-
-IF NOT EXIST %automl_env_file% GOTO YmlMissing
-
-IF "%CONDA_EXE%"=="" GOTO CondaMissing
-
-call conda activate %conda_env_name% 2>nul:
-
-if not errorlevel 1 (
-  echo Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment %conda_env_name%
-  call pip install --upgrade azureml-sdk[automl,notebooks,explain]
-  if errorlevel 1 goto ErrorExit
-) else (
-  call conda env create -f %automl_env_file% -n %conda_env_name%
-)
-
-call conda activate %conda_env_name% 2>nul:
-if errorlevel 1 goto ErrorExit
-
-call python -m ipykernel install --user --name %conda_env_name% --display-name "Python (%conda_env_name%)"
-
-REM azureml.widgets is now installed as part of the pip install under the conda env.
-REM Removing the old user install so that the notebooks will use the latest widget.
-call jupyter nbextension uninstall --user --py azureml.widgets
-
-echo.
-echo.
-echo ***************************************
-echo * AutoML setup completed successfully *
-echo ***************************************
-IF NOT "%options%"=="nolaunch" (
-  echo.
-  echo Starting jupyter notebook - please run the configuration notebook 
-  echo.
-  jupyter notebook --log-level=50 --notebook-dir='..\..'
-)
-
-goto End
-
-:CondaMissing
-echo Please run this script from an Anaconda Prompt window.
-echo You can start an Anaconda Prompt window by
-echo typing Anaconda Prompt on the Start menu.
-echo If you don't see the Anaconda Prompt app, install Miniconda.
-echo If you are running an older version of Miniconda or Anaconda,
-echo you can upgrade using the command: conda update conda
-goto End
-
-:YmlMissing
-echo File %automl_env_file% not found.
-
-:ErrorExit
-echo Install failed
-
+@echo off
+set conda_env_name=%1
+set automl_env_file=%2
+set options=%3
+set PIP_NO_WARN_SCRIPT_LOCATION=0
+
+IF "%conda_env_name%"=="" SET conda_env_name="azure_automl"
+IF "%automl_env_file%"=="" SET automl_env_file="automl_env.yml"
+
+IF NOT EXIST %automl_env_file% GOTO YmlMissing
+
+IF "%CONDA_EXE%"=="" GOTO CondaMissing
+
+call conda activate %conda_env_name% 2>nul:
+
+if not errorlevel 1 (
+  echo Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment %conda_env_name%
+  call pip install --upgrade azureml-sdk[automl,notebooks,explain]
+  if errorlevel 1 goto ErrorExit
+) else (
+  call conda env create -f %automl_env_file% -n %conda_env_name%
+)
+
+call conda activate %conda_env_name% 2>nul:
+if errorlevel 1 goto ErrorExit
+
+call python -m ipykernel install --user --name %conda_env_name% --display-name "Python (%conda_env_name%)"
+
+REM azureml.widgets is now installed as part of the pip install under the conda env.
+REM Removing the old user install so that the notebooks will use the latest widget.
+call jupyter nbextension uninstall --user --py azureml.widgets
+
+echo.
+echo.
+echo ***************************************
+echo * AutoML setup completed successfully *
+echo ***************************************
+IF NOT "%options%"=="nolaunch" (
+  echo.
+  echo Starting jupyter notebook - please run the configuration notebook 
+  echo.
+  jupyter notebook --log-level=50 --notebook-dir='..\..'
+)
+
+goto End
+
+:CondaMissing
+echo Please run this script from an Anaconda Prompt window.
+echo You can start an Anaconda Prompt window by
+echo typing Anaconda Prompt on the Start menu.
+echo If you don't see the Anaconda Prompt app, install Miniconda.
+echo If you are running an older version of Miniconda or Anaconda,
+echo you can upgrade using the command: conda update conda
+goto End
+
+:YmlMissing
+echo File %automl_env_file% not found.
+
+:ErrorExit
+echo Install failed
+
 :End

how-to-use-azureml/automated-machine-learning/automl_setup_linux.sh

@@ -1,52 +1,52 @@
-#!/bin/bash
-
-CONDA_ENV_NAME=$1
-AUTOML_ENV_FILE=$2
-OPTIONS=$3
-PIP_NO_WARN_SCRIPT_LOCATION=0
-
-if [ "$CONDA_ENV_NAME" == "" ]
-then
-  CONDA_ENV_NAME="azure_automl"
-fi
-
-if [ "$AUTOML_ENV_FILE" == "" ]
-then
-  AUTOML_ENV_FILE="automl_env.yml"
-fi
-
-if [ ! -f $AUTOML_ENV_FILE ]; then
-    echo "File $AUTOML_ENV_FILE not found"
-    exit 1
-fi
-
-if source activate $CONDA_ENV_NAME 2> /dev/null
-then
-   echo "Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment" $CONDA_ENV_NAME
-   pip install --upgrade azureml-sdk[automl,notebooks,explain] &&
-   jupyter nbextension uninstall --user --py azureml.widgets
-else
-   conda env create -f $AUTOML_ENV_FILE -n $CONDA_ENV_NAME &&
-   source activate $CONDA_ENV_NAME &&
-   python -m ipykernel install --user --name $CONDA_ENV_NAME --display-name "Python ($CONDA_ENV_NAME)" &&
-   jupyter nbextension uninstall --user --py azureml.widgets &&
-   echo "" &&
-   echo "" &&
-   echo "***************************************" &&
-   echo "* AutoML setup completed successfully *" &&
-   echo "***************************************" &&
-   if [ "$OPTIONS" != "nolaunch" ]
-   then
-      echo "" &&
-      echo "Starting jupyter notebook - please run the configuration notebook" &&
-      echo "" &&
-      jupyter notebook --log-level=50 --notebook-dir '../..'
-   fi
-fi
-
-if [ $? -gt 0 ]
-then
-   echo "Installation failed"
-fi
-
-
+#!/bin/bash
+
+CONDA_ENV_NAME=$1
+AUTOML_ENV_FILE=$2
+OPTIONS=$3
+PIP_NO_WARN_SCRIPT_LOCATION=0
+
+if [ "$CONDA_ENV_NAME" == "" ]
+then
+  CONDA_ENV_NAME="azure_automl"
+fi
+
+if [ "$AUTOML_ENV_FILE" == "" ]
+then
+  AUTOML_ENV_FILE="automl_env.yml"
+fi
+
+if [ ! -f $AUTOML_ENV_FILE ]; then
+    echo "File $AUTOML_ENV_FILE not found"
+    exit 1
+fi
+
+if source activate $CONDA_ENV_NAME 2> /dev/null
+then
+   echo "Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment" $CONDA_ENV_NAME
+   pip install --upgrade azureml-sdk[automl,notebooks,explain] &&
+   jupyter nbextension uninstall --user --py azureml.widgets
+else
+   conda env create -f $AUTOML_ENV_FILE -n $CONDA_ENV_NAME &&
+   source activate $CONDA_ENV_NAME &&
+   python -m ipykernel install --user --name $CONDA_ENV_NAME --display-name "Python ($CONDA_ENV_NAME)" &&
+   jupyter nbextension uninstall --user --py azureml.widgets &&
+   echo "" &&
+   echo "" &&
+   echo "***************************************" &&
+   echo "* AutoML setup completed successfully *" &&
+   echo "***************************************" &&
+   if [ "$OPTIONS" != "nolaunch" ]
+   then
+      echo "" &&
+      echo "Starting jupyter notebook - please run the configuration notebook" &&
+      echo "" &&
+      jupyter notebook --log-level=50 --notebook-dir '../..'
+   fi
+fi
+
+if [ $? -gt 0 ]
+then
+   echo "Installation failed"
+fi
+
+

how-to-use-azureml/automated-machine-learning/automl_setup_mac.sh

@@ -1,54 +1,54 @@
-#!/bin/bash
-
-CONDA_ENV_NAME=$1
-AUTOML_ENV_FILE=$2
-OPTIONS=$3
-PIP_NO_WARN_SCRIPT_LOCATION=0
-
-if [ "$CONDA_ENV_NAME" == "" ]
-then
-  CONDA_ENV_NAME="azure_automl"
-fi
-
-if [ "$AUTOML_ENV_FILE" == "" ]
-then
-  AUTOML_ENV_FILE="automl_env_mac.yml"
-fi
-
-if [ ! -f $AUTOML_ENV_FILE ]; then
-    echo "File $AUTOML_ENV_FILE not found"
-    exit 1
-fi
-
-if source activate $CONDA_ENV_NAME 2> /dev/null
-then
-   echo "Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment" $CONDA_ENV_NAME
-   pip install --upgrade azureml-sdk[automl,notebooks,explain] &&
-   jupyter nbextension uninstall --user --py azureml.widgets
-else
-   conda env create -f $AUTOML_ENV_FILE -n $CONDA_ENV_NAME &&
-   source activate $CONDA_ENV_NAME &&
-   conda install lightgbm -c conda-forge -y &&
-   python -m ipykernel install --user --name $CONDA_ENV_NAME --display-name "Python ($CONDA_ENV_NAME)" &&
-   jupyter nbextension uninstall --user --py azureml.widgets &&
-   echo "" &&
-   echo "" &&
-   echo "***************************************" &&
-   echo "* AutoML setup completed successfully *" &&
-   echo "***************************************" &&
-   if [ "$OPTIONS" != "nolaunch" ]
-   then
-      echo "" &&
-      echo "Starting jupyter notebook - please run the configuration notebook" &&
-      echo "" &&
-      jupyter notebook --log-level=50 --notebook-dir '../..'
-   fi
-fi
-
-if [ $? -gt 0 ]
-then
-   echo "Installation failed"
-fi
-
-
-
+#!/bin/bash
+
+CONDA_ENV_NAME=$1
+AUTOML_ENV_FILE=$2
+OPTIONS=$3
+PIP_NO_WARN_SCRIPT_LOCATION=0
+
+if [ "$CONDA_ENV_NAME" == "" ]
+then
+  CONDA_ENV_NAME="azure_automl"
+fi
+
+if [ "$AUTOML_ENV_FILE" == "" ]
+then
+  AUTOML_ENV_FILE="automl_env_mac.yml"
+fi
+
+if [ ! -f $AUTOML_ENV_FILE ]; then
+    echo "File $AUTOML_ENV_FILE not found"
+    exit 1
+fi
+
+if source activate $CONDA_ENV_NAME 2> /dev/null
+then
+   echo "Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment" $CONDA_ENV_NAME
+   pip install --upgrade azureml-sdk[automl,notebooks,explain] &&
+   jupyter nbextension uninstall --user --py azureml.widgets
+else
+   conda env create -f $AUTOML_ENV_FILE -n $CONDA_ENV_NAME &&
+   source activate $CONDA_ENV_NAME &&
+   conda install lightgbm -c conda-forge -y &&
+   python -m ipykernel install --user --name $CONDA_ENV_NAME --display-name "Python ($CONDA_ENV_NAME)" &&
+   jupyter nbextension uninstall --user --py azureml.widgets &&
+   echo "" &&
+   echo "" &&
+   echo "***************************************" &&
+   echo "* AutoML setup completed successfully *" &&
+   echo "***************************************" &&
+   if [ "$OPTIONS" != "nolaunch" ]
+   then
+      echo "" &&
+      echo "Starting jupyter notebook - please run the configuration notebook" &&
+      echo "" &&
+      jupyter notebook --log-level=50 --notebook-dir '../..'
+   fi
+fi
+
+if [ $? -gt 0 ]
+then
+   echo "Installation failed"
+fi
+
+
+

how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb

@@ -0,0 +1,932 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Automated Machine Learning\n",
+        "_**Classification with Deployment using a Bank Marketing Dataset**_\n",
+        "\n",
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Train](#Train)\n",
+        "1. [Results](#Results)\n",
+        "1. [Deploy](#Deploy)\n",
+        "1. [Test](#Test)\n",
+        "1. [Acknowledgements](#Acknowledgements)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "\n",
+        "In this example we use the UCI Bank Marketing dataset to showcase how you can use AutoML for a  classification problem and deploy it to an Azure Container Instance (ACI). The classification goal is to predict if the client will subscribe to a term deposit with the bank.\n",
+        "\n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set.  Otherwise, go through the [configuration](../../../configuration.ipynb)  notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
+        "\n",
+        "Please find the ONNX related documentations [here](https://github.com/onnx/onnx).\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 with ONNX compatible config on.\n",
+        "4. Explore the results, featurization transparency options and save the ONNX model\n",
+        "5. Inference with the ONNX model.\n",
+        "6. Register the model.\n",
+        "7. Create a container image.\n",
+        "8. Create an Azure Container Instance (ACI) service.\n",
+        "9. Test the ACI service.\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"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup\n",
+        "\n",
+        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import logging\n",
+        "\n",
+        "from matplotlib import pyplot as plt\n",
+        "import pandas as pd\n",
+        "import os\n",
+        "\n",
+        "import azureml.core\n",
+        "from azureml.core.experiment import Experiment\n",
+        "from azureml.core.workspace import Workspace\n",
+        "from azureml.automl.core.featurization import FeaturizationConfig\n",
+        "from azureml.core.dataset import Dataset\n",
+        "from azureml.train.automl import AutoMLConfig\n",
+        "from azureml.explain.model._internal.explanation_client import ExplanationClient"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "\n",
+        "# choose a name for experiment\n",
+        "experiment_name = 'automl-classification-bmarketing-all'\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['Experiment Name'] = experiment.name\n",
+        "pd.set_option('display.max_colwidth', -1)\n",
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create or Attach existing AmlCompute\n",
+        "You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
+        "#### Creation of AmlCompute takes approximately 5 minutes. \n",
+        "If the AmlCompute with that name is already in your workspace this code will skip the 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 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",
+        "amlcompute_cluster_name = \"cpu-cluster-4\"\n",
+        "\n",
+        "found = False\n",
+        "# Check if this compute target already exists in the workspace.\n",
+        "cts = ws.compute_targets\n",
+        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
+        "    found = True\n",
+        "    print('Found existing compute target.')\n",
+        "    compute_target = cts[amlcompute_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, amlcompute_cluster_name, provisioning_config)\n",
+        "    \n",
+        "print('Checking cluster status...')\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 AmlCompute status, use get_status()."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Load Data\n",
+        "\n",
+        "Leverage azure compute to load the bank marketing dataset as a Tabular Dataset into the dataset variable. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Training Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "data = pd.read_csv(\"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\")\n",
+        "data.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Add missing values in 75% of the lines.\n",
+        "import numpy as np\n",
+        "\n",
+        "missing_rate = 0.75\n",
+        "n_missing_samples = int(np.floor(data.shape[0] * missing_rate))\n",
+        "missing_samples = np.hstack((np.zeros(data.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, data.shape[1], n_missing_samples)\n",
+        "data.values[np.where(missing_samples)[0], missing_features] = np.nan"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "if not os.path.isdir('data'):\n",
+        "    os.mkdir('data')\n",
+        "    \n",
+        "# Save the train data to a csv to be uploaded to the datastore\n",
+        "pd.DataFrame(data).to_csv(\"data/train_data.csv\", index=False)\n",
+        "\n",
+        "ds = ws.get_default_datastore()\n",
+        "ds.upload(src_dir='./data', target_path='bankmarketing', overwrite=True, show_progress=True)\n",
+        "\n",
+        " \n",
+        "\n",
+        "# Upload the training data as a tabular dataset for access during training on remote compute\n",
+        "train_data = Dataset.Tabular.from_delimited_files(path=ds.path('bankmarketing/train_data.csv'))\n",
+        "label = \"y\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Validation Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "validation_data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_validate.csv\"\n",
+        "validation_dataset = Dataset.Tabular.from_delimited_files(validation_data)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Test Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "test_data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_test.csv\"\n",
+        "test_dataset = Dataset.Tabular.from_delimited_files(test_data)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train\n",
+        "\n",
+        "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
+        "\n",
+        "|Property|Description|\n",
+        "|-|-|\n",
+        "|**task**|classification or regression or forecasting|\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",
+        "|**blacklist_models** or **whitelist_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><br><br>Allowed values for **Forecasting**<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><br><i>Arima</i><br><i>Prophet</i>|\n",
+        "|**experiment_exit_score**| Value indicating the target for *primary_metric*. <br>Once the target is surpassed the run terminates.|\n",
+        "|**experiment_timeout_minutes**| Maximum amount of time in minutes that all iterations combined can take before the experiment terminates.|\n",
+        "|**enable_early_stopping**| Flag to enble early termination if the score is not improving in the short term.|\n",
+        "|**featurization**| 'auto' / 'off'  Indicator for whether featurization step should be done automatically or not. Note: If the input data is sparse, featurization cannot be turned on.|\n",
+        "|**n_cross_validations**|Number of cross validation splits.|\n",
+        "|**training_data**|Input dataset, containing both features and label column.|\n",
+        "|**label_column_name**|The name of the label column.|\n",
+        "|**model_explainability**|Indicate to explain each trained pipeline or not.|\n",
+        "\n",
+        "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "automl_settings = {\n",
+        "    \"experiment_timeout_minutes\" : 20,\n",
+        "    \"enable_early_stopping\" : True,\n",
+        "    \"iteration_timeout_minutes\": 5,\n",
+        "    \"max_concurrent_iterations\": 4,\n",
+        "    \"max_cores_per_iteration\": -1,\n",
+        "    #\"n_cross_validations\": 2,\n",
+        "    \"primary_metric\": 'AUC_weighted',\n",
+        "    \"featurization\": 'auto',\n",
+        "    \"verbosity\": logging.INFO,\n",
+        "}\n",
+        "\n",
+        "automl_config = AutoMLConfig(task = 'classification',\n",
+        "                             debug_log = 'automl_errors.log',\n",
+        "                             compute_target=compute_target,\n",
+        "                             experiment_exit_score = 0.9984,\n",
+        "                             blacklist_models = ['KNN','LinearSVM'],\n",
+        "                             enable_onnx_compatible_models=True,\n",
+        "                             training_data = train_data,\n",
+        "                             label_column_name = label,\n",
+        "                             validation_data = validation_dataset,\n",
+        "                             model_explainability=True,\n",
+        "                             **automl_settings\n",
+        "                            )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run = experiment.submit(automl_config, show_output = False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Run the following cell to access previous runs. Uncomment the cell below and update the run_id."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#from azureml.train.automl.run import AutoMLRun\n",
+        "#experiment_name = 'automl-classification-bmarketing'\n",
+        "#experiment = Experiment(ws, experiment_name)\n",
+        "#remote_run = AutoMLRun(experiment=experiment, run_id='<run_ID_goes_here')\n",
+        "#remote_run"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Wait for the remote run to complete\n",
+        "remote_run.wait_for_completion()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "best_run_customized, fitted_model_customized = remote_run.get_output()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Transparency\n",
+        "\n",
+        "View updated featurization summary"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "custom_featurizer = fitted_model_customized.named_steps['datatransformer']\n",
+        "df = custom_featurizer.get_featurization_summary()\n",
+        "pd.DataFrame(data=df)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Set `is_user_friendly=False` to get a more detailed summary for the transforms being applied."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "df = custom_featurizer.get_featurization_summary(is_user_friendly=False)\n",
+        "pd.DataFrame(data=df)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "df = custom_featurizer.get_stats_feature_type_summary()\n",
+        "pd.DataFrame(data=df)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Results"
+      ]
+    },
+    {
+      "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 the Best Model's explanation\n",
+        "Retrieve the explanation from the best_run which includes explanations for engineered features and raw features. Make sure that the run for generating explanations for the best model is completed."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Wait for the best model explanation run to complete\n",
+        "from azureml.train.automl.run import AutoMLRun\n",
+        "model_explainability_run_id = remote_run.get_properties().get('ModelExplainRunId')\n",
+        "print(model_explainability_run_id)\n",
+        "if model_explainability_run_id is not None:\n",
+        "    model_explainability_run = AutoMLRun(experiment=experiment, run_id=model_explainability_run_id)\n",
+        "    model_explainability_run.wait_for_completion()\n",
+        "\n",
+        "# Get the best run object\n",
+        "best_run, fitted_model = remote_run.get_output()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Download engineered feature importance from artifact store\n",
+        "You can use ExplanationClient to download the engineered feature explanations from the artifact store of the best_run."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "client = ExplanationClient.from_run(best_run)\n",
+        "engineered_explanations = client.download_model_explanation(raw=False)\n",
+        "exp_data = engineered_explanations.get_feature_importance_dict()\n",
+        "exp_data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Download raw feature importance from artifact store\n",
+        "You can use ExplanationClient to download the raw feature explanations from the artifact store of the best_run."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "client = ExplanationClient.from_run(best_run)\n",
+        "engineered_explanations = client.download_model_explanation(raw=True)\n",
+        "exp_data = engineered_explanations.get_feature_importance_dict()\n",
+        "exp_data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Retrieve the Best ONNX 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*.\n",
+        "\n",
+        "Set the parameter return_onnx_model=True to retrieve the best ONNX model, instead of the Python model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "best_run, onnx_mdl = remote_run.get_output(return_onnx_model=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Save the best ONNX model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.automl.core.onnx_convert import OnnxConverter\n",
+        "onnx_fl_path = \"./best_model.onnx\"\n",
+        "OnnxConverter.save_onnx_model(onnx_mdl, onnx_fl_path)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Predict with the ONNX model, using onnxruntime package\n",
+        "#### Note: The code will install the onnxruntime==0.4.0 if not installed. Newer versions of the onnxruntime have compatibility issues."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "test_df = test_dataset.to_pandas_dataframe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "import json\n",
+        "from azureml.automl.core.onnx_convert import OnnxConvertConstants\n",
+        "from azureml.train.automl import constants\n",
+        "\n",
+        "if sys.version_info < OnnxConvertConstants.OnnxIncompatiblePythonVersion:\n",
+        "    python_version_compatible = True\n",
+        "else:\n",
+        "    python_version_compatible = False\n",
+        "\n",
+        "onnxrt_present = False\n",
+        "try:\n",
+        "    import onnxruntime\n",
+        "    from azureml.automl.core.onnx_convert import OnnxInferenceHelper    \n",
+        "    from onnxruntime import __version__ as ORT_VER\n",
+        "    if ORT_VER == '0.4.0':\n",
+        "        onnxrt_present = True\n",
+        "except ImportError:\n",
+        "    onnxrt_present = False\n",
+        "    \n",
+        "# Install the onnxruntime if the version 0.4.0 is not installed.\n",
+        "if not onnxrt_present:\n",
+        "    print(\"Installing the onnxruntime version 0.4.0.\")\n",
+        "    !{sys.executable} -m pip install --user --force-reinstall onnxruntime==0.4.0\n",
+        "    onnxrt_present = True\n",
+        "\n",
+        "def get_onnx_res(run):\n",
+        "    res_path = 'onnx_resource.json'\n",
+        "    run.download_file(name=constants.MODEL_RESOURCE_PATH_ONNX, output_file_path=res_path)\n",
+        "    with open(res_path) as f:\n",
+        "        onnx_res = json.load(f)\n",
+        "    return onnx_res\n",
+        "\n",
+        "if onnxrt_present and python_version_compatible:    \n",
+        "    mdl_bytes = onnx_mdl.SerializeToString()\n",
+        "    onnx_res = get_onnx_res(best_run)\n",
+        "\n",
+        "    onnxrt_helper = OnnxInferenceHelper(mdl_bytes, onnx_res)\n",
+        "    pred_onnx, pred_prob_onnx = onnxrt_helper.predict(test_df)\n",
+        "\n",
+        "    print(pred_onnx)\n",
+        "    print(pred_prob_onnx)\n",
+        "else:\n",
+        "    if not python_version_compatible:\n",
+        "        print('Please use Python version 3.6 or 3.7 to run the inference helper.')    \n",
+        "    if not onnxrt_present:\n",
+        "        print('Please install the onnxruntime package to do the prediction with ONNX model.')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Deploy\n",
+        "\n",
+        "### Retrieve the Best Model\n",
+        "\n",
+        "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+      ]
+    },
+    {
+      "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": [
+        "best_run, fitted_model = remote_run.get_output()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import shutil\n",
+        "\n",
+        "sript_folder = os.path.join(os.getcwd(), 'inference')\n",
+        "project_folder = '/inference'\n",
+        "os.makedirs(project_folder, exist_ok=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model_name = best_run.properties['model_name']\n",
+        "\n",
+        "script_file_name = 'inference/score.py'\n",
+        "conda_env_file_name = 'inference/env.yml'\n",
+        "\n",
+        "best_run.download_file('outputs/scoring_file_v_1_0_0.py', 'inference/score.py')\n",
+        "best_run.download_file('outputs/conda_env_v_1_0_0.yml', 'inference/env.yml')"
+      ]
+    },
+    {
+      "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 trained on bank marketing data to predict if a client will subscribe to a term deposit'\n",
+        "tags = None\n",
+        "model = remote_run.register_model(model_name = model_name, description = description, tags = tags)\n",
+        "\n",
+        "print(remote_run.model_id) # This will be written to the script file later in the notebook."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Deploy the model as a Web Service on Azure Container Instance"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.webservice import AciWebservice\n",
+        "from azureml.core.webservice import Webservice\n",
+        "from azureml.core.model import Model\n",
+        "\n",
+        "inference_config = InferenceConfig(runtime = \"python\", \n",
+        "                                   entry_script = script_file_name,\n",
+        "                                   conda_file = conda_env_file_name)\n",
+        "\n",
+        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
+        "                                               memory_gb = 1, \n",
+        "                                               tags = {'area': \"bmData\", 'type': \"automl_classification\"}, \n",
+        "                                               description = 'sample service for Automl Classification')\n",
+        "\n",
+        "aci_service_name = 'automl-sample-bankmarketing-all'\n",
+        "print(aci_service_name)\n",
+        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
+        "aci_service.wait_for_deployment(True)\n",
+        "print(aci_service.state)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Delete a Web Service\n",
+        "\n",
+        "Deletes the specified 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\n",
+        "\n",
+        "Gets 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\n",
+        "\n",
+        "Now that the model is trained, run the test data through the trained model to get the predicted values."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Load the bank marketing datasets.\n",
+        "from numpy import array"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_test = test_dataset.drop_columns(columns=['y'])\n",
+        "y_test = test_dataset.keep_columns(columns=['y'], validate=True)\n",
+        "test_dataset.take(5).to_pandas_dataframe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_test = X_test.to_pandas_dataframe()\n",
+        "y_test = y_test.to_pandas_dataframe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "y_pred  = fitted_model.predict(X_test)\n",
+        "actual = array(y_test)\n",
+        "actual = actual[:,0]\n",
+        "print(y_pred.shape, \" \", actual.shape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Calculate metrics for the prediction\n",
+        "\n",
+        "Now visualize the data on a scatter plot to show what our truth (actual) values are compared to the predicted values \n",
+        "from the trained model that was returned."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%matplotlib notebook\n",
+        "test_pred = plt.scatter(actual, y_pred, color='b')\n",
+        "test_test = plt.scatter(actual, actual, color='g')\n",
+        "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Acknowledgements"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This Bank Marketing dataset is made available under the Creative Commons (CCO: Public Domain) License: https://creativecommons.org/publicdomain/zero/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: https://creativecommons.org/publicdomain/zero/1.0/ and is available at: https://www.kaggle.com/janiobachmann/bank-marketing-dataset .\n",
+        "\n",
+        "_**Acknowledgements**_\n",
+        "This data set is originally available within the UCI Machine Learning Database: https://archive.ics.uci.edu/ml/datasets/bank+marketing\n",
+        "\n",
+        "[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "anumamah"
+      }
+    ],
+    "category": "tutorial",
+    "compute": [
+      "AML"
+    ],
+    "datasets": [
+      "Bankmarketing"
+    ],
+    "deployment": [
+      "ACI"
+    ],
+    "exclude_from_index": false,
+    "framework": [
+      "None"
+    ],
+    "friendly_name": "Automated ML run with basic edition features.",
+    "index_order": 5,
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.7"
+    },
+    "tags": [
+      "featurization",
+      "explainability",
+      "remote_run",
+      "AutomatedML"
+    ],
+    "task": "Classification"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.yml

@@ -0,0 +1,13 @@
+name: auto-ml-classification-bank-marketing-all-features
+dependencies:
+- pip:
+  - azureml-sdk
+  - interpret
+  - azureml-defaults
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml
+  - onnxruntime==0.4.0
+  - azureml-explain-model
+  - azureml-contrib-interpret

how-to-use-azureml/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.ipynb

@@ -1,729 +0,0 @@
-{
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "name": "python36",
-      "language": "python"
-    },
-    "authors": [
-      {
-        "name": "v-rasav"
-      }
-    ],
-    "language_info": {
-      "mimetype": "text/x-python",
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "pygments_lexer": "ipython3",
-      "name": "python",
-      "file_extension": ".py",
-      "nbconvert_exporter": "python",
-      "version": "3.6.7"
-    }
-  },
-  "nbformat": 4,
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.png)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Automated Machine Learning\n",
-        "_**Classification with Deployment using a Bank Marketing Dataset**_\n",
-        "\n",
-        "## Contents\n",
-        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#Setup)\n",
-        "1. [Train](#Train)\n",
-        "1. [Results](#Results)\n",
-        "1. [Deploy](#Deploy)\n",
-        "1. [Test](#Test)\n",
-        "1. [Acknowledgements](#Acknowledgements)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Introduction\n",
-        "\n",
-        "In this example we use the UCI Bank Marketing dataset to showcase how you can use AutoML for a  classification problem and deploy it to an Azure Container Instance (ACI). The classification goal is to predict if the client will subscribe to a term deposit with the bank.\n",
-        "\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set.  Otherwise, go through the [configuration](../../../configuration.ipynb)  notebook first if you haven't already to establish your connection to the AzureML Workspace. \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."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Setup\n",
-        "\n",
-        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "import json\n",
-        "import logging\n",
-        "\n",
-        "from matplotlib import pyplot as plt\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "import os\n",
-        "from sklearn import datasets\n",
-        "import azureml.dataprep as dprep\n",
-        "from sklearn.model_selection import train_test_split\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"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "# choose a name for experiment\n",
-        "experiment_name = 'automl-classification-bmarketing'\n",
-        "# project folder\n",
-        "project_folder = './sample_projects/automl-classification-bankmarketing'\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",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
-        "outputDf.T"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Create or Attach existing AmlCompute\n",
-        "You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
-        "#### Creation of AmlCompute takes approximately 5 minutes. \n",
-        "If the AmlCompute with that name is already in your workspace this code will skip the 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 on the default limits and how to request more quota."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "from azureml.core.compute import AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
-        "\n",
-        "# Choose a name for your cluster.\n",
-        "amlcompute_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 amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
-        "    found = True\n",
-        "    print('Found existing compute target.')\n",
-        "    compute_target = cts[amlcompute_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, amlcompute_cluster_name, provisioning_config)\n",
-        "    \n",
-        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "    compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "    \n",
-        "     # For a more detailed view of current AmlCompute status, use get_status()."
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Data\n",
-        "\n",
-        "Here load the data in the get_data() script to be utilized in azure compute. To do this  first load all the necessary libraries and dependencies to set up paths for the data and to create the conda_Run_config."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "if not os.path.isdir('data'):\n",
-        "    os.mkdir('data')\n",
-        "    \n",
-        "if not os.path.exists(project_folder):\n",
-        "    os.makedirs(project_folder)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "\n",
-        "# create a new RunConfig object\n",
-        "conda_run_config = RunConfiguration(framework=\"python\")\n",
-        "\n",
-        "# Set compute target to AmlCompute\n",
-        "conda_run_config.target = compute_target\n",
-        "conda_run_config.environment.docker.enabled = True\n",
-        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
-        "\n",
-        "\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy','py-xgboost<=0.80'])\n",
-        "conda_run_config.environment.python.conda_dependencies = cd"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Load Data\n",
-        "\n",
-        "Here we create the script to be run in azure comput for loading the data, we load the bank marketing dataset into X_train and y_train. Next X_train and y_train is returned for training the model."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
-        "dflow = dprep.auto_read_file(data)\n",
-        "dflow.get_profile()\n",
-        "X_train = dflow.drop_columns(columns=['y'])\n",
-        "y_train = dflow.keep_columns(columns=['y'], validate_column_exists=True)\n",
-        "dflow.head()"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Train\n",
-        "\n",
-        "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
-        "\n",
-        "|Property|Description|\n",
-        "|-|-|\n",
-        "|**task**|classification or regression|\n",
-        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
-        "|**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, ], Multi-class targets.|\n",
-        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
-        "\n",
-        "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "automl_settings = {\n",
-        "    \"iteration_timeout_minutes\": 5,\n",
-        "    \"iterations\": 10,\n",
-        "    \"n_cross_validations\": 2,\n",
-        "    \"primary_metric\": 'AUC_weighted',\n",
-        "    \"preprocess\": True,\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",
-        "                             X = X_train,\n",
-        "                             y = y_train,\n",
-        "                             **automl_settings\n",
-        "                            )"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
-        "In this example, we specify `show_output = True` to print currently running iterations to the console."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "remote_run = experiment.submit(automl_config, show_output = True)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "remote_run"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Results"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Widget for Monitoring Runs\n",
-        "\n",
-        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
-        "\n",
-        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "from azureml.widgets import RunDetails\n",
-        "RunDetails(remote_run).show() "
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Deploy\n",
-        "\n",
-        "### Retrieve the Best Model\n",
-        "\n",
-        "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "best_run, fitted_model = remote_run.get_output()"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "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": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "description = 'AutoML Model trained on bank marketing data to predict if a client will subscribe to a term deposit'\n",
-        "tags = None\n",
-        "model = remote_run.register_model(description = description, tags = tags)\n",
-        "\n",
-        "print(remote_run.model_id) # This will be written to the script file later in the notebook."
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create Scoring Script\n",
-        "The scoring script is required to generate the image for deployment. It contains the code to do the predictions on input data."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "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": "code"
-    },
-    {
-      "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": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "dependencies = remote_run.get_run_sdk_dependencies(iteration = 1)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
-        "    print('{}\\t{}'.format(p, dependencies[p]))"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "\n",
-        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost<=0.80'],\n",
-        "                                 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"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "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>>', remote_run.model_id))"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create a Container Image\n",
-        "\n",
-        "Next use Azure Container Instances for deploying models as a web service for quickly deploying and validating your model\n",
-        "or when testing a model that is under development."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "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': \"bmData\", '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": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Deploy the Image as a Web Service on Azure Container Instance\n",
-        "\n",
-        "Deploy an image that contains the model and other assets needed by the service."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "from azureml.core.webservice import AciWebservice\n",
-        "\n",
-        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
-        "                                               memory_gb = 1, \n",
-        "                                               tags = {'area': \"bmData\", 'type': \"automl_classification\"}, \n",
-        "                                               description = 'sample service for Automl Classification')"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "from azureml.core.webservice import Webservice\n",
-        "\n",
-        "aci_service_name = 'automl-sample-bankmarketing'\n",
-        "print(aci_service_name)\n",
-        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
-        "                                           image = image,\n",
-        "                                           name = aci_service_name,\n",
-        "                                           workspace = ws)\n",
-        "aci_service.wait_for_deployment(True)\n",
-        "print(aci_service.state)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Delete a Web Service\n",
-        "\n",
-        "Deletes the specified web service."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "#aci_service.delete()"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Get Logs from a Deployed Web Service\n",
-        "\n",
-        "Gets logs from a deployed web service."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "#aci_service.get_logs()"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Test\n",
-        "\n",
-        "Now that the model is trained split our data in the same way the data was split for training (The difference here is the data is being split locally) and then run the test data through the trained model to get the predicted values."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "# Load the bank marketing datasets.\n",
-        "from sklearn.datasets import load_diabetes\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from numpy import array"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_validate.csv\"\n",
-        "dflow = dprep.auto_read_file(data)\n",
-        "dflow.get_profile()\n",
-        "X_test = dflow.drop_columns(columns=['y'])\n",
-        "y_test = dflow.keep_columns(columns=['y'], validate_column_exists=True)\n",
-        "dflow.head()"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "X_test = X_test.to_pandas_dataframe()\n",
-        "y_test = y_test.to_pandas_dataframe()"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "y_pred  = fitted_model.predict(X_test)\n",
-        "actual = array(y_test)\n",
-        "actual = actual[:,0]\n",
-        "print(y_pred.shape, \" \", actual.shape)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Calculate metrics for the prediction\n",
-        "\n",
-        "Now visualize the data on a scatter plot to show what our truth (actual) values are compared to the predicted values \n",
-        "from the trained model that was returned."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "%matplotlib notebook\n",
-        "test_pred = plt.scatter(actual, y_pred, color='b')\n",
-        "test_test = plt.scatter(actual, actual, color='g')\n",
-        "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
-        "plt.show()"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Acknowledgements"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "This Bank Marketing dataset is made available under the Creative Commons (CCO: Public Domain) License: https://creativecommons.org/publicdomain/zero/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: https://creativecommons.org/publicdomain/zero/1.0/ and is available at: https://www.kaggle.com/janiobachmann/bank-marketing-dataset .\n",
-        "\n",
-        "_**Acknowledgements**_\n",
-        "This data set is originally available within the UCI Machine Learning Database: https://archive.ics.uci.edu/ml/datasets/bank+marketing\n",
-        "\n",
-        "[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014"
-      ],
-      "cell_type": "markdown"
-    }
-  ],
-  "nbformat_minor": 2
-}

how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.yml

@@ -1,8 +1,11 @@
-name: auto-ml-classification-credit-card-fraud
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - pandas_ml
+name: auto-ml-classification-credit-card-fraud
+dependencies:
+- pip:
+  - azureml-sdk
+  - interpret
+  - azureml-defaults
+  - azureml-explain-model
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml

how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb

@@ -0,0 +1,558 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Automated Machine Learning\n",
+        "_**Text Classification Using Deep Learning**_\n",
+        "\n",
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Data](#Data)\n",
+        "1. [Train](#Train)\n",
+        "1. [Evaluate](#Evaluate)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "This notebook demonstrates classification with text data using deep learning in AutoML.\n",
+        "\n",
+        "AutoML highlights here include using deep neural networks (DNNs) to create embedded features from text data. Depending on the compute cluster the user provides, AutoML tried out Bidirectional Encoder Representations from Transformers (BERT) when a GPU compute is used, and Bidirectional Long-Short Term neural network (BiLSTM) when a CPU compute is used, thereby optimizing the choice of DNN for the uesr's setup.\n",
+        "\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
+        "\n",
+        "An Enterprise workspace is required for this notebook. To learn more about creating an Enterprise workspace or upgrading to an Enterprise workspace from the Azure portal, please visit our [Workspace page](https://docs.microsoft.com/azure/machine-learning/service/concept-workspace#upgrade).\n",
+        "\n",
+        "Notebook synopsis:\n",
+        "1. Creating an Experiment in an existing Workspace\n",
+        "2. Configuration and remote run of AutoML for a text dataset (20 Newsgroups dataset from scikit-learn) for classification\n",
+        "3. Evaluating the final model on a test set\n",
+        "4. Deploying the model on ACI"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import logging\n",
+        "import os\n",
+        "import shutil\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",
+        "from azureml.core.dataset import Dataset\n",
+        "from azureml.core.compute import AmlCompute\n",
+        "from azureml.core.compute import ComputeTarget\n",
+        "from azureml.core.run import Run\n",
+        "from azureml.widgets import RunDetails\n",
+        "from azureml.core.model import Model \n",
+        "from helper import run_inference, get_result_df\n",
+        "from azureml.train.automl import AutoMLConfig\n",
+        "from sklearn.datasets import fetch_20newsgroups"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "As part of the setup you have already created a <b>Workspace</b>. To run AutoML, you also need to create an <b>Experiment</b>. An Experiment corresponds to a prediction problem you are trying to solve, while a Run corresponds to a specific approach to the problem."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "\n",
+        "# Choose an experiment name.\n",
+        "experiment_name = 'automl-classification-text-dnn'\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['Experiment Name'] = experiment.name\n",
+        "pd.set_option('display.max_colwidth', -1)\n",
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Set up a compute cluster\n",
+        "This section uses a user-provided compute cluster (named \"cpu-cluster\" in this example). If a cluster with this name does not exist in the user's workspace, the below code will create a new cluster. You can choose the parameters of the cluster as mentioned in the comments.\n",
+        "\n",
+        "Whether you provide/select a CPU or GPU cluster, AutoML will choose the appropriate DNN for that setup - BiLSTM or BERT text featurizer will be included in the candidate featurizers on CPU and GPU respectively."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Choose a name for your cluster.\n",
+        "amlcompute_cluster_name = \"cpu-dnntext\"\n",
+        "\n",
+        "found = False\n",
+        "# Check if this compute target already exists in the workspace.\n",
+        "cts = ws.compute_targets\n",
+        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
+        "    found = True\n",
+        "    print('Found existing compute target.')\n",
+        "    compute_target = cts[amlcompute_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\", # CPU for BiLSTM\n",
+        "                                                                # To use BERT, select a GPU such as \"STANDARD_NC6\" \n",
+        "                                                                # or similar GPU option\n",
+        "                                                                # available in your workspace\n",
+        "                                                                max_nodes = 6)\n",
+        "\n",
+        "    # Create the cluster\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
+        "\n",
+        "print('Checking cluster status...')\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 AmlCompute status, use get_status()."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Get data\n",
+        "For this notebook we will use 20 Newsgroups data from scikit-learn. We filter the data to contain four classes and take a sample as training data. Please note that for accuracy improvement, more data is needed. For this notebook we provide a small-data example so that you can use this template to use with your larger sized data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "data_dir = \"text-dnn-data\" # Local directory to store data\n",
+        "blobstore_datadir = data_dir # Blob store directory to store data in\n",
+        "target_column_name = 'y'\n",
+        "feature_column_name = 'X'\n",
+        "\n",
+        "def get_20newsgroups_data():\n",
+        "    '''Fetches 20 Newsgroups data from scikit-learn\n",
+        "       Returns them in form of pandas dataframes\n",
+        "    '''\n",
+        "    remove = ('headers', 'footers', 'quotes')\n",
+        "    categories = [\n",
+        "        'alt.atheism',\n",
+        "        'talk.religion.misc',\n",
+        "        'comp.graphics',\n",
+        "        'sci.space',\n",
+        "        ]\n",
+        "\n",
+        "    data = fetch_20newsgroups(subset = 'train', categories = categories,\n",
+        "                                    shuffle = True, random_state = 42,\n",
+        "                                    remove = remove)\n",
+        "    data = pd.DataFrame({feature_column_name: data.data, target_column_name: data.target})\n",
+        "\n",
+        "    data_train = data[:200]\n",
+        "    data_test = data[200:300]    \n",
+        "\n",
+        "    data_train = remove_blanks_20news(data_train, feature_column_name, target_column_name)\n",
+        "    data_test = remove_blanks_20news(data_test, feature_column_name, target_column_name)\n",
+        "    \n",
+        "    return data_train, data_test\n",
+        "    \n",
+        "def remove_blanks_20news(data, feature_column_name, target_column_name):\n",
+        "    \n",
+        "    data[feature_column_name] = data[feature_column_name].replace(r'\\n', ' ', regex=True).apply(lambda x: x.strip())\n",
+        "    data = data[data[feature_column_name] != '']\n",
+        "    \n",
+        "    return data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Featch data and upload to datastore for use in training"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "data_train, data_test = get_20newsgroups_data()\n",
+        "\n",
+        "if not os.path.isdir(data_dir):\n",
+        "    os.mkdir(data_dir)\n",
+        "    \n",
+        "train_data_fname = data_dir + '/train_data.csv'\n",
+        "test_data_fname = data_dir + '/test_data.csv'\n",
+        "\n",
+        "data_train.to_csv(train_data_fname, index=False)\n",
+        "data_test.to_csv(test_data_fname, index=False)\n",
+        "\n",
+        "datastore = ws.get_default_datastore()\n",
+        "datastore.upload(src_dir=data_dir, target_path=blobstore_datadir,\n",
+        "                    overwrite=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "train_dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, blobstore_datadir + '/train_data.csv')])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Prepare AutoML run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This step requires an Enterprise workspace to gain access to this feature. To learn more about creating an Enterprise workspace or upgrading to an Enterprise workspace from the Azure portal, please visit our [Workspace page](https://docs.microsoft.com/azure/machine-learning/service/concept-workspace#upgrade)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "automl_settings = {\n",
+        "    \"experiment_timeout_minutes\": 20,\n",
+        "    \"primary_metric\": 'accuracy',\n",
+        "    \"preprocess\": True,\n",
+        "    \"max_concurrent_iterations\": 4, \n",
+        "    \"max_cores_per_iteration\": -1,\n",
+        "    \"enable_dnn\": True,\n",
+        "    \"enable_early_stopping\": True,\n",
+        "    \"validation_size\": 0.3,\n",
+        "    \"verbosity\": logging.INFO,\n",
+        "    \"enable_voting_ensemble\": False,\n",
+        "    \"enable_stack_ensemble\": False,\n",
+        "}\n",
+        "\n",
+        "automl_config = AutoMLConfig(task = 'classification',\n",
+        "                             debug_log = 'automl_errors.log',\n",
+        "                             compute_target=compute_target,\n",
+        "                             training_data=train_dataset,\n",
+        "                             label_column_name=target_column_name,\n",
+        "                             **automl_settings\n",
+        "                            )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Submit AutoML Run"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "automl_run = experiment.submit(automl_config, show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "automl_run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Displaying the run objects gives you links to the visual tools in the Azure Portal. Go try them!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Retrieve the Best Model\n",
+        "Below we select the best model pipeline from our iterations, use it to test on test data on the same compute cluster."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can test the model locally to get a feel of the input/output. This step may require additional package installations such as pytorch."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#best_run, fitted_model = automl_run.get_output()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Deploying the model\n",
+        "We now use the best fitted model from the AutoML Run to make predictions on the test set.  "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Get results stats, extract the best model from AutoML run, download and register the resultant best model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "summary_df = get_result_df(automl_run)\n",
+        "best_dnn_run_id = summary_df['run_id'].iloc[0]\n",
+        "best_dnn_run = Run(experiment, best_dnn_run_id)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model_dir = 'Model' # Local folder where the model will be stored temporarily\n",
+        "if not os.path.isdir(model_dir):\n",
+        "    os.mkdir(model_dir)\n",
+        "    \n",
+        "best_dnn_run.download_file('outputs/model.pkl', model_dir + '/model.pkl')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Register the model in your Azure Machine Learning Workspace. If you previously registered a model, please make sure to delete it so as to replace it with this new model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Register the model\n",
+        "model_name = 'textDNN-20News'\n",
+        "model = Model.register(model_path = model_dir + '/model.pkl',\n",
+        "                       model_name = model_name,\n",
+        "                       tags=None,\n",
+        "                       workspace=ws)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Evaluate on Test Data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We now use the best fitted model from the AutoML Run to make predictions on the test set.  \n",
+        "\n",
+        "Test set schema should match that of the training set."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "test_dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, blobstore_datadir + '/test_data.csv')])\n",
+        "\n",
+        "# preview the first 3 rows of the dataset\n",
+        "test_dataset.take(3).to_pandas_dataframe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "test_experiment = Experiment(ws, experiment_name + \"_test\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "script_folder = os.path.join(os.getcwd(), 'inference')\n",
+        "os.makedirs(script_folder, exist_ok=True)\n",
+        "shutil.copy2('infer.py', script_folder)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "test_run = run_inference(test_experiment, compute_target, script_folder, best_dnn_run, test_dataset,\n",
+        "                 target_column_name, model_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Display computed metrics"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "test_run"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "RunDetails(test_run).show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "test_run.wait_for_completion()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pd.Series(test_run.get_metrics())"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "anshirga"
+      }
+    ],
+    "datasets": [
+      "None"
+    ],
+    "deployment": [
+      "None"
+    ],
+    "exclude_from_index": false,
+    "framework": [
+      "None"
+    ],
+    "friendly_name": "DNN Text Featurization",
+    "index_order": 2,
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.7"
+    },
+    "tags": [
+      "None"
+    ],
+    "task": "Text featurization using DNNs for classification"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.yml

@@ -1,8 +1,10 @@
-name: auto-ml-classification-with-whitelisting
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - pandas_ml
+name: auto-ml-classification-text-dnn
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-train
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml
+  - statsmodels

how-to-use-azureml/automated-machine-learning/classification-text-dnn/helper.py

@@ -0,0 +1,60 @@
+import pandas as pd
+from azureml.core import Environment
+from azureml.core.conda_dependencies import CondaDependencies
+from azureml.train.estimator import Estimator
+from azureml.core.run import Run
+
+
+def run_inference(test_experiment, compute_target, script_folder, train_run,
+                  test_dataset, target_column_name, model_name):
+
+    train_run.download_file('outputs/conda_env_v_1_0_0.yml',
+                            'inference/condafile.yml')
+
+    inference_env = Environment("myenv")
+    inference_env.docker.enabled = True
+    inference_env.python.conda_dependencies = CondaDependencies(
+        conda_dependencies_file_path='inference/condafile.yml')
+
+    est = Estimator(source_directory=script_folder,
+                    entry_script='infer.py',
+                    script_params={
+                        '--target_column_name': target_column_name,
+                        '--model_name': model_name
+                    },
+                    inputs=[test_dataset.as_named_input('test_data')],
+                    compute_target=compute_target,
+                    environment_definition=inference_env)
+
+    run = test_experiment.submit(
+        est, tags={
+            'training_run_id': train_run.id,
+            'run_algorithm': train_run.properties['run_algorithm'],
+            'valid_score': train_run.properties['score'],
+            'primary_metric': train_run.properties['primary_metric']
+        })
+
+    run.log("run_algorithm", run.tags['run_algorithm'])
+    return run
+
+
+def get_result_df(remote_run):
+
+    children = list(remote_run.get_children(recursive=True))
+    summary_df = pd.DataFrame(index=['run_id', 'run_algorithm',
+                                     'primary_metric', 'Score'])
+    goal_minimize = False
+    for run in children:
+        if('run_algorithm' in run.properties and 'score' in run.properties):
+            summary_df[run.id] = [run.id, run.properties['run_algorithm'],
+                                  run.properties['primary_metric'],
+                                  float(run.properties['score'])]
+            if('goal' in run.properties):
+                goal_minimize = run.properties['goal'].split('_')[-1] == 'min'
+
+    summary_df = summary_df.T.sort_values(
+        'Score',
+        ascending=goal_minimize).drop_duplicates(['run_algorithm'])
+    summary_df = summary_df.set_index('run_algorithm')
+
+    return summary_df

how-to-use-azureml/automated-machine-learning/classification-text-dnn/infer.py

@@ -0,0 +1,54 @@
+import numpy as np
+import argparse
+from azureml.core import Run
+from sklearn.externals import joblib
+from azureml.automl.core._vendor.automl.client.core.common import metrics
+from automl.client.core.common import constants
+from azureml.core.model import Model
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    '--target_column_name', type=str, dest='target_column_name',
+    help='Target Column Name')
+parser.add_argument(
+    '--model_name', type=str, dest='model_name',
+    help='Name of registered model')
+
+args = parser.parse_args()
+target_column_name = args.target_column_name
+model_name = args.model_name
+
+print('args passed are: ')
+print('Target column name: ', target_column_name)
+print('Name of registered model: ', model_name)
+
+model_path = Model.get_model_path(model_name)
+# deserialize the model file back into a sklearn model
+model = joblib.load(model_path)
+
+run = Run.get_context()
+# get input dataset by name
+test_dataset = run.input_datasets['test_data']
+
+X_test_df = test_dataset.drop_columns(columns=[target_column_name]) \
+                        .to_pandas_dataframe()
+y_test_df = test_dataset.with_timestamp_columns(None) \
+                        .keep_columns(columns=[target_column_name]) \
+                        .to_pandas_dataframe()
+
+predicted = model.predict_proba(X_test_df)
+
+# use automl metrics module
+scores = metrics.compute_metrics_classification(
+    np.array(predicted),
+    np.array(y_test_df),
+    class_labels=model.classes_,
+    metrics=list(constants.Metric.SCALAR_CLASSIFICATION_SET)
+)
+
+print("scores:")
+print(scores)
+
+for key, value in scores.items():
+    run.log(key, value)

how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb

@@ -1,510 +0,0 @@
-{
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "name": "python36",
-      "language": "python"
-    },
-    "authors": [
-      {
-        "name": "savitam"
-      }
-    ],
-    "language_info": {
-      "mimetype": "text/x-python",
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "pygments_lexer": "ipython3",
-      "name": "python",
-      "file_extension": ".py",
-      "nbconvert_exporter": "python",
-      "version": "3.6.6"
-    }
-  },
-  "nbformat": 4,
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.png)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Automated Machine Learning\n",
-        "_**Classification with Deployment**_\n",
-        "\n",
-        "## Contents\n",
-        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#Setup)\n",
-        "1. [Train](#Train)\n",
-        "1. [Deploy](#Deploy)\n",
-        "1. [Test](#Test)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Introduction\n",
-        "\n",
-        "In this example we use the scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) to showcase how you can use AutoML for a simple classification problem and deploy it to an Azure Container Instance (ACI).\n",
-        "\n",
-        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
-        "\n",
-        "In this notebook you will learn how to:\n",
-        "1. Create an experiment using an existing workspace.\n",
-        "2. Configure AutoML using `AutoMLConfig`.\n",
-        "3. Train the model using local compute.\n",
-        "4. Explore the results.\n",
-        "5. Register the model.\n",
-        "6. Create a container image.\n",
-        "7. Create an Azure Container Instance (ACI) service.\n",
-        "8. Test the ACI service."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Setup\n",
-        "\n",
-        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "import json\n",
-        "import logging\n",
-        "\n",
-        "from matplotlib import pyplot as plt\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "from sklearn import datasets\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl.run import AutoMLRun"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "# choose a name for experiment\n",
-        "experiment_name = 'automl-classification-deployment'\n",
-        "# project folder\n",
-        "project_folder = './sample_projects/automl-classification-deployment'\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",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
-        "outputDf.T"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Train\n",
-        "\n",
-        "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
-        "\n",
-        "|Property|Description|\n",
-        "|-|-|\n",
-        "|**task**|classification or regression|\n",
-        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
-        "|**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, ], Multi-class targets.|\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": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "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",
-        "                             verbosity = logging.INFO,\n",
-        "                             X = X_train, \n",
-        "                             y = y_train,\n",
-        "                             path = project_folder)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
-        "In this example, we specify `show_output = True` to print currently running iterations to the console."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "local_run = experiment.submit(automl_config, show_output = True)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "local_run"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Deploy\n",
-        "\n",
-        "### Retrieve the Best Model\n",
-        "\n",
-        "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "best_run, fitted_model = local_run.get_output()"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "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": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "description = 'AutoML Model'\n",
-        "tags = None\n",
-        "model = local_run.register_model(description = description, tags = tags)\n",
-        "\n",
-        "print(local_run.model_id) # This will be written to the script file later in the notebook."
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create Scoring Script"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "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": "code"
-    },
-    {
-      "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. The following cells create a file, myenv.yml, which specifies the dependencies from the run."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "experiment = Experiment(ws, experiment_name)\n",
-        "ml_run = AutoMLRun(experiment = experiment, run_id = local_run.id)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "dependencies = ml_run.get_run_sdk_dependencies(iteration = 7)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
-        "    print('{}\\t{}'.format(p, dependencies[p]))"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "\n",
-        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost<=0.80'],\n",
-        "                                 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"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "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": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create a Container Image"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "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": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Deploy the Image as a Web Service on Azure Container Instance"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "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"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "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": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Delete a Web Service"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "#aci_service.delete()"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Get Logs from a Deployed Web Service"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "#aci_service.get_logs()"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Test"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "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()"
-      ],
-      "cell_type": "code"
-    }
-  ],
-  "nbformat_minor": 2
-}

how-to-use-azureml/automated-machine-learning/classification-with-onnx/auto-ml-classification-with-onnx.ipynb

@@ -1,381 +0,0 @@
-{
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "name": "python36",
-      "language": "python"
-    },
-    "authors": [
-      {
-        "name": "savitam"
-      }
-    ],
-    "language_info": {
-      "mimetype": "text/x-python",
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "pygments_lexer": "ipython3",
-      "name": "python",
-      "file_extension": ".py",
-      "nbconvert_exporter": "python",
-      "version": "3.6.6"
-    }
-  },
-  "nbformat": 4,
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-with-onnx/auto-ml-classification-with-onnx.png)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Automated Machine Learning\n",
-        "_**Classification with Local Compute**_\n",
-        "\n",
-        "## Contents\n",
-        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#Setup)\n",
-        "1. [Data](#Data)\n",
-        "1. [Train](#Train)\n",
-        "1. [Results](#Results)\n",
-        "1. [Test](#Test)\n",
-        "\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Introduction\n",
-        "\n",
-        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
-        "\n",
-        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
-        "\n",
-        "Please find the ONNX related documentations [here](https://github.com/onnx/onnx).\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 with ONNX compatible config on.\n",
-        "4. Explore the results and save the ONNX model."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Setup\n",
-        "\n",
-        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "import logging\n",
-        "\n",
-        "from matplotlib import pyplot as plt\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "from sklearn import datasets\n",
-        "from sklearn.model_selection import train_test_split\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, constants"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "# Choose a name for the experiment and specify the project folder.\n",
-        "experiment_name = 'automl-classification-onnx'\n",
-        "project_folder = './sample_projects/automl-classification-onnx'\n",
-        "\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "\n",
-        "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace Name'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
-        "outputDf.T"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Data\n",
-        "\n",
-        "This uses scikit-learn's [load_iris](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) method."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "iris = datasets.load_iris()\n",
-        "X_train, X_test, y_train, y_test = train_test_split(iris.data, \n",
-        "                                                    iris.target, \n",
-        "                                                    test_size=0.2, \n",
-        "                                                    random_state=0)\n",
-        "\n",
-        "\n"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Ensure the x_train and x_test are pandas DataFrame."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "# Convert the X_train and X_test to pandas DataFrame and set column names,\n",
-        "# This is needed for initializing the input variable names of ONNX model, \n",
-        "# and the prediction with the ONNX model using the inference helper.\n",
-        "X_train = pd.DataFrame(X_train, columns=['c1', 'c2', 'c3', 'c4'])\n",
-        "X_test = pd.DataFrame(X_test, columns=['c1', 'c2', 'c3', 'c4'])"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Train with enable ONNX compatible models config on\n",
-        "\n",
-        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
-        "\n",
-        "Set the parameter enable_onnx_compatible_models=True, if you also want to generate the ONNX compatible models. Please note, the forecasting task and TensorFlow models are not ONNX compatible yet.\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",
-        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-        "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
-        "|**enable_onnx_compatible_models**|Enable the ONNX compatible models in the experiment.|\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": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Set the preprocess=True,  currently the InferenceHelper only supports this mode."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "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",
-        "                             verbosity = logging.INFO,                             \n",
-        "                             X = X_train, \n",
-        "                             y = y_train,\n",
-        "                             preprocess=True,\n",
-        "                             enable_onnx_compatible_models=True,\n",
-        "                             path = project_folder)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
-        "In this example, we specify `show_output = True` to print currently running iterations to the console."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "local_run = experiment.submit(automl_config, show_output = True)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "local_run"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Results"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Widget for Monitoring Runs\n",
-        "\n",
-        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
-        "\n",
-        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "from azureml.widgets import RunDetails\n",
-        "RunDetails(local_run).show() "
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Retrieve the Best ONNX 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*.\n",
-        "\n",
-        "Set the parameter return_onnx_model=True to retrieve the best ONNX model, instead of the Python model."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "best_run, onnx_mdl = local_run.get_output(return_onnx_model=True)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Save the best ONNX model"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "from azureml.automl.core.onnx_convert import OnnxConverter\n",
-        "onnx_fl_path = \"./best_model.onnx\"\n",
-        "OnnxConverter.save_onnx_model(onnx_mdl, onnx_fl_path)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Predict with the ONNX model, using onnxruntime package"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "import sys\n",
-        "import json\n",
-        "from azureml.automl.core.onnx_convert import OnnxConvertConstants\n",
-        "\n",
-        "if sys.version_info < OnnxConvertConstants.OnnxIncompatiblePythonVersion:\n",
-        "    python_version_compatible = True\n",
-        "else:\n",
-        "    python_version_compatible = False\n",
-        "\n",
-        "try:\n",
-        "    import onnxruntime\n",
-        "    from azureml.automl.core.onnx_convert import OnnxInferenceHelper    \n",
-        "    onnxrt_present = True\n",
-        "except ImportError:\n",
-        "    onnxrt_present = False\n",
-        "\n",
-        "def get_onnx_res(run):\n",
-        "    res_path = 'onnx_resource.json'\n",
-        "    run.download_file(name=constants.MODEL_RESOURCE_PATH_ONNX, output_file_path=res_path)\n",
-        "    with open(res_path) as f:\n",
-        "        onnx_res = json.load(f)\n",
-        "    return onnx_res\n",
-        "\n",
-        "if onnxrt_present and python_version_compatible:    \n",
-        "    mdl_bytes = onnx_mdl.SerializeToString()\n",
-        "    onnx_res = get_onnx_res(best_run)\n",
-        "\n",
-        "    onnxrt_helper = OnnxInferenceHelper(mdl_bytes, onnx_res)\n",
-        "    pred_onnx, pred_prob_onnx = onnxrt_helper.predict(X_test)\n",
-        "\n",
-        "    print(pred_onnx)\n",
-        "    print(pred_prob_onnx)\n",
-        "else:\n",
-        "    if not python_version_compatible:\n",
-        "        print('Please use Python version 3.6 or 3.7 to run the inference helper.')    \n",
-        "    if not onnxrt_present:\n",
-        "        print('Please install the onnxruntime package to do the prediction with ONNX model.')"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [],
-      "cell_type": "code"
-    }
-  ],
-  "nbformat_minor": 2
-}

how-to-use-azureml/automated-machine-learning/classification-with-whitelisting/auto-ml-classification-with-whitelisting.ipynb

@@ -1,399 +0,0 @@
-{
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "name": "python36",
-      "language": "python"
-    },
-    "authors": [
-      {
-        "name": "savitam"
-      }
-    ],
-    "language_info": {
-      "mimetype": "text/x-python",
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "pygments_lexer": "ipython3",
-      "name": "python",
-      "file_extension": ".py",
-      "nbconvert_exporter": "python",
-      "version": "3.6.6"
-    }
-  },
-  "nbformat": 4,
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-with-whitelisting/auto-ml-classification-with-whitelisting.png)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Automated Machine Learning\n",
-        "_**Classification using whitelist models**_\n",
-        "\n",
-        "## Contents\n",
-        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#Setup)\n",
-        "1. [Data](#Data)\n",
-        "1. [Train](#Train)\n",
-        "1. [Results](#Results)\n",
-        "1. [Test](#Test)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Introduction\n",
-        "\n",
-        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
-        "\n",
-        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
-        "This notebooks shows how can automl can be trained on a a selected list of models,see the readme.md for the models.\n",
-        "This trains the model exclusively on tensorflow based models.\n",
-        "\n",
-        "In this notebook you will learn how to:\n",
-        "1. Create an `Experiment` in an existing `Workspace`.\n",
-        "2. Configure AutoML using `AutoMLConfig`.\n",
-        "3. Train the model on a whilelisted models using local compute. \n",
-        "4. Explore the results.\n",
-        "5. Test the best fitted model."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Setup\n",
-        "\n",
-        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "#Note: This notebook will install tensorflow if not already installed in the enviornment..\n",
-        "import logging\n",
-        "\n",
-        "from matplotlib import pyplot as plt\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "from sklearn import datasets\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.core.workspace import Workspace\n",
-        "import sys\n",
-        "whitelist_models=[\"LightGBM\"]\n",
-        "if \"3.7\" != sys.version[0:3]:\n",
-        "    try:\n",
-        "        import tensorflow as tf1\n",
-        "    except ImportError:\n",
-        "        from pip._internal import main\n",
-        "        main(['install', 'tensorflow>=1.10.0,<=1.12.0'])\n",
-        "        logging.getLogger().setLevel(logging.ERROR)\n",
-        "    whitelist_models=[\"TensorFlowLinearClassifier\", \"TensorFlowDNN\"]\n",
-        "\n",
-        "from azureml.train.automl import AutoMLConfig"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "# Choose a name for the experiment and specify the project folder.\n",
-        "experiment_name = 'automl-local-whitelist'\n",
-        "project_folder = './sample_projects/automl-local-whitelist'\n",
-        "\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "\n",
-        "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace Name'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
-        "outputDf.T"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Data\n",
-        "\n",
-        "This uses scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "digits = datasets.load_digits()\n",
-        "\n",
-        "# Exclude the first 100 rows from training so that they can be used for test.\n",
-        "X_train = digits.data[100:,:]\n",
-        "y_train = digits.target[100:]"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Train\n",
-        "\n",
-        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
-        "\n",
-        "|Property|Description|\n",
-        "|-|-|\n",
-        "|**task**|classification or regression|\n",
-        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
-        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
-        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
-        "|**n_cross_validations**|Number of cross validation splits.|\n",
-        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-        "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
-        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
-        "|**whitelist_models**|List of models that AutoML should use.  The possible values are listed [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#configure-your-experiment-settings).|"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "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",
-        "                             verbosity = logging.INFO,\n",
-        "                             X = X_train, \n",
-        "                             y = y_train,\n",
-        "                             enable_tf=True,\n",
-        "                             whitelist_models=whitelist_models,\n",
-        "                             path = project_folder)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
-        "In this example, we specify `show_output = True` to print currently running iterations to the console."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "local_run = experiment.submit(automl_config, show_output = True)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "local_run"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Results"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Widget for Monitoring Runs\n",
-        "\n",
-        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
-        "\n",
-        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "from azureml.widgets import RunDetails\n",
-        "RunDetails(local_run).show() "
-      ],
-      "cell_type": "code"
-    },
-    {
-      "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": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "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": "code"
-    },
-    {
-      "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": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "best_run, fitted_model = local_run.get_output()\n",
-        "print(best_run)\n",
-        "print(fitted_model)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "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": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "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": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Model from a Specific Iteration\n",
-        "Show the run and the model from the third iteration:"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "iteration = 3\n",
-        "third_run, third_model = local_run.get_output(iteration = iteration)\n",
-        "print(third_run)\n",
-        "print(third_model)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Test\n",
-        "\n",
-        "#### Load Test Data"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "digits = datasets.load_digits()\n",
-        "X_test = digits.data[:10, :]\n",
-        "y_test = digits.target[:10]\n",
-        "images = digits.images[:10]"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Testing Our Best Fitted Model\n",
-        "We will try to predict 2 digits and see how our model works."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "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()"
-      ],
-      "cell_type": "code"
-    }
-  ],
-  "nbformat_minor": 2
-}

how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.ipynb

@@ -1,482 +0,0 @@
-{
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "name": "python36",
-      "language": "python"
-    },
-    "authors": [
-      {
-        "name": "savitam"
-      }
-    ],
-    "language_info": {
-      "mimetype": "text/x-python",
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "pygments_lexer": "ipython3",
-      "name": "python",
-      "file_extension": ".py",
-      "nbconvert_exporter": "python",
-      "version": "3.6.6"
-    }
-  },
-  "nbformat": 4,
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.png)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Automated Machine Learning\n",
-        "_**Classification with Local Compute**_\n",
-        "\n",
-        "## Contents\n",
-        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#Setup)\n",
-        "1. [Data](#Data)\n",
-        "1. [Train](#Train)\n",
-        "1. [Results](#Results)\n",
-        "1. [Test](#Test)\n",
-        "\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Introduction\n",
-        "\n",
-        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
-        "\n",
-        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
-        "\n",
-        "In this notebook you will learn how to:\n",
-        "1. Create an `Experiment` in an existing `Workspace`.\n",
-        "2. Configure AutoML using `AutoMLConfig`.\n",
-        "3. Train the model using local compute.\n",
-        "4. Explore the results.\n",
-        "5. Test the best fitted model."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Setup\n",
-        "\n",
-        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "import logging\n",
-        "\n",
-        "from matplotlib import pyplot as plt\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "from sklearn import datasets\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl import AutoMLConfig"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Accessing the Azure ML workspace requires authentication with Azure.\n",
-        "\n",
-        "The default authentication is interactive authentication using the default tenant.  Executing the `ws = Workspace.from_config()` line in the cell below will prompt for authentication the first time that it is run.\n",
-        "\n",
-        "If you have multiple Azure tenants, you can specify the tenant by replacing the `ws = Workspace.from_config()` line in the cell below with the following:\n",
-        "\n",
-        "```\n",
-        "from azureml.core.authentication import InteractiveLoginAuthentication\n",
-        "auth = InteractiveLoginAuthentication(tenant_id = 'mytenantid')\n",
-        "ws = Workspace.from_config(auth = auth)\n",
-        "```\n",
-        "\n",
-        "If you need to run in an environment where interactive login is not possible, you can use Service Principal authentication by replacing the `ws = Workspace.from_config()` line in the cell below with the following:\n",
-        "\n",
-        "```\n",
-        "from azureml.core.authentication import ServicePrincipalAuthentication\n",
-        "auth = auth = ServicePrincipalAuthentication('mytenantid', 'myappid', 'mypassword')\n",
-        "ws = Workspace.from_config(auth = auth)\n",
-        "```\n",
-        "For more details, see [aka.ms/aml-notebook-auth](http://aka.ms/aml-notebook-auth)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "# Choose a name for the experiment and specify the project folder.\n",
-        "experiment_name = 'automl-classification'\n",
-        "project_folder = './sample_projects/automl-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",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
-        "outputDf.T"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Data\n",
-        "\n",
-        "This uses scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "digits = datasets.load_digits()\n",
-        "\n",
-        "# Exclude the first 100 rows from training so that they can be used for test.\n",
-        "X_train = digits.data[100:,:]\n",
-        "y_train = digits.target[100:]"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Train\n",
-        "\n",
-        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
-        "\n",
-        "|Property|Description|\n",
-        "|-|-|\n",
-        "|**task**|classification or regression|\n",
-        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
-        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-        "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
-        "|**n_cross_validations**|Number of cross validation splits.|\n",
-        "|\n",
-        "\n",
-        "Automated machine learning trains multiple machine learning pipelines.  Each pipelines training is known as an iteration.\n",
-        "* You can specify a maximum number of iterations using the `iterations` parameter.\n",
-        "* You can specify a maximum time for the run using the `experiment_timeout_minutes` parameter.\n",
-        "* If you specify neither the `iterations` nor the `experiment_timeout_minutes`, automated ML keeps running iterations while it continues to see improvements in the scores.\n",
-        "\n",
-        "The following example doesn't specify `iterations` or `experiment_timeout_minutes` and so runs until the scores stop improving.\n"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             primary_metric = 'AUC_weighted',\n",
-        "                             X = X_train, \n",
-        "                             y = y_train,\n",
-        "                             n_cross_validations = 3)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
-        "In this example, we specify `show_output = True` to print currently running iterations to the console."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "local_run = experiment.submit(automl_config, show_output = True)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "local_run"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "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": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "local_run = local_run.continue_experiment(X = X_train, \n",
-        "                                          y = y_train, \n",
-        "                                          show_output = True,\n",
-        "                                          iterations = 5)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Results"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Widget for Monitoring Runs\n",
-        "\n",
-        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
-        "\n",
-        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "from azureml.widgets import RunDetails\n",
-        "RunDetails(local_run).show() "
-      ],
-      "cell_type": "code"
-    },
-    {
-      "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": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "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": "code"
-    },
-    {
-      "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": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "best_run, fitted_model = local_run.get_output()\n",
-        "print(best_run)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Print the properties of the model\n",
-        "The fitted_model is a python object and you can read the different properties of the object.\n",
-        "The following shows printing hyperparameters for each step in the pipeline."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "from pprint import pprint\n",
-        "\n",
-        "def print_model(model, prefix=\"\"):\n",
-        "    for step in model.steps:\n",
-        "        print(prefix + step[0])\n",
-        "        if hasattr(step[1], 'estimators') and hasattr(step[1], 'weights'):\n",
-        "            pprint({'estimators': list(e[0] for e in step[1].estimators), 'weights': step[1].weights})\n",
-        "            print()\n",
-        "            for estimator in step[1].estimators:\n",
-        "                print_model(estimator[1], estimator[0]+ ' - ')\n",
-        "        elif hasattr(step[1], '_base_learners') and hasattr(step[1], '_meta_learner'):\n",
-        "            print(\"\\nMeta Learner\")\n",
-        "            pprint(step[1]._meta_learner)\n",
-        "            print()\n",
-        "            for estimator in step[1]._base_learners:\n",
-        "                print_model(estimator[1], estimator[0]+ ' - ')\n",
-        "        else:\n",
-        "            pprint(step[1].get_params())\n",
-        "            print()\n",
-        "            \n",
-        "print_model(fitted_model)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "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": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "lookup_metric = \"log_loss\"\n",
-        "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
-        "print(best_run)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "print_model(fitted_model)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Model from a Specific Iteration\n",
-        "Show the run and the model from the third iteration:"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "iteration = 3\n",
-        "third_run, third_model = local_run.get_output(iteration = iteration)\n",
-        "print(third_run)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "print_model(third_model)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Test \n",
-        "\n",
-        "#### Load Test Data"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "digits = datasets.load_digits()\n",
-        "X_test = digits.data[:10, :]\n",
-        "y_test = digits.target[:10]\n",
-        "images = digits.images[:10]"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Testing Our Best Fitted Model\n",
-        "We will try to predict 2 digits and see how our model works."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "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()"
-      ],
-      "cell_type": "code"
-    }
-  ],
-  "nbformat_minor": 2
-}

how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.yml

@@ -1,8 +0,0 @@
-name: auto-ml-classification
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - pandas_ml

how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb

@@ -0,0 +1,555 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/continous-retraining/auto-ml-continuous-retraining.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Automated Machine Learning \n",
+        "**Continous retraining using Pipelines and Time-Series TabularDataset**\n",
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "2. [Setup](#Setup)\n",
+        "3. [Compute](#Compute)\n",
+        "4. [Run Configuration](#Run-Configuration)\n",
+        "5. [Data Ingestion Pipeline](#Data-Ingestion-Pipeline)\n",
+        "6. [Training Pipeline](#Training-Pipeline)\n",
+        "7. [Publish Retraining Pipeline and Schedule](#Publish-Retraining-Pipeline-and-Schedule)\n",
+        "8. [Test Retraining](#Test-Retraining)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "An Enterprise workspace is required for this notebook. To learn more about creating an Enterprise workspace or upgrading to an Enterprise workspace from the Azure portal, please visit our [Workspace page.](https://docs.microsoft.com/azure/machine-learning/service/concept-workspace#upgrade)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "In this example we use AutoML and Pipelines to enable contious retraining of a model based on updates to the training dataset. We will create two pipelines, the first one to demonstrate a training dataset that gets updated over time. We leverage time-series capabilities of `TabularDataset` to achieve this. The second pipeline utilizes pipeline `Schedule` to trigger continuous retraining. \n",
+        "Make sure you have executed the [configuration notebook](../../../configuration.ipynb) before running this notebook.\n",
+        "In this notebook you will learn how to:\n",
+        "* Create an Experiment in an existing Workspace.\n",
+        "* Configure AutoML using AutoMLConfig.\n",
+        "* Create data ingestion pipeline to update a time-series based TabularDataset\n",
+        "* Create training pipeline to prepare data, run AutoML, register the model and setup pipeline triggers.\n",
+        "\n",
+        "## Setup\n",
+        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import logging\n",
+        "\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import datasets\n",
+        "\n",
+        "import azureml.core\n",
+        "from azureml.core.experiment import Experiment\n",
+        "from azureml.core.workspace import Workspace\n",
+        "from azureml.train.automl import AutoMLConfig"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Accessing the Azure ML workspace requires authentication with Azure.\n",
+        "\n",
+        "The default authentication is interactive authentication using the default tenant. Executing the ws = Workspace.from_config() line in the cell below will prompt for authentication the first time that it is run.\n",
+        "\n",
+        "If you have multiple Azure tenants, you can specify the tenant by replacing the ws = Workspace.from_config() line in the cell below with the following:\n",
+        "```\n",
+        "from azureml.core.authentication import InteractiveLoginAuthentication\n",
+        "auth = InteractiveLoginAuthentication(tenant_id = 'mytenantid')\n",
+        "ws = Workspace.from_config(auth = auth)\n",
+        "```\n",
+        "If you need to run in an environment where interactive login is not possible, you can use Service Principal authentication by replacing the ws = Workspace.from_config() line in the cell below with the following:\n",
+        "```\n",
+        "from azureml.core.authentication import ServicePrincipalAuthentication\n",
+        "auth = auth = ServicePrincipalAuthentication('mytenantid', 'myappid', 'mypassword')\n",
+        "ws = Workspace.from_config(auth = auth)\n",
+        "```\n",
+        "For more details, see aka.ms/aml-notebook-auth"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "dstor = ws.get_default_datastore()\n",
+        "\n",
+        "# Choose a name for the run history container in the workspace.\n",
+        "experiment_name = 'retrain-noaaweather'\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['Run History Name'] = experiment_name\n",
+        "pd.set_option('display.max_colwidth', -1)\n",
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Compute \n",
+        "\n",
+        "#### Create or Attach existing AmlCompute\n",
+        "\n",
+        "You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
+        "#### Creation of AmlCompute takes approximately 5 minutes. \n",
+        "If the AmlCompute with that name is already in your workspace this code will skip the 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 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, ComputeTarget\n",
+        "\n",
+        "# Choose a name for your cluster.\n",
+        "amlcompute_cluster_name = \"cpu-cluster-42\"\n",
+        "\n",
+        "found = False\n",
+        "# Check if this compute target already exists in the workspace.\n",
+        "cts = ws.compute_targets\n",
+        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
+        "    found = True\n",
+        "    print('Found existing compute target.')\n",
+        "    compute_target = cts[amlcompute_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 = 4)\n",
+        "\n",
+        "    # Create the cluster.\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
+        "    \n",
+        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
+        "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
+        "    compute_target.wait_for_completion(show_output = True, min_node_count = 0, timeout_in_minutes = 10)\n",
+        "    \n",
+        "     # For a more detailed view of current AmlCompute status, use get_status()."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Run Configuration"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.runconfig import CondaDependencies, DEFAULT_CPU_IMAGE, RunConfiguration\n",
+        "\n",
+        "# create a new RunConfig object\n",
+        "conda_run_config = RunConfiguration(framework=\"python\")\n",
+        "\n",
+        "# Set compute target to AmlCompute\n",
+        "conda_run_config.target = compute_target\n",
+        "\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]', 'applicationinsights', 'azureml-opendatasets'], \n",
+        "                              conda_packages=['numpy', 'py-xgboost'], \n",
+        "                              pin_sdk_version=False)\n",
+        "#cd.add_pip_package('azureml-explain-model')\n",
+        "conda_run_config.environment.python.conda_dependencies = cd\n",
+        "\n",
+        "print('run config is ready')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Data Ingestion Pipeline \n",
+        "For this demo, we will use NOAA weather data from [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/). You can replace this with your own dataset, or you can skip this pipeline if you already have a time-series based `TabularDataset`.\n",
+        "\n",
+        "### Upload Data Step\n",
+        "The data ingestion pipeline has a single step with a script to query the latest weather data and upload it to the blob store. During the first run, the script will create and register a time-series based `TabularDataset` with the past one week of weather data. For each subsequent run, the script will create a partition in the blob store by querying NOAA for new weather data since the last modified time of the dataset (`dataset.data_changed_time`) and creating a data.csv file."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import Pipeline, PipelineParameter\n",
+        "from azureml.pipeline.steps import PythonScriptStep\n",
+        "\n",
+        "# The name of the Dataset to create \n",
+        "dataset = \"NOAA-Weather-DS4\"\n",
+        "ds_name = PipelineParameter(name=\"ds_name\", default_value=dataset)\n",
+        "upload_data_step = PythonScriptStep(script_name=\"upload_weather_data.py\", \n",
+        "                                         allow_reuse=False,\n",
+        "                                         name=\"upload_weather_data\",\n",
+        "                                         arguments=[\"--ds_name\", ds_name],\n",
+        "                                         compute_target=compute_target, \n",
+        "                                         runconfig=conda_run_config)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Submit Pipeline Run"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "data_pipeline = Pipeline(\n",
+        "    description=\"pipeline_with_uploaddata\",\n",
+        "    workspace=ws,    \n",
+        "    steps=[upload_data_step])\n",
+        "data_pipeline_run = experiment.submit(data_pipeline, pipeline_parameters={\"ds_name\":dataset})"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "data_pipeline_run.wait_for_completion()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Training Pipeline\n",
+        "### Prepare Training Data Step\n",
+        "\n",
+        "Script to bring data into common X,y format. We need to set allow_reuse flag to False to allow the pipeline to run even when inputs don't change. We also need the name of the model to check the time the model was last trained."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import PipelineData\n",
+        "\n",
+        "target_column = PipelineParameter(\"target_column\", default_value=\"y\")\n",
+        "# The model name with which to register the trained model in the workspace.\n",
+        "model_name = PipelineParameter(\"model_name\", default_value=\"y\")\n",
+        "output_x = PipelineData(\"output_x\", datastore=dstor)\n",
+        "output_y = PipelineData(\"output_y\", datastore=dstor)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "data_prep_step = PythonScriptStep(script_name=\"check_data.py\", \n",
+        "                                         allow_reuse=False,\n",
+        "                                         name=\"check_data\",\n",
+        "                                         arguments=[\"--target_column\", target_column,\n",
+        "                                                       \"--output_x\", output_x,\n",
+        "                                                       \"--output_y\", output_y,\n",
+        "                                                       \"--ds_name\", ds_name,\n",
+        "                                                       \"--model_name\", model_name],\n",
+        "                                         outputs=[output_x, output_y], \n",
+        "                                         compute_target=compute_target, \n",
+        "                                         runconfig=conda_run_config)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### AutoMLStep\n",
+        "Create an AutoMLConfig and a training step."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.automl import AutoMLStep, AutoMLConfig\n",
+        "\n",
+        "automl_settings = {\n",
+        "    \"iteration_timeout_minutes\": 20,\n",
+        "    \"experiment_timeout_minutes\": 30,\n",
+        "    \"n_cross_validations\": 3,\n",
+        "    \"primary_metric\": 'r2_score',\n",
+        "    \"preprocess\": True,\n",
+        "    \"max_concurrent_iterations\": 3,\n",
+        "    \"max_cores_per_iteration\": -1,\n",
+        "    \"verbosity\": logging.INFO,\n",
+        "    \"enable_early_stopping\": True\n",
+        "}\n",
+        "\n",
+        "automl_config = AutoMLConfig(task = 'regression',\n",
+        "                             debug_log = 'automl_errors.log',\n",
+        "                             path = \".\",\n",
+        "                             compute_target=compute_target,\n",
+        "                             run_configuration=conda_run_config,\n",
+        "                             data_script = \"get_data.py\",\n",
+        "                             **automl_settings\n",
+        "                            )"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import PipelineData, TrainingOutput\n",
+        "\n",
+        "metrics_output_name = 'metrics_output'\n",
+        "best_model_output_name = 'best_model_output'\n",
+        "\n",
+        "metirics_data = PipelineData(name='metrics_data',\n",
+        "                           datastore=dstor,\n",
+        "                           pipeline_output_name=metrics_output_name,\n",
+        "                           training_output=TrainingOutput(type='Metrics'))\n",
+        "model_data = PipelineData(name='model_data',\n",
+        "                           datastore=dstor,\n",
+        "                           pipeline_output_name=best_model_output_name,\n",
+        "                           training_output=TrainingOutput(type='Model'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "automl_step = AutoMLStep(\n",
+        "    name='automl_module',\n",
+        "    automl_config=automl_config,\n",
+        "    inputs=[output_x, output_y],\n",
+        "    outputs=[metirics_data, model_data],\n",
+        "    allow_reuse=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Register Model Step\n",
+        "Script to register the model to the workspace. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "register_model_step = PythonScriptStep(script_name=\"register_model.py\",\n",
+        "                                       name=\"register_model\",\n",
+        "                                       allow_reuse=False,\n",
+        "                                       arguments=[\"--model_name\", model_name, \"--model_path\", model_data, \"--ds_name\", ds_name],\n",
+        "                                       inputs=[model_data],\n",
+        "                                       compute_target=compute_target,\n",
+        "                                       runconfig=conda_run_config)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Submit Pipeline Run"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "training_pipeline = Pipeline(\n",
+        "    description=\"training_pipeline\",\n",
+        "    workspace=ws,    \n",
+        "    steps=[data_prep_step, automl_step, register_model_step])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "training_pipeline_run = experiment.submit(training_pipeline, pipeline_parameters={\n",
+        "        \"target_column\": \"temperature\", \"ds_name\": dataset, \"model_name\": \"noaaweatherds\"})"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "training_pipeline_run.wait_for_completion()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Publish Retraining Pipeline and Schedule\n",
+        "Once we are happy with the pipeline, we can publish the training pipeline to the workspace and create a schedule to trigger on blob change. The schedule polls the blob store where the data is being uploaded and runs the retraining pipeline if there is a data change. A new version of the model will be registered to the workspace once the run is complete."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_name = \"Retraining-Pipeline-NOAAWeather\"\n",
+        "\n",
+        "published_pipeline = training_pipeline.publish(\n",
+        "    name=pipeline_name, \n",
+        "    description=\"Pipeline that retrains AutoML model\")\n",
+        "\n",
+        "published_pipeline"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import Schedule\n",
+        "schedule = Schedule.create(workspace=ws, name=\"RetrainingSchedule\",\n",
+        "                           pipeline_parameters={\"target_column\": \"temperature\",\"ds_name\": dataset, \"model_name\": \"noaaweatherds\"},\n",
+        "                           pipeline_id=published_pipeline.id, \n",
+        "                           experiment_name=experiment_name, \n",
+        "                           datastore=dstor,\n",
+        "                           wait_for_provisioning=True,\n",
+        "                           polling_interval=1440)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Test Retraining\n",
+        "Here we setup the data ingestion pipeline to run on a schedule, to verify that the retraining pipeline runs as expected. \n",
+        "\n",
+        "Note: \n",
+        "* Azure NOAA Weather data is updated daily and retraining will not trigger if there is no new data available. \n",
+        "* Depending on the polling interval set in the schedule, the retraining may take some time trigger after data ingestion pipeline completes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pipeline_name = \"DataIngestion-Pipeline-NOAAWeather\"\n",
+        "\n",
+        "published_pipeline = training_pipeline.publish(\n",
+        "    name=pipeline_name, \n",
+        "    description=\"Pipeline that updates NOAAWeather Dataset\")\n",
+        "\n",
+        "published_pipeline"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import Schedule\n",
+        "schedule = Schedule.create(workspace=ws, name=\"RetrainingSchedule-DataIngestion\",\n",
+        "                           pipeline_parameters={\"ds_name\":dataset},\n",
+        "                           pipeline_id=published_pipeline.id, \n",
+        "                           experiment_name=experiment_name, \n",
+        "                           datastore=dstor,\n",
+        "                           wait_for_provisioning=True,\n",
+        "                           polling_interval=1440)"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "vivijay"
+      }
+    ],
+    "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
+}

how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.yml

@@ -1,8 +1,9 @@
-name: auto-ml-classification-bank-marketing
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - pandas_ml
+name: auto-ml-continuous-retraining
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-pipeline
+  - azureml-widgets
+  - matplotlib
+  - pandas_ml

how-to-use-azureml/automated-machine-learning/continuous-retraining/check_data.py

@@ -0,0 +1,75 @@
+import argparse
+import os
+import azureml.core
+from datetime import datetime
+import pandas as pd
+import pytz
+from azureml.core import Dataset, Model
+from azureml.core.run import Run, _OfflineRun
+from azureml.core import Workspace
+
+run = Run.get_context()
+ws = None
+if type(run) == _OfflineRun:
+    ws = Workspace.from_config()
+else:
+    ws = run.experiment.workspace
+
+
+def write_output(df, path):
+    os.makedirs(path, exist_ok=True)
+    print("%s created" % path)
+    df.to_csv(path + "/part-00000", index=False)
+
+
+print("Check for new data and prepare the data")
+
+parser = argparse.ArgumentParser("split")
+parser.add_argument("--target_column", type=str, help="input split features")
+parser.add_argument("--ds_name", help="input dataset name")
+parser.add_argument("--model_name", help="name of the deployed model")
+parser.add_argument("--output_x", type=str,
+                    help="output features")
+parser.add_argument("--output_y", type=str,
+                    help="output labels")
+
+
+args = parser.parse_args()
+
+print("Argument 1(ds_name): %s" % args.ds_name)
+print("Argument 2(target_column): %s" % args.target_column)
+print("Argument 3(model_name): %s" % args.model_name)
+print("Argument 4(output_x): %s" % args.output_x)
+print("Argument 5(output_y): %s" % args.output_y)
+
+# Get the latest registered model
+try:
+    model = Model(ws, args.model_name)
+    last_train_time = model.created_time
+    print("Model was last trained on {0}.".format(last_train_time))
+except Exception as e:
+    print("Could not get last model train time.")
+    last_train_time = datetime.min.replace(tzinfo=pytz.UTC)
+
+train_ds = Dataset.get_by_name(ws, args.ds_name)
+dataset_changed_time = train_ds.data_changed_time
+
+if dataset_changed_time > last_train_time:
+    # New data is available since the model was last trained
+    print("Dataset was last updated on {0}. Retraining...".format(dataset_changed_time))
+    train_ds = train_ds.drop_columns(["partition_date"])
+    X_train = train_ds.drop_columns(
+        columns=[args.target_column]).to_pandas_dataframe()
+    y_train = train_ds.keep_columns(
+        columns=[args.target_column]).to_pandas_dataframe()
+
+    non_null = y_train[args.target_column].notnull()
+    y = y_train[non_null]
+    X = X_train[non_null]
+
+    if not (args.output_x is None and args.output_y is None):
+        write_output(X, args.output_x)
+        write_output(y, args.output_y)
+else:
+    print("Cancelling run since there is no new data.")
+    run.parent.cancel()

how-to-use-azureml/automated-machine-learning/continuous-retraining/get_data.py

@@ -0,0 +1,15 @@
+import os
+import pandas as pd
+
+
+def get_data():
+    print("In get_data")
+    print(os.environ['AZUREML_DATAREFERENCE_output_x'])
+    X_train = pd.read_csv(
+        os.environ['AZUREML_DATAREFERENCE_output_x'] + "/part-00000")
+    y_train = pd.read_csv(
+        os.environ['AZUREML_DATAREFERENCE_output_y'] + "/part-00000")
+
+    print(X_train.head(3))
+
+    return {"X": X_train.values, "y": y_train.values.flatten()}

how-to-use-azureml/automated-machine-learning/continuous-retraining/register_model.py

@@ -0,0 +1,33 @@
+from azureml.core.model import Model, Dataset
+from azureml.core.run import Run, _OfflineRun
+from azureml.core import Workspace
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--model_name")
+parser.add_argument("--model_path")
+parser.add_argument("--ds_name")
+args = parser.parse_args()
+
+print("Argument 1(model_name): %s" % args.model_name)
+print("Argument 2(model_path): %s" % args.model_path)
+print("Argument 3(ds_name): %s" % args.ds_name)
+
+run = Run.get_context()
+ws = None
+if type(run) == _OfflineRun:
+    ws = Workspace.from_config()
+else:
+    ws = run.experiment.workspace
+
+train_ds = Dataset.get_by_name(ws, args.ds_name)
+datasets = [(Dataset.Scenario.TRAINING, train_ds)]
+
+# Register model with training dataset
+
+model = Model.register(workspace=ws,
+                       model_path=args.model_path,
+                       model_name=args.model_name,
+                       datasets=datasets)
+
+print("Registered version {0} of model {1}".format(model.version, model.name))

how-to-use-azureml/automated-machine-learning/continuous-retraining/upload_weather_data.py

@@ -0,0 +1,89 @@
+import argparse
+import os
+from datetime import datetime
+from dateutil.relativedelta import relativedelta
+import pandas as pd
+import traceback
+from azureml.core import Dataset
+from azureml.core.run import Run, _OfflineRun
+from azureml.core import Workspace
+from azureml.opendatasets import NoaaIsdWeather
+
+run = Run.get_context()
+ws = None
+if type(run) == _OfflineRun:
+    ws = Workspace.from_config()
+else:
+    ws = run.experiment.workspace
+
+usaf_list = ['725724', '722149', '723090', '722159', '723910', '720279',
+             '725513', '725254', '726430', '720381', '723074', '726682',
+             '725486', '727883', '723177', '722075', '723086', '724053',
+             '725070', '722073', '726060', '725224', '725260', '724520',
+             '720305', '724020', '726510', '725126', '722523', '703333',
+             '722249', '722728', '725483', '722972', '724975', '742079',
+             '727468', '722193', '725624', '722030', '726380', '720309',
+             '722071', '720326', '725415', '724504', '725665', '725424',
+             '725066']
+
+
+def get_noaa_data(start_time, end_time):
+    columns = ['usaf', 'wban', 'datetime', 'latitude', 'longitude', 'elevation',
+               'windAngle', 'windSpeed', 'temperature', 'stationName', 'p_k']
+    isd = NoaaIsdWeather(start_time, end_time, cols=columns)
+    noaa_df = isd.to_pandas_dataframe()
+    df_filtered = noaa_df[noaa_df["usaf"].isin(usaf_list)]
+    df_filtered.reset_index(drop=True)
+    print("Received {0} rows of training data between {1} and {2}".format(
+        df_filtered.shape[0], start_time, end_time))
+    return df_filtered
+
+
+print("Check for new data and prepare the data")
+
+parser = argparse.ArgumentParser("split")
+parser.add_argument("--ds_name", help="name of the Dataset to update")
+args = parser.parse_args()
+
+print("Argument 1(ds_name): %s" % args.ds_name)
+
+dstor = ws.get_default_datastore()
+register_dataset = False
+try:
+    ds = Dataset.get_by_name(ws, args.ds_name)
+    end_time_last_slice = ds.data_changed_time.replace(tzinfo=None)
+    print("Dataset {0} last updated on {1}".format(args.ds_name,
+                                                   end_time_last_slice))
+except Exception as e:
+    print(traceback.format_exc())
+    print("Dataset with name {0} not found, registering new dataset.".format(args.ds_name))
+    register_dataset = True
+    end_time_last_slice = datetime.today() - relativedelta(weeks=1)
+
+end_time = datetime.utcnow()
+train_df = get_noaa_data(end_time_last_slice, end_time)
+
+if train_df.size > 0:
+    print("Received {0} rows of new data after {0}.".format(
+        train_df.shape[0], end_time_last_slice))
+    folder_name = "{}/{:04d}/{:02d}/{:02d}/{:02d}/{:02d}/{:02d}".format(args.ds_name, end_time.year,
+                                                                        end_time.month, end_time.day,
+                                                                        end_time.hour, end_time.minute,
+                                                                        end_time.second)
+    file_path = "{0}/data.csv".format(folder_name)
+
+    # Add a new partition to the registered dataset
+    os.makedirs(folder_name, exist_ok=True)
+    train_df.to_csv(file_path, index=False)
+
+    dstor.upload_files(files=[file_path],
+                       target_path=folder_name,
+                       overwrite=True,
+                       show_progress=True)
+
+    if register_dataset:
+        ds = Dataset.Tabular.from_delimited_files(dstor.path("{}/**/*.csv".format(
+            args.ds_name)), partition_format='/{partition_date:yyyy/MM/dd/hh/mm/ss}/data.csv')
+        ds.register(ws, name=args.ds_name)
+else:
+    print("No new data since {0}.".format(end_time_last_slice))

how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb

@@ -1,526 +0,0 @@
-{
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "name": "python36",
-      "language": "python"
-    },
-    "authors": [
-      {
-        "name": "savitam"
-      }
-    ],
-    "language_info": {
-      "mimetype": "text/x-python",
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "pygments_lexer": "ipython3",
-      "name": "python",
-      "file_extension": ".py",
-      "nbconvert_exporter": "python",
-      "version": "3.6.5"
-    }
-  },
-  "nbformat": 4,
-  "cells": [
-    {
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.png)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "# Automated Machine Learning\n",
-        "_**Prepare Data using `azureml.dataprep` for Remote Execution (AmlCompute)**_\n",
-        "\n",
-        "## Contents\n",
-        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#Setup)\n",
-        "1. [Data](#Data)\n",
-        "1. [Train](#Train)\n",
-        "1. [Results](#Results)\n",
-        "1. [Test](#Test)"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Introduction\n",
-        "In this example we showcase how you can use the `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone; full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
-        "\n",
-        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
-        "\n",
-        "In this notebook you will learn how to:\n",
-        "1. Define data loading and preparation steps in a `Dataflow` using `azureml.dataprep`.\n",
-        "2. Pass the `Dataflow` to AutoML for a local run.\n",
-        "3. Pass the `Dataflow` to AutoML for a remote run."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Setup\n",
-        "\n",
-        "Currently, Data Prep only supports __Ubuntu 16__ and __Red Hat Enterprise Linux 7__. We are working on supporting more linux distros."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "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": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "import logging\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"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "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",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
-        "outputDf.T"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Data"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
-        "# The data referenced here was a 1MB simple random sample of the Chicago Crime data into a local temporary directory.\n",
-        "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
-        "# and convert column types manually.\n",
-        "example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n",
-        "dflow = dprep.auto_read_file(example_data).skip(1)  # Remove the header row.\n",
-        "dflow.get_profile()"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "# As `Primary Type` is our y data, we need to drop the values those are null in this column.\n",
-        "dflow = dflow.drop_nulls('Primary Type')\n",
-        "dflow.head(5)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "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.\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": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "X = dflow.drop_columns(columns=['Primary Type', 'FBI Code'])\n",
-        "y = dflow.keep_columns(columns=['Primary Type'], validate_column_exists=True)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Train\n",
-        "\n",
-        "This creates a general AutoML settings object applicable for both local and remote runs."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "automl_settings = {\n",
-        "    \"iteration_timeout_minutes\" : 10,\n",
-        "    \"iterations\" : 2,\n",
-        "    \"primary_metric\" : 'AUC_weighted',\n",
-        "    \"preprocess\" : True,\n",
-        "    \"verbosity\" : logging.INFO\n",
-        "}"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Create or Attach an AmlCompute cluster"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "from azureml.core.compute import AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
-        "\n",
-        "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"cpu-cluster\"\n",
-        "\n",
-        "found = False\n",
-        "\n",
-        "# Check if this compute target already exists in the workspace.\n",
-        "\n",
-        "cts = ws.compute_targets\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
-        "    found = True\n",
-        "    print('Found existing compute target.')\n",
-        "    compute_target = cts[amlcompute_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\",\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "\n",
-        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "    compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "\n",
-        "     # For a more detailed view of current AmlCompute status, use get_status()."
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "\n",
-        "# create a new RunConfig object\n",
-        "conda_run_config = RunConfiguration(framework=\"python\")\n",
-        "\n",
-        "# Set compute target to AmlCompute\n",
-        "conda_run_config.target = compute_target\n",
-        "conda_run_config.environment.docker.enabled = True\n",
-        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
-        "\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy','py-xgboost<=0.80'])\n",
-        "conda_run_config.environment.python.conda_dependencies = cd"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "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": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "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"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "remote_run = experiment.submit(automl_config, show_output = True)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "remote_run"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "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": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "remote_run.clean_preprocessor_cache()"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "### Cancelling Runs\n",
-        "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "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": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Results"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Widget for Monitoring Runs\n",
-        "\n",
-        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
-        "\n",
-        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "from azureml.widgets import RunDetails\n",
-        "RunDetails(remote_run).show()"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "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": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "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": "code"
-    },
-    {
-      "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": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "best_run, fitted_model = remote_run.get_output()\n",
-        "print(best_run)\n",
-        "print(fitted_model)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "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": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "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": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Model from a Specific Iteration\n",
-        "Show the run and the model from the first iteration:"
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "iteration = 0\n",
-        "best_run, fitted_model = remote_run.get_output(iteration = iteration)\n",
-        "print(best_run)\n",
-        "print(fitted_model)"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "## Test\n",
-        "\n",
-        "#### Load Test Data\n",
-        "For the test data, it should have the same preparation step as the train data. Otherwise it might get failed at the preprocessing step."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "dflow_test = dprep.auto_read_file(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv').skip(1)\n",
-        "dflow_test = dflow_test.drop_nulls('Primary Type')"
-      ],
-      "cell_type": "code"
-    },
-    {
-      "metadata": {},
-      "source": [
-        "#### Testing Our Best Fitted Model\n",
-        "We will use confusion matrix to see how our model works."
-      ],
-      "cell_type": "markdown"
-    },
-    {
-      "metadata": {},
-      "outputs": [],
-      "execution_count": null,
-      "source": [
-        "from pandas_ml import ConfusionMatrix\n",
-        "\n",
-        "y_test = dflow_test.keep_columns(columns=['Primary Type']).to_pandas_dataframe()\n",
-        "X_test = dflow_test.drop_columns(columns=['Primary Type', 'FBI Code']).to_pandas_dataframe()\n",
-        "\n",
-        "\n",
-        "ypred = fitted_model.predict(X_test)\n",
-        "\n",
-        "cm = ConfusionMatrix(y_test['Primary Type'], ypred)\n",
-        "\n",
-        "print(cm)\n",
-        "\n",
-        "cm.plot()"
-      ],
-      "cell_type": "code"
-    }
-  ],
-  "nbformat_minor": 2
-}