Update 12/2 2

Dockerfiles/1.0.10/Dockerfile

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

Dockerfiles/1.0.15/Dockerfile

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

Dockerfiles/1.0.17/Dockerfile

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

Dockerfiles/1.0.18/Dockerfile

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

Dockerfiles/1.0.2/Dockerfile

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

Dockerfiles/1.0.21/Dockerfile

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

Dockerfiles/1.0.23/Dockerfile

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

Dockerfiles/1.0.30/Dockerfile

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

Dockerfiles/1.0.33/Dockerfile

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

Dockerfiles/1.0.41/Dockerfile

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

Dockerfiles/1.0.43/Dockerfile

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

Dockerfiles/1.0.6/Dockerfile

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

Dockerfiles/1.0.8/Dockerfile

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

LICENSE

@@ -0,0 +1,21 @@
+    MIT License
+
+    Copyright (c) Microsoft Corporation. All rights reserved.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in all
+    copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+    SOFTWARE

README.md

@@ -0,0 +1,53 @@
+For full documentation for Azure Machine Learning service, visit **https://aka.ms/aml-docs**.
+# Sample Notebooks for Azure Machine Learning service
+
+To run the notebooks in this repository use one of these methods:
+
+## Use Azure Notebooks - Jupyter based notebooks in the Azure cloud
+
+1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
+[Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks.
+1. Follow the instructions in the [00.configuration](00.configuration.ipynb) notebook to create and connect to a workspace.
+1. Open one of the sample notebooks.
+    
+    **Make sure the Azure Notebook kernel is set to `Python 3.6`** when you open a notebook.  
+    
+    ![set kernel to Python 3.6](images/python36.png)
+
+
+## **Use your own notebook server**
+
+Video walkthrough:
+
+[![get started video](images/yt_cover.png)](https://youtu.be/VIsXeTuW3FU)
+
+1. Setup a Jupyter Notebook server and [install the Azure Machine Learning SDK](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python).
+1. Clone [this repository](https://aka.ms/aml-notebooks).
+1. You may need to install other packages for specific notebook. 
+    - For example, to run the Azure Machine Learning Data Prep notebooks, install the extra dataprep SDK:
+    ```
+     pip install --upgrade azureml-dataprep
+    ```
+
+1. Start your notebook server.
+1. Follow the instructions in the [00.configuration](00.configuration.ipynb) notebook to create and connect to a workspace.
+1. Open one of the sample notebooks.
+
+
+
+> Note: **Looking for automated machine learning samples?**
+> For your convenience, you can use an installation script instead of the steps below for the automated ML notebooks. Go to the [automl folder README](automl/README.md) and follow the instructions.  The script installs all  packages needed for notebooks in that folder.
+
+# Contributing
+
+This project welcomes contributions and suggestions.  Most contributions require you to agree to a
+Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
+the rights to use your contribution. For details, visit https://cla.microsoft.com.
+
+When you submit a pull request, a CLA-bot will automatically determine whether you need to provide
+a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions
+provided by the bot. You will only need to do this once across all repos using our CLA.
+
+This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
+For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
+contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

configuration.ipynb

@@ -1,389 +1,376 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-    "\n",
-    "Licensed under the MIT License."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![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 AZUREML-SDK-VERSION of the Azure ML SDK\")\n",
-    "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you are using an older version of the SDK then this notebook was created using, you should upgrade your SDK.\n",
-    "\n",
-    "### 3. Azure Container Instance registration\n",
-    "Azure Machine Learning uses of [Azure Container Instance (ACI)](https://azure.microsoft.com/services/container-instances) to deploy dev/test web services. An Azure subscription needs to be registered to use ACI.  If you or the subscription owner have not yet registered ACI on your subscription, you will need to use the [Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest) and execute the following commands.  Note that if you ran through the AML [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) you have already registered ACI. \n",
-    "\n",
-    "```shell\n",
-    "# check to see if ACI is already registered\n",
-    "(myenv) $ az provider show -n Microsoft.ContainerInstance -o table\n",
-    "\n",
-    "# if ACI is not registered, run this command.\n",
-    "# note you need to be the subscription owner in order to execute this command successfully.\n",
-    "(myenv) $ az provider register -n Microsoft.ContainerInstance\n",
-    "```\n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Configure your Azure ML workspace\n",
-    "\n",
-    "### Workspace parameters\n",
-    "\n",
-    "To use an AML Workspace, you will need to import the Azure ML SDK and supply the following information:\n",
-    "* Your subscription id\n",
-    "* A resource group name\n",
-    "* (optional) The region that will host your workspace\n",
-    "* A name for your workspace\n",
-    "\n",
-    "You can get your subscription ID from the [Azure portal](https://portal.azure.com).\n",
-    "\n",
-    "You will also need access to a [_resource group_](https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-group-overview#resource-groups), which organizes Azure resources and provides a default region for the resources in a group.  You can see what resource groups to which you have access, or create a new one in the [Azure portal](https://portal.azure.com).  If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n",
-    "\n",
-    "The region to host your workspace will be used if you are creating a new workspace.  You do not need to specify this if you are using an existing workspace. You can find the list of supported regions [here](https://azure.microsoft.com/en-us/global-infrastructure/services/?products=machine-learning-service).  You should pick a region that is close to your location or that contains your data.\n",
-    "\n",
-    "The name for your workspace is unique within the subscription and should be descriptive enough to discern among other AML Workspaces.  The subscription may be used only by you, or it may be used by your department or your entire enterprise, so choose a name that makes sense for your situation.\n",
-    "\n",
-    "The following cell allows you to specify your workspace parameters.  This cell uses the python method `os.getenv` to read values from environment variables which is useful for automation.  If no environment variable exists, the parameters will be set to the specified default values.  \n",
-    "\n",
-    "If you ran the Azure Machine Learning [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) in Azure Notebooks, you already have a configured workspace!  You can go to your Azure Machine Learning Getting Started library, view *config.json* file, and copy-paste the values for subscription ID, resource group and workspace name below.\n",
-    "\n",
-    "Replace the default values in the cell below with your workspace parameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "subscription_id = os.getenv(\"SUBSCRIPTION_ID\", default=\"<my-subscription-id>\")\n",
-    "resource_group = os.getenv(\"RESOURCE_GROUP\", default=\"<my-resource-group>\")\n",
-    "workspace_name = os.getenv(\"WORKSPACE_NAME\", default=\"<my-workspace-name>\")\n",
-    "workspace_region = os.getenv(\"WORKSPACE_REGION\", default=\"eastus2\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Access your workspace\n",
-    "\n",
-    "The following cell uses the Azure ML SDK to attempt to load the workspace specified by your parameters.  If this cell succeeds, your notebook library will be configured to access the workspace from all notebooks using the `Workspace.from_config()` method.  The cell can fail if the specified workspace doesn't exist or you don't have permissions to access it. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core import Workspace\n",
-    "\n",
-    "try:\n",
-    "    ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name)\n",
-    "    # write the details of the workspace to a configuration file to the notebook library\n",
-    "    ws.write_config()\n",
-    "    print(\"Workspace configuration succeeded. Skip the workspace creation steps below\")\n",
-    "except:\n",
-    "    print(\"Workspace not accessible. Change your parameters or create a new workspace below\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create a new workspace\n",
-    "\n",
-    "If you don't have an existing workspace and are the owner of the subscription or resource group, you can create a new workspace.  If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n",
-    "\n",
-    "**Note**: As with other Azure services, there are limits on certain resources (for example AmlCompute quota) associated with the Azure ML service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n",
-    "\n",
-    "This cell will create an Azure ML workspace for you in a subscription provided you have the correct permissions.\n",
-    "\n",
-    "This will fail if:\n",
-    "* You do not have permission to create a workspace in the resource group\n",
-    "* You do not have permission to create a resource group if it's non-existing.\n",
-    "* You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription\n",
-    "\n",
-    "If workspace creation fails, please work with your IT admin to provide you with the appropriate permissions or to provision the required resources.\n",
-    "\n",
-    "**Note**: A Basic workspace is created by default. If you would like to create an Enterprise workspace, please specify sku = 'enterprise'.\n",
-    "Please visit our [pricing page](https://azure.microsoft.com/en-us/pricing/details/machine-learning/) for more details on our Enterprise edition.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "create workspace"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "from azureml.core import Workspace\n",
-    "\n",
-    "# Create the workspace using the specified parameters\n",
-    "ws = Workspace.create(name = workspace_name,\n",
-    "                      subscription_id = subscription_id,\n",
-    "                      resource_group = resource_group, \n",
-    "                      location = workspace_region,\n",
-    "                      create_resource_group = True,\n",
-    "                      sku = 'basic',\n",
-    "                      exist_ok = True)\n",
-    "ws.get_details()\n",
-    "\n",
-    "# write the details of the workspace to a configuration file to the notebook library\n",
-    "ws.write_config()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create compute resources for your training experiments\n",
-    "\n",
-    "Many of the sample notebooks use Azure ML managed compute (AmlCompute) to train models using a dynamically scalable pool of compute. In this section you will create default compute clusters for use by the other notebooks and any other operations you choose.\n",
-    "\n",
-    "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
-    "\n",
-    "To create a cluster, you need to specify a compute configuration that specifies the type of machine to be used and the scalability behaviors.  Then you choose a name for the cluster that is unique within the workspace that can be used to address the cluster later.\n",
-    "\n",
-    "The cluster parameters are:\n",
-    "* vm_size - this describes the virtual machine type and size used in the cluster.  All machines in the cluster are the same type.  You can get the list of vm sizes available in your region by using the CLI command\n",
-    "\n",
-    "```shell\n",
-    "az vm list-skus -o tsv\n",
-    "```\n",
-    "* min_nodes - this sets the minimum size of the cluster.  If you set the minimum to 0 the cluster will shut down all nodes while not in use.  Setting this number to a value higher than 0 will allow for faster start-up times, but you will also be billed when the cluster is not in use.\n",
-    "* max_nodes - this sets the maximum size of the cluster.  Setting this to a larger number allows for more concurrency and a greater distributed processing of scale-out jobs.\n",
-    "\n",
-    "\n",
-    "To create a **CPU** cluster now, run the cell below. The autoscale settings mean that the cluster will scale down to 0 nodes when inactive and up to 4 nodes when busy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
-    "from azureml.core.compute_target import ComputeTargetException\n",
-    "\n",
-    "# Choose a name for your CPU cluster\n",
-    "cpu_cluster_name = \"cpu-cluster\"\n",
-    "\n",
-    "# Verify that cluster does not exist already\n",
-    "try:\n",
-    "    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
-    "    print(\"Found existing cpu-cluster\")\n",
-    "except ComputeTargetException:\n",
-    "    print(\"Creating new cpu-cluster\")\n",
-    "    \n",
-    "    # Specify the configuration for the new cluster\n",
-    "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_D2_V2\",\n",
-    "                                                           min_nodes=0,\n",
-    "                                                           max_nodes=4)\n",
-    "\n",
-    "    # Create the cluster with the specified name and configuration\n",
-    "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
-    "    \n",
-    "    # Wait for the cluster to complete, show the output log\n",
-    "    cpu_cluster.wait_for_completion(show_output=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To create a **GPU** cluster, run the cell below. Note that your subscription must have sufficient quota for GPU VMs or the command will fail. To increase quota, see [these instructions](https://docs.microsoft.com/en-us/azure/azure-supportability/resource-manager-core-quotas-request). "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
-    "from azureml.core.compute_target import ComputeTargetException\n",
-    "\n",
-    "# Choose a name for your GPU cluster\n",
-    "gpu_cluster_name = \"gpu-cluster\"\n",
-    "\n",
-    "# Verify that cluster does not exist already\n",
-    "try:\n",
-    "    gpu_cluster = ComputeTarget(workspace=ws, name=gpu_cluster_name)\n",
-    "    print(\"Found existing gpu cluster\")\n",
-    "except ComputeTargetException:\n",
-    "    print(\"Creating new gpu-cluster\")\n",
-    "    \n",
-    "    # Specify the configuration for the new cluster\n",
-    "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_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"
-   }
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Configuration\n",
+        "\n",
+        "_**Setting up your Azure Machine Learning services workspace and configuring your notebook library**_\n",
+        "\n",
+        "---\n",
+        "---\n",
+        "\n",
+        "## Table of Contents\n",
+        "\n",
+        "1. [Introduction](#Introduction)\n",
+        "    1. What is an Azure Machine Learning workspace\n",
+        "1. [Setup](#Setup)\n",
+        "    1. Azure subscription\n",
+        "    1. Azure ML SDK and other library installation\n",
+        "    1. Azure Container Instance registration\n",
+        "1. [Configure your Azure ML Workspace](#Configure%20your%20Azure%20ML%20workspace)\n",
+        "    1. Workspace parameters\n",
+        "    1. Access your workspace\n",
+        "    1. Create a new workspace\n",
+        "    1. Create compute resources\n",
+        "1. [Next steps](#Next%20steps)\n",
+        "\n",
+        "---\n",
+        "\n",
+        "## Introduction\n",
+        "\n",
+        "This notebook configures your library of notebooks to connect to an Azure Machine Learning (ML) workspace.  In this case, a library contains all of the notebooks in the current folder and any nested folders.  You can configure this notebook library to use an existing workspace or create a new workspace.\n",
+        "\n",
+        "Typically you will need to run this notebook only once per notebook library as all other notebooks will use connection information that is written here.  If you want to redirect your notebook library to work with a different workspace, then you should re-run this notebook.\n",
+        "\n",
+        "In this notebook you will\n",
+        "* Learn about getting an Azure subscription\n",
+        "* Specify your workspace parameters\n",
+        "* Access or create your workspace\n",
+        "* Add a default compute cluster for your workspace\n",
+        "\n",
+        "### What is an Azure Machine Learning workspace\n",
+        "\n",
+        "An Azure ML Workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an Azure ML Workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, deployment, inferencing, and the monitoring of deployed models."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup\n",
+        "\n",
+        "This section describes activities required before you can access any Azure ML services functionality."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 1. Azure Subscription\n",
+        "\n",
+        "In order to create an Azure ML Workspace, first you need access to an Azure subscription.  An Azure subscription allows you to manage storage, compute, and other assets in the Azure cloud.  You can [create a new subscription](https://azure.microsoft.com/en-us/free/) or access existing subscription information from the [Azure portal](https://portal.azure.com).  Later in this notebook you will need information such as your subscription ID in order to create and access AML workspaces.\n",
+        "\n",
+        "### 2. Azure ML SDK and other library installation\n",
+        "\n",
+        "If you are running in your own environment, follow [SDK installation instructions](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment).  If you are running in Azure Notebooks or another Microsoft managed environment, the SDK is already installed.\n",
+        "\n",
+        "Also install following libraries to your environment. Many of the example notebooks depend on them\n",
+        "\n",
+        "```\n",
+        "(myenv) $ conda install -y matplotlib tqdm scikit-learn\n",
+        "```\n",
+        "\n",
+        "Once installation is complete, the following cell checks the Azure ML SDK version:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "install"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "import azureml.core\n",
+        "\n",
+        "print(\"This notebook was created using version 1.0.2 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If you are using an older version of the SDK then this notebook was created using, you should upgrade your SDK.\n",
+        "\n",
+        "### 3. Azure Container Instance registration\n",
+        "Azure Machine Learning uses of [Azure Container Instance (ACI)](https://azure.microsoft.com/services/container-instances) to deploy dev/test web services. An Azure subscription needs to be registered to use ACI.  If you or the subscription owner have not yet registered ACI on your subscription, you will need to use the [Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest) and execute the following commands.  Note that if you ran through the AML [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) you have already registered ACI. \n",
+        "\n",
+        "```shell\n",
+        "# check to see if ACI is already registered\n",
+        "(myenv) $ az provider show -n Microsoft.ContainerInstance -o table\n",
+        "\n",
+        "# if ACI is not registered, run this command.\n",
+        "# note you need to be the subscription owner in order to execute this command successfully.\n",
+        "(myenv) $ az provider register -n Microsoft.ContainerInstance\n",
+        "```\n",
+        "\n",
+        "---"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Configure your Azure ML workspace\n",
+        "\n",
+        "### Workspace parameters\n",
+        "\n",
+        "To use an AML Workspace, you will need to import the Azure ML SDK and supply the following information:\n",
+        "* Your subscription id\n",
+        "* A resource group name\n",
+        "* (optional) The region that will host your workspace\n",
+        "* A name for your workspace\n",
+        "\n",
+        "You can get your subscription ID from the [Azure portal](https://portal.azure.com).\n",
+        "\n",
+        "You will also need access to a [_resource group_](https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-group-overview#resource-groups), which organizes Azure resources and provides a default region for the resources in a group.  You can see what resource groups to which you have access, or create a new one in the [Azure portal](https://portal.azure.com).  If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n",
+        "\n",
+        "The region to host your workspace will be used if you are creating a new workspace.  You do not need to specify this if you are using an existing workspace. You can find the list of supported regions [here](https://azure.microsoft.com/en-us/global-infrastructure/services/?products=machine-learning-service).  You should pick a region that is close to your location or that contains your data.\n",
+        "\n",
+        "The name for your workspace is unique within the subscription and should be descriptive enough to discern among other AML Workspaces.  The subscription may be used only by you, or it may be used by your department or your entire enterprise, so choose a name that makes sense for your situation.\n",
+        "\n",
+        "The following cell allows you to specify your workspace parameters.  This cell uses the python method `os.getenv` to read values from environment variables which is useful for automation.  If no environment variable exists, the parameters will be set to the specified default values.  \n",
+        "\n",
+        "If you ran the Azure Machine Learning [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) in Azure Notebooks, you already have a configured workspace!  You can go to your Azure Machine Learning Getting Started library, view *config.json* file, and copy-paste the values for subscription ID, resource group and workspace name below.\n",
+        "\n",
+        "Replace the default values in the cell below with your workspace parameters"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "subscription_id = os.getenv(\"SUBSCRIPTION_ID\", default=\"<my-subscription-id>\")\n",
+        "resource_group = os.getenv(\"RESOURCE_GROUP\", default=\"<my-resource-group>\")\n",
+        "workspace_name = os.getenv(\"WORKSPACE_NAME\", default=\"<my-workspace-name>\")\n",
+        "workspace_region = os.getenv(\"WORKSPACE_REGION\", default=\"eastus2\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Access your workspace\n",
+        "\n",
+        "The following cell uses the Azure ML SDK to attempt to load the workspace specified by your parameters.  If this cell succeeds, your notebook library will be configured to access the workspace from all notebooks using the `Workspace.from_config()` method.  The cell can fail if the specified workspace doesn't exist or you don't have permissions to access it. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace\n",
+        "\n",
+        "try:\n",
+        "    ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name)\n",
+        "    # write the details of the workspace to a configuration file to the notebook library\n",
+        "    ws.write_config()\n",
+        "    print(\"Workspace configuration succeeded. Skip the workspace creation steps below\")\n",
+        "except:\n",
+        "    print(\"Workspace not accessible. Change your parameters or create a new workspace below\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create a new workspace\n",
+        "\n",
+        "If you don't have an existing workspace and are the owner of the subscription or resource group, you can create a new workspace.  If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n",
+        "\n",
+        "**Note**: As with other Azure services, there are limits on certain resources (for example AmlCompute quota) associated with the Azure ML service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n",
+        "\n",
+        "This cell will create an Azure ML workspace for you in a subscription provided you have the correct permissions.\n",
+        "\n",
+        "This will fail if:\n",
+        "* You do not have permission to create a workspace in the resource group\n",
+        "* You do not have permission to create a resource group if it's non-existing.\n",
+        "* You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription\n",
+        "\n",
+        "If workspace creation fails, please work with your IT admin to provide you with the appropriate permissions or to provision the required resources."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "create workspace"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace\n",
+        "\n",
+        "# Create the workspace using the specified parameters\n",
+        "ws = Workspace.create(name = workspace_name,\n",
+        "                      subscription_id = subscription_id,\n",
+        "                      resource_group = resource_group, \n",
+        "                      location = workspace_region,\n",
+        "                      create_resource_group = True,\n",
+        "                      exist_ok = True)\n",
+        "ws.get_details()\n",
+        "\n",
+        "# write the details of the workspace to a configuration file to the notebook library\n",
+        "ws.write_config()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create compute resources for your training experiments\n",
+        "\n",
+        "Many of the sample notebooks use Azure ML managed compute (AmlCompute) to train models using a dynamically scalable pool of compute. In this section you will create default compute clusters for use by the other notebooks and any other operations you choose.\n",
+        "\n",
+        "To create a cluster, you need to specify a compute configuration that specifies the type of machine to be used and the scalability behaviors.  Then you choose a name for the cluster that is unique within the workspace that can be used to address the cluster later.\n",
+        "\n",
+        "The cluster parameters are:\n",
+        "* vm_size - this describes the virtual machine type and size used in the cluster.  All machines in the cluster are the same type.  You can get the list of vm sizes available in your region by using the CLI command\n",
+        "\n",
+        "```shell\n",
+        "az vm list-skus -o tsv\n",
+        "```\n",
+        "* min_nodes - this sets the minimum size of the cluster.  If you set the minimum to 0 the cluster will shut down all nodes while note in use.  Setting this number to a value higher than 0 will allow for faster start-up times, but you will also be billed when the cluster is not in use.\n",
+        "* max_nodes - this sets the maximum size of the cluster.  Setting this to a larger number allows for more concurrency and a greater distributed processing of scale-out jobs.\n",
+        "\n",
+        "\n",
+        "To create a **CPU** cluster now, run the cell below. The autoscale settings mean that the cluster will scale down to 0 nodes when inactive and up to 4 nodes when busy."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# Choose a name for your CPU cluster\n",
+        "cpu_cluster_name = \"cpucluster\"\n",
+        "\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
+        "    print(\"Found existing cpucluster\")\n",
+        "except ComputeTargetException:\n",
+        "    print(\"Creating new cpucluster\")\n",
+        "    \n",
+        "    # Specify the configuration for the new cluster\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_D2_V2\",\n",
+        "                                                           min_nodes=0,\n",
+        "                                                           max_nodes=4)\n",
+        "\n",
+        "    # Create the cluster with the specified name and configuration\n",
+        "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
+        "    \n",
+        "    # Wait for the cluster to complete, show the output log\n",
+        "    cpu_cluster.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To create a **GPU** cluster, run the cell below. Note that your subscription must have sufficient quota for GPU VMs or the command will fail. To increase quota, see [these instructions](https://docs.microsoft.com/en-us/azure/azure-supportability/resource-manager-core-quotas-request). "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# Choose a name for your GPU cluster\n",
+        "gpu_cluster_name = \"gpucluster\"\n",
+        "\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    gpu_cluster = ComputeTarget(workspace=ws, name=gpu_cluster_name)\n",
+        "    print(\"Found existing gpu cluster\")\n",
+        "except ComputeTargetException:\n",
+        "    print(\"Creating new gpucluster\")\n",
+        "    \n",
+        "    # Specify the configuration for the new cluster\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_NC6\",\n",
+        "                                                           min_nodes=0,\n",
+        "                                                           max_nodes=4)\n",
+        "    # Create the cluster with the specified name and configuration\n",
+        "    gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, compute_config)\n",
+        "\n",
+        "    # Wait for the cluster to complete, show the output log\n",
+        "    gpu_cluster.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "---\n",
+        "\n",
+        "## Next steps\n",
+        "\n",
+        "In this notebook you configured this notebook library to connect easily to an Azure ML workspace.  You can copy this notebook to your own libraries to connect them to you workspace, or use it to bootstrap new workspaces completely.\n",
+        "\n",
+        "If you came here from another notebook, you can return there and complete that exercise, or you can try out the [Tutorials](./tutorials) or jump into \"how-to\" notebooks and start creating and deploying models.  A good place to start is the [train in notebook](./how-to-use-azureml/training/train-in-notebook) example that walks through a simplified but complete end to end machine learning process."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
   ],
-  "kernelspec": {
-   "display_name": "Python 3.6",
-   "language": "python",
-   "name": "python36"
+  "metadata": {
+    "authors": [
+      {
+        "name": "roastala"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.7"
+    }
   },
-  "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
-}
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

configuration.yml

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

how-to-use-azureml/README.md

@@ -0,0 +1,16 @@
+## Examples to get started with Azure Machine Learning service
+
+Learn how to use Azure Machine Learning services for experimentation and model management.
+
+As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) notebook first to set up your Azure ML Workspace. Then, run the notebooks in following recommended order.
+
+ * [train-within-notebook](train-within-notebook/train-within-notebook.ipynb): Train a model hile tracking run history, and learn how to deploy the model as web service to Azure Container Instance.
+ * [train-on-local](train-on-local/train-on-local.ipynb): Learn how to submit a run and use Azure ML managed run configuration.
+* [train-on-aci](train-on-aci/train-on-aci.ipynb): Submit a remote run on serverless Docker-based compute.
+* [train-on-remote-vm](train-on-remote-vm/train-on-remote-vm.ipynb): Use Data Science Virtual Machine as a target for remote runs.
+* [logging-api](logging-api/logging-api.ipynb): Learn about the details of logging metrics to run history.
+* [register-model-create-image-deploy-service](register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb): Learn about the details of model management.
+* [production-deploy-to-aks](production-deploy-to-aks/production-deploy-to-aks.ipynb) Deploy a model to production at scale on Azure Kubernetes Service.
+* [enable-data-collection-for-models-in-aks](enable-data-collection-for-models-in-aks/enable-data-collection-for-models-in-aks.ipynb) Learn about data collection APIs for deployed model.
+* [enable-app-insights-in-production-service](enable-app-insights-in-production-serviceenable-app-insights-in-production-service.ipynb) Learn how to use App Insights with production web service.
+ 

how-to-use-azureml/automated-machine-learning/01.auto-ml-classification.ipynb

@@ -0,0 +1,414 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 01: Classification with Local Compute\n",
+    "\n",
+    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you will learn how to:\n",
+    "1. Create an `Experiment` in an existing `Workspace`.\n",
+    "2. Configure AutoML using `AutoMLConfig`.\n",
+    "3. Train the model using local compute.\n",
+    "4. Explore the results.\n",
+    "5. Test the best fitted model.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose a name for the experiment and specify the project folder.\n",
+    "experiment_name = 'automl-local-classification'\n",
+    "project_folder = './sample_projects/automl-local-classification'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Training Data\n",
+    "\n",
+    "This uses scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "\n",
+    "digits = datasets.load_digits()\n",
+    "\n",
+    "# Exclude the first 100 rows from training so that they can be used for test.\n",
+    "X_train = digits.data[100:,:]\n",
+    "y_train = digits.target[100:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|classification or regression|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**n_cross_validations**|Number of cross validation splits.|\n",
+    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
+    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             primary_metric = 'AUC_weighted',\n",
+    "                             iteration_timeout_minutes = 60,\n",
+    "                             iterations = 25,\n",
+    "                             n_cross_validations = 3,\n",
+    "                             verbosity = logging.INFO,\n",
+    "                             X = X_train, \n",
+    "                             y = y_train,\n",
+    "                             path = project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
+    "In this example, we specify `show_output = True` to print currently running iterations to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Optionally, you can continue an interrupted local run by calling `continue_experiment` without the `iterations` parameter, or run more iterations for a completed run by specifying the `iterations` parameter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = local_run.continue_experiment(X = X_train, \n",
+    "                                          y = y_train, \n",
+    "                                          show_output = True,\n",
+    "                                          iterations = 5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(local_run).show() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(local_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model that has the smallest `log_loss` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lookup_metric = \"log_loss\"\n",
+    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration\n",
+    "Show the run and the model from the third iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 3\n",
+    "third_run, third_model = local_run.get_output(iteration = iteration)\n",
+    "print(third_run)\n",
+    "print(third_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test the Best Fitted Model\n",
+    "\n",
+    "#### Load Test Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:10, :]\n",
+    "y_test = digits.target[:10]\n",
+    "images = digits.images[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Testing Our Best Fitted Model\n",
+    "We will try to predict 2 digits and see how our model works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Randomly select digits and test.\n",
+    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+    "    print(index)\n",
+    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+    "    label = y_test[index]\n",
+    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
+    "    fig = plt.figure(1, figsize = (3,3))\n",
+    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "    ax1.set_title(title)\n",
+    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "    plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/02.auto-ml-regression.ipynb

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

how-to-use-azureml/automated-machine-learning/03.auto-ml-remote-execution.ipynb

@@ -0,0 +1,507 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 03: Remote Execution using DSVM (Ubuntu)\n",
+    "\n",
+    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you wiil learn how to:\n",
+    "1. Create an `Experiment` in an existing `Workspace`.\n",
+    "2. Attach an existing DSVM to a workspace.\n",
+    "3. Configure AutoML using `AutoMLConfig`.\n",
+    "4. Train the model using the DSVM.\n",
+    "5. Explore the results.\n",
+    "6. Test the best fitted model.\n",
+    "\n",
+    "In addition, this notebook showcases the following features:\n",
+    "- **Parallel** executions for iterations\n",
+    "- **Asynchronous** tracking of progress\n",
+    "- **Cancellation** of individual iterations or the entire run\n",
+    "- Retrieving models for any iteration or logged metric\n",
+    "- Specifying AutoML settings as `**kwargs`\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "import time\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose a name for the run history container in the workspace.\n",
+    "experiment_name = 'automl-remote-dsvm4'\n",
+    "project_folder = './sample_projects/automl-remote-dsvm4'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a Remote Linux DSVM\n",
+    "**Note:** If creation fails with a message about Marketplace purchase eligibilty, start creation of a DSVM through the [Azure portal](https://portal.azure.com), and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled this setting, you can exit the portal without actually creating the DSVM, and creation of the DSVM through the notebook should work.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import DsvmCompute\n",
+    "\n",
+    "dsvm_name = 'mydsvma'\n",
+    "try:\n",
+    "    dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
+    "    print('Found an existing DSVM.')\n",
+    "except:\n",
+    "    print('Creating a new DSVM.')\n",
+    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
+    "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
+    "    dsvm_compute.wait_for_completion(show_output = True)\n",
+    "    print(\"Waiting one minute for ssh to be accessible\")\n",
+    "    time.sleep(60) # Wait for ssh to be accessible"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.runconfig import RunConfiguration\n",
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "\n",
+    "# create a new RunConfig object\n",
+    "conda_run_config = RunConfiguration(framework=\"python\")\n",
+    "\n",
+    "# Set compute target to the Linux DSVM\n",
+    "conda_run_config.target = dsvm_compute\n",
+    "\n",
+    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
+    "conda_run_config.environment.python.conda_dependencies = cd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Get Data File\n",
+    "For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
+    "In this example, the `get_data()` function returns data using scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(project_folder):\n",
+    "    os.makedirs(project_folder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile $project_folder/get_data.py\n",
+    "\n",
+    "from sklearn import datasets\n",
+    "from scipy import sparse\n",
+    "import numpy as np\n",
+    "\n",
+    "def get_data():\n",
+    "    \n",
+    "    digits = datasets.load_digits()\n",
+    "    X_train = digits.data[100:,:]\n",
+    "    y_train = digits.target[100:]\n",
+    "\n",
+    "    return { \"X\" : X_train, \"y\" : y_train }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML <a class=\"anchor\" id=\"Instantiate-AutoML-Remote-DSVM\"></a>\n",
+    "\n",
+    "You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
+    "\n",
+    "**Note:** When using Remote DSVM, you can't pass Numpy arrays directly to the fit method.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**n_cross_validations**|Number of cross validation splits.|\n",
+    "|**max_concurrent_iterations**|Maximum number of iterations to execute in parallel. This should be less than the number of cores on the DSVM.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_settings = {\n",
+    "    \"iteration_timeout_minutes\": 10,\n",
+    "    \"iterations\": 20,\n",
+    "    \"n_cross_validations\": 5,\n",
+    "    \"primary_metric\": 'AUC_weighted',\n",
+    "    \"preprocess\": False,\n",
+    "    \"max_concurrent_iterations\": 2,\n",
+    "    \"verbosity\": logging.INFO\n",
+    "}\n",
+    "\n",
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             path = project_folder, \n",
+    "                             run_configuration=conda_run_config,\n",
+    "                             data_script = project_folder + \"/get_data.py\",\n",
+    "                             **automl_settings\n",
+    "                            )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note:** The first run on a new DSVM may take several minutes to prepare the environment."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run.\n",
+    "\n",
+    "In this example, we specify `show_output = False` to suppress console output while the run is in progress."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remote_run = experiment.submit(automl_config, show_output = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results\n",
+    "\n",
+    "#### Loading Executed Runs\n",
+    "In case you need to load a previously executed run, enable the cell below and replace the `run_id` value."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "remote_run = AutoMLRun(experiment=experiment, run_id = 'AutoML_480d3ed6-fc94-44aa-8f4e-0b945db9d3ef')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "You can click on a pipeline to see run properties and output logs.  Logs are also available on the DSVM under `/tmp/azureml_run/{iterationid}/azureml-logs`\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(remote_run).show() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Wait until the run finishes.\n",
+    "remote_run.wait_for_completion(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(remote_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cancelling Runs\n",
+    "\n",
+    "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cancel the ongoing experiment and stop scheduling new iterations.\n",
+    "# remote_run.cancel()\n",
+    "\n",
+    "# Cancel iteration 1 and move onto iteration 2.\n",
+    "# remote_run.cancel_iteration(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = remote_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model which has the smallest `log_loss` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lookup_metric = \"log_loss\"\n",
+    "best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration\n",
+    "Show the run and the model from the third iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 3\n",
+    "third_run, third_model = remote_run.get_output(iteration = iteration)\n",
+    "print(third_run)\n",
+    "print(third_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test the Best Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n",
+    "\n",
+    "#### Load Test Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:10, :]\n",
+    "y_test = digits.target[:10]\n",
+    "images = digits.images[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Test Our Best Fitted Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Randomly select digits and test.\n",
+    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+    "    print(index)\n",
+    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+    "    label = y_test[index]\n",
+    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
+    "    fig = plt.figure(1, figsize=(3,3))\n",
+    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "    ax1.set_title(title)\n",
+    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "    plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/03b.auto-ml-remote-batchai.ipynb

@@ -0,0 +1,528 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 03: Remote Execution using Batch AI\n",
+    "\n",
+    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you would see\n",
+    "1. Create an `Experiment` in an existing `Workspace`.\n",
+    "2. Attach an existing Batch AI compute to a workspace.\n",
+    "3. Configure AutoML using `AutoMLConfig`.\n",
+    "4. Train the model using Batch AI.\n",
+    "5. Explore the results.\n",
+    "6. Test the best fitted model.\n",
+    "\n",
+    "In addition this notebook showcases the following features\n",
+    "- **Parallel** executions for iterations\n",
+    "- **Asynchronous** tracking of progress\n",
+    "- **Cancellation** of individual iterations or the entire run\n",
+    "- Retrieving models for any iteration or logged metric\n",
+    "- Specifying AutoML settings as `**kwargs`\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose a name for the run history container in the workspace.\n",
+    "experiment_name = 'automl-remote-batchai'\n",
+    "project_folder = './sample_projects/automl-remote-batchai'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Batch AI Cluster\n",
+    "The cluster is created as Machine Learning Compute and will appear under your workspace.\n",
+    "\n",
+    "**Note:** The creation of the Batch AI cluster can take over 10 minutes, please be patient.\n",
+    "\n",
+    "As with other Azure services, there are limits on certain resources (e.g. Batch AI cluster size) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import AmlCompute\n",
+    "from azureml.core.compute import ComputeTarget\n",
+    "\n",
+    "# Choose a name for your cluster.\n",
+    "batchai_cluster_name = \"automlcl\"\n",
+    "\n",
+    "found = False\n",
+    "# Check if this compute target already exists in the workspace.\n",
+    "cts = ws.compute_targets\n",
+    "if batchai_cluster_name in cts and cts[batchai_cluster_name].type == 'BatchAI':\n",
+    "    found = True\n",
+    "    print('Found existing compute target.')\n",
+    "    compute_target = cts[batchai_cluster_name]\n",
+    "    \n",
+    "if not found:\n",
+    "    print('Creating a new compute target...')\n",
+    "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
+    "                                                                #vm_priority = 'lowpriority', # optional\n",
+    "                                                                max_nodes = 6)\n",
+    "\n",
+    "    # Create the cluster.\n",
+    "    compute_target = ComputeTarget.create(ws, batchai_cluster_name, provisioning_config)\n",
+    "    \n",
+    "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
+    "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
+    "    compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
+    "    \n",
+    "     # For a more detailed view of current Batch AI cluster status, use the 'status' property."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.runconfig import RunConfiguration\n",
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "\n",
+    "# create a new RunConfig object\n",
+    "conda_run_config = RunConfiguration(framework=\"python\")\n",
+    "\n",
+    "# Set compute target to the Batch AI cluster\n",
+    "conda_run_config.target = compute_target\n",
+    "conda_run_config.environment.docker.enabled = True\n",
+    "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
+    "\n",
+    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
+    "conda_run_config.environment.python.conda_dependencies = cd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Get Data File\n",
+    "For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
+    "In this example, the `get_data()` function returns data using scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(project_folder):\n",
+    "    os.makedirs(project_folder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile $project_folder/get_data.py\n",
+    "\n",
+    "from sklearn import datasets\n",
+    "from scipy import sparse\n",
+    "import numpy as np\n",
+    "\n",
+    "def get_data():\n",
+    "    \n",
+    "    digits = datasets.load_digits()\n",
+    "    X_train = digits.data\n",
+    "    y_train = digits.target\n",
+    "\n",
+    "    return { \"X\" : X_train, \"y\" : y_train }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
+    "\n",
+    "You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
+    "\n",
+    "**Note:** When using Batch AI, you can't pass Numpy arrays directly to the fit method.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**n_cross_validations**|Number of cross validation splits.|\n",
+    "|**max_concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of cores on the DSVM.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_settings = {\n",
+    "    \"iteration_timeout_minutes\": 2,\n",
+    "    \"iterations\": 20,\n",
+    "    \"n_cross_validations\": 5,\n",
+    "    \"primary_metric\": 'AUC_weighted',\n",
+    "    \"preprocess\": False,\n",
+    "    \"max_concurrent_iterations\": 5,\n",
+    "    \"verbosity\": logging.INFO\n",
+    "}\n",
+    "\n",
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             path = project_folder,\n",
+    "                             run_configuration=conda_run_config,\n",
+    "                             data_script = project_folder + \"/get_data.py\",\n",
+    "                             **automl_settings\n",
+    "                            )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run.\n",
+    "In this example, we specify `show_output = False` to suppress console output while the run is in progress."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remote_run = experiment.submit(automl_config, show_output = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results\n",
+    "\n",
+    "#### Loading executed runs\n",
+    "In case you need to load a previously executed run, enable the cell below and replace the `run_id` value."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "remote_run = AutoMLRun(experiment = experiment, run_id = 'AutoML_5db13491-c92a-4f1d-b622-8ab8d973a058')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "You can click on a pipeline to see run properties and output logs.  Logs are also available on the DSVM under `/tmp/azureml_run/{iterationid}/azureml-logs`\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remote_run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(remote_run).show() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Wait until the run finishes.\n",
+    "remote_run.wait_for_completion(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(remote_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cancelling Runs\n",
+    "\n",
+    "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cancel the ongoing experiment and stop scheduling new iterations.\n",
+    "# remote_run.cancel()\n",
+    "\n",
+    "# Cancel iteration 1 and move onto iteration 2.\n",
+    "# remote_run.cancel_iteration(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = remote_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model which has the smallest `log_loss` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lookup_metric = \"log_loss\"\n",
+    "best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration\n",
+    "Show the run and the model from the third iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 3\n",
+    "third_run, third_model = remote_run.get_output(iteration=iteration)\n",
+    "print(third_run)\n",
+    "print(third_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n",
+    "\n",
+    "#### Load Test Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:10, :]\n",
+    "y_test = digits.target[:10]\n",
+    "images = digits.images[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Testing Our Best Fitted Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Randomly select digits and test.\n",
+    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+    "    print(index)\n",
+    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+    "    label = y_test[index]\n",
+    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
+    "    fig = plt.figure(1, figsize=(3,3))\n",
+    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "    ax1.set_title(title)\n",
+    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "    plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "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/04.auto-ml-remote-execution-text-data-blob-store.ipynb

@@ -0,0 +1,539 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Auto ML 04: Remote Execution with Text Data from Azure Blob Storage\n",
+    "\n",
+    "In this example we use the [Burning Man 2016 dataset](https://innovate.burningman.org/datasets-page/) to showcase how you can use AutoML to handle text data from Azure Blob Storage.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you will learn how to:\n",
+    "1. Create an `Experiment` in an existing `Workspace`.\n",
+    "2. Attach an existing DSVM to a workspace.\n",
+    "3. Configure AutoML using `AutoMLConfig`.\n",
+    "4. Train the model using the DSVM.\n",
+    "5. Explore the results.\n",
+    "6. Test the best fitted model.\n",
+    "\n",
+    "In addition this notebook showcases the following features\n",
+    "- **Parallel** executions for iterations\n",
+    "- **Asynchronous** tracking of progress\n",
+    "- **Cancellation** of individual iterations or the entire run\n",
+    "- Retrieving models for any iteration or logged metric\n",
+    "- Specifying AutoML settings as `**kwargs`\n",
+    "- Handling **text** data using the `preprocess` flag\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose a name for the run history container in the workspace.\n",
+    "experiment_name = 'automl-remote-dsvm-blobstore'\n",
+    "project_folder = './sample_projects/automl-remote-dsvm-blobstore'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data=output, index=['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Attach a Remote Linux DSVM\n",
+    "To use a remote Docker compute target:\n",
+    "1. Create a Linux DSVM in Azure, following these [quick instructions](https://docs.microsoft.com/en-us/azure/machine-learning/desktop-workbench/how-to-create-dsvm-hdi). Make sure you use the Ubuntu flavor (not CentOS). Make sure that disk space is available under `/tmp` because AutoML creates files under `/tmp/azureml_run`s. The DSVM should have more cores than the number of parallel runs that you plan to enable. It should also have at least 4GB per core.\n",
+    "2. Enter the IP address, user name and password below.\n",
+    "\n",
+    "**Note:** By default, SSH runs on port 22 and you don't need to change the port number below. If you've configured SSH to use a different port, change `dsvm_ssh_port` accordinglyaddress. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on changing SSH ports for security reasons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import ComputeTarget, RemoteCompute\n",
+    "import time\n",
+    "\n",
+    "# Add your VM information below\n",
+    "# If a compute with the specified compute_name already exists, it will be used and the dsvm_ip_addr, dsvm_ssh_port, \n",
+    "# dsvm_username and dsvm_password will be ignored.\n",
+    "compute_name  = 'mydsvmb'\n",
+    "dsvm_ip_addr  = '<<ip_addr>>'\n",
+    "dsvm_ssh_port = 22\n",
+    "dsvm_username = '<<username>>'\n",
+    "dsvm_password = '<<password>>'\n",
+    "\n",
+    "if compute_name in ws.compute_targets:\n",
+    "    print('Using existing compute.')\n",
+    "    dsvm_compute = ws.compute_targets[compute_name]\n",
+    "else:\n",
+    "    attach_config = RemoteCompute.attach_configuration(address=dsvm_ip_addr, username=dsvm_username, password=dsvm_password, ssh_port=dsvm_ssh_port)\n",
+    "    ComputeTarget.attach(workspace=ws, name=compute_name, attach_configuration=attach_config)\n",
+    "\n",
+    "    while ws.compute_targets[compute_name].provisioning_state == 'Creating':\n",
+    "        time.sleep(1)\n",
+    "\n",
+    "    dsvm_compute = ws.compute_targets[compute_name]\n",
+    "    \n",
+    "    if dsvm_compute.provisioning_state == 'Failed':\n",
+    "        print('Attached failed.')\n",
+    "        print(dsvm_compute.provisioning_errors)\n",
+    "        dsvm_compute.detach()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.runconfig import RunConfiguration\n",
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "\n",
+    "# create a new RunConfig object\n",
+    "conda_run_config = RunConfiguration(framework=\"python\")\n",
+    "\n",
+    "# Set compute target to the Linux DSVM\n",
+    "conda_run_config.target = dsvm_compute\n",
+    "\n",
+    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
+    "conda_run_config.environment.python.conda_dependencies = cd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Get Data File\n",
+    "For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
+    "In this example, the `get_data()` function returns a [dictionary](README.md#getdata)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(project_folder):\n",
+    "    os.makedirs(project_folder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile $project_folder/get_data.py\n",
+    "\n",
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "def get_data():\n",
+    "    # Load Burning Man 2016 data.\n",
+    "    df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
+    "                     delimiter=\"\\t\", quotechar='\"')\n",
+    "    # Get integer labels.\n",
+    "    le = LabelEncoder()\n",
+    "    le.fit(df[\"Label\"].values)\n",
+    "    y = le.transform(df[\"Label\"].values)\n",
+    "    X = df.drop([\"Label\"], axis=1)\n",
+    "\n",
+    "    X_train, _, y_train, _ = train_test_split(X, y, test_size = 0.1, random_state = 42)\n",
+    "\n",
+    "    return { \"X\" : X_train, \"y\" : y_train }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### View data\n",
+    "\n",
+    "You can execute the `get_data()` function locally to view the training data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%run  $project_folder/get_data.py\n",
+    "data_dict = get_data()\n",
+    "df = data_dict[\"X\"]\n",
+    "y = data_dict[\"y\"]\n",
+    "pd.set_option('display.max_colwidth', 15)\n",
+    "df['Label'] = pd.Series(y, index=df.index)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
+    "\n",
+    "You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
+    "\n",
+    "**Note:** When using Remote DSVM, you can't pass Numpy arrays directly to the fit method.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**n_cross_validations**|Number of cross validation splits.|\n",
+    "|**max_concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of cores on the DSVM.|\n",
+    "|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.|\n",
+    "|**enable_cache**|Setting this to *True* enables preprocess done once and reuse the same preprocessed data for all the iterations. Default value is True.\n",
+    "|**max_cores_per_iteration**|Indicates how many cores on the compute target would be used to train a single pipeline.<br>Default is *1*; you can set it to *-1* to use all cores.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_settings = {\n",
+    "    \"iteration_timeout_minutes\": 60,\n",
+    "    \"iterations\": 4,\n",
+    "    \"n_cross_validations\": 5,\n",
+    "    \"primary_metric\": 'AUC_weighted',\n",
+    "    \"preprocess\": True,\n",
+    "    \"max_cores_per_iteration\": 2\n",
+    "}\n",
+    "\n",
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             path = project_folder,\n",
+    "                             run_configuration=conda_run_config,\n",
+    "                             data_script = project_folder + \"/get_data.py\",\n",
+    "                             **automl_settings\n",
+    "                            )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remote_run = experiment.submit(automl_config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "You can click on a pipeline to see run properties and output logs.  Logs are also available on the DSVM under `/tmp/azureml_run/{iterationid}/azureml-logs`\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(remote_run).show() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Wait until the run finishes.\n",
+    "remote_run.wait_for_completion(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pre-process cache cleanup\n",
+    "The preprocess data gets cache at user default file store. When the run is completed the cache can be cleaned by running below cell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remote_run.clean_preprocessor_cache()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(remote_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cancelling Runs\n",
+    "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cancel the ongoing experiment and stop scheduling new iterations.\n",
+    "# remote_run.cancel()\n",
+    "\n",
+    "# Cancel iteration 1 and move onto iteration 2.\n",
+    "# remote_run.cancel_iteration(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = remote_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model which has the smallest `accuracy` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lookup_metric = \"accuracy\"\n",
+    "# best_run, fitted_model = remote_run.get_output(metric = lookup_metric)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 0\n",
+    "zero_run, zero_model = remote_run.get_output(iteration = iteration)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sklearn\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from pandas_ml import ConfusionMatrix\n",
+    "\n",
+    "df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
+    "                     delimiter=\"\\t\", quotechar='\"')\n",
+    "\n",
+    "# get integer labels\n",
+    "le = LabelEncoder()\n",
+    "le.fit(df[\"Label\"].values)\n",
+    "y = le.transform(df[\"Label\"].values)\n",
+    "X = df.drop([\"Label\"], axis=1)\n",
+    "\n",
+    "_, X_test, _, y_test = train_test_split(X, y, test_size=0.1, random_state=42)\n",
+    "\n",
+    "\n",
+    "ypred = fitted_model.predict(X_test.values)\n",
+    "\n",
+    "\n",
+    "ypred_strings = le.inverse_transform(ypred)\n",
+    "ytest_strings = le.inverse_transform(y_test)\n",
+    "\n",
+    "cm = ConfusionMatrix(ytest_strings, ypred_strings)\n",
+    "\n",
+    "print(cm)\n",
+    "\n",
+    "cm.plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/05.auto-ml-missing-data-Blacklist-Early-Termination.ipynb

@@ -0,0 +1,381 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 05: Blacklisting Models, Early Termination, and Handling Missing Data\n",
+    "\n",
+    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for handling missing values in data. We also provide a stopping metric indicating a target for the primary metrics so that AutoML can terminate the run without necessarly going through all the iterations. Finally, if you want to avoid a certain pipeline, we allow you to specify a blacklist of algorithms that AutoML will ignore for this run.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you will learn how to:\n",
+    "1. Create an `Experiment` in an existing `Workspace`.\n",
+    "2. Configure AutoML using `AutoMLConfig`.\n",
+    "4. Train the model.\n",
+    "5. Explore the results.\n",
+    "6. Test the best fitted model.\n",
+    "\n",
+    "In addition this notebook showcases the following features\n",
+    "- **Blacklisting** certain pipelines\n",
+    "- Specifying **target metrics** to indicate stopping criteria\n",
+    "- Handling **missing data** in the input\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose a name for the experiment.\n",
+    "experiment_name = 'automl-local-missing-data'\n",
+    "project_folder = './sample_projects/automl-local-missing-data'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data=output, index=['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating missing data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy import sparse\n",
+    "\n",
+    "digits = datasets.load_digits()\n",
+    "X_train = digits.data[10:,:]\n",
+    "y_train = digits.target[10:]\n",
+    "\n",
+    "# Add missing values in 75% of the lines.\n",
+    "missing_rate = 0.75\n",
+    "n_missing_samples = int(np.floor(X_train.shape[0] * missing_rate))\n",
+    "missing_samples = np.hstack((np.zeros(X_train.shape[0] - n_missing_samples, dtype=np.bool), np.ones(n_missing_samples, dtype=np.bool)))\n",
+    "rng = np.random.RandomState(0)\n",
+    "rng.shuffle(missing_samples)\n",
+    "missing_features = rng.randint(0, X_train.shape[1], n_missing_samples)\n",
+    "X_train[np.where(missing_samples)[0], missing_features] = np.nan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(data = X_train)\n",
+    "df['Label'] = pd.Series(y_train, index=df.index)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.  This includes setting `experiment_exit_score`, which should cause the run to complete before the `iterations` count is reached.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|classification or regression|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**n_cross_validations**|Number of cross validation splits.|\n",
+    "|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.|\n",
+    "|**experiment_exit_score**|*double* value indicating the target for *primary_metric*. <br>Once the target is surpassed the run terminates.|\n",
+    "|**blacklist_models**|*List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run.<br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGD</i><br><i>MultinomialNaiveBayes</i><br><i>BernoulliNaiveBayes</i><br><i>SVM</i><br><i>LinearSVM</i><br><i>KNN</i><br><i>DecisionTree</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>GradientBoosting</i><br><i>TensorFlowDNN</i><br><i>TensorFlowLinearClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i>|\n",
+    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers.|\n",
+    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             primary_metric = 'AUC_weighted',\n",
+    "                             iteration_timeout_minutes = 60,\n",
+    "                             iterations = 20,\n",
+    "                             n_cross_validations = 5,\n",
+    "                             preprocess = True,\n",
+    "                             experiment_exit_score = 0.9984,\n",
+    "                             blacklist_models = ['KNN','LinearSVM'],\n",
+    "                             verbosity = logging.INFO,\n",
+    "                             X = X_train, \n",
+    "                             y = y_train,\n",
+    "                             path = project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
+    "In this example, we specify `show_output = True` to print currently running iterations to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(local_run).show() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(local_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model which has the smallest `accuracy` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lookup_metric = \"accuracy\"\n",
+    "# best_run, fitted_model = local_run.get_output(metric = lookup_metric)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration\n",
+    "Show the run and the model from the third iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# iteration = 3\n",
+    "# best_run, fitted_model = local_run.get_output(iteration = iteration)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing the best Fitted Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:10, :]\n",
+    "y_test = digits.target[:10]\n",
+    "images = digits.images[:10]\n",
+    "\n",
+    "# Randomly select digits and test.\n",
+    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+    "    print(index)\n",
+    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+    "    label = y_test[index]\n",
+    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
+    "    fig = plt.figure(1, figsize=(3,3))\n",
+    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "    ax1.set_title(title)\n",
+    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "    plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/06.auto-ml-sparse-data-train-test-split.ipynb

@@ -0,0 +1,384 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 06: Train Test Split and Handling Sparse Data\n",
+    "\n",
+    "In this example we use the scikit-learn's [20newsgroup](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_20newsgroups.html) to showcase how you can use AutoML for handling sparse data and how to specify custom cross validations splits.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you will learn how to:\n",
+    "1. Create an `Experiment` in an existing `Workspace`.\n",
+    "2. Configure AutoML using `AutoMLConfig`.\n",
+    "4. Train the model.\n",
+    "5. Explore the results.\n",
+    "6. Test the best fitted model.\n",
+    "\n",
+    "In addition this notebook showcases the following features\n",
+    "- Explicit train test splits \n",
+    "- Handling **sparse data** in the input"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# choose a name for the experiment\n",
+    "experiment_name = 'automl-local-missing-data'\n",
+    "# project folder\n",
+    "project_folder = './sample_projects/automl-local-missing-data'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data=output, index=['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating Sparse Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_20newsgroups\n",
+    "from sklearn.feature_extraction.text import HashingVectorizer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "remove = ('headers', 'footers', 'quotes')\n",
+    "categories = [\n",
+    "    'alt.atheism',\n",
+    "    'talk.religion.misc',\n",
+    "    'comp.graphics',\n",
+    "    'sci.space',\n",
+    "]\n",
+    "data_train = fetch_20newsgroups(subset = 'train', categories = categories,\n",
+    "                                shuffle = True, random_state = 42,\n",
+    "                                remove = remove)\n",
+    "\n",
+    "X_train, X_valid, y_train, y_valid = train_test_split(data_train.data, data_train.target, test_size = 0.33, random_state = 42)\n",
+    "\n",
+    "\n",
+    "vectorizer = HashingVectorizer(stop_words = 'english', alternate_sign = False,\n",
+    "                               n_features = 2**16)\n",
+    "X_train = vectorizer.transform(X_train)\n",
+    "X_valid = vectorizer.transform(X_valid)\n",
+    "\n",
+    "summary_df = pd.DataFrame(index = ['No of Samples', 'No of Features'])\n",
+    "summary_df['Train Set'] = [X_train.shape[0], X_train.shape[1]]\n",
+    "summary_df['Validation Set'] = [X_valid.shape[0], X_valid.shape[1]]\n",
+    "summary_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|classification or regression|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.<br>**Note:** If input data is sparse, you cannot use *True*.|\n",
+    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
+    "|**X_valid**|(sparse) array-like, shape = [n_samples, n_features] for the custom validation set.|\n",
+    "|**y_valid**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification for the custom validation set.|\n",
+    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             primary_metric = 'AUC_weighted',\n",
+    "                             iteration_timeout_minutes = 60,\n",
+    "                             iterations = 5,\n",
+    "                             preprocess = False,\n",
+    "                             verbosity = logging.INFO,\n",
+    "                             X = X_train, \n",
+    "                             y = y_train,\n",
+    "                             X_valid = X_valid, \n",
+    "                             y_valid = y_valid, \n",
+    "                             path = project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
+    "In this example, we specify `show_output = True` to print currently running iterations to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(local_run).show() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(local_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "    \n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model which has the smallest `accuracy` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lookup_metric = \"accuracy\"\n",
+    "# best_run, fitted_model = local_run.get_output(metric = lookup_metric)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration\n",
+    "Show the run and the model from the third iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# iteration = 3\n",
+    "# best_run, fitted_model = local_run.get_output(iteration = iteration)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing the Best Fitted Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load test data.\n",
+    "from pandas_ml import ConfusionMatrix\n",
+    "\n",
+    "data_test = fetch_20newsgroups(subset = 'test', categories = categories,\n",
+    "                               shuffle = True, random_state = 42,\n",
+    "                               remove = remove)\n",
+    "\n",
+    "X_test = vectorizer.transform(data_test.data)\n",
+    "y_test = data_test.target\n",
+    "\n",
+    "# Test our best pipeline.\n",
+    "\n",
+    "y_pred = fitted_model.predict(X_test)\n",
+    "y_pred_strings = [data_test.target_names[i] for i in y_pred]\n",
+    "y_test_strings = [data_test.target_names[i] for i in y_test]\n",
+    "\n",
+    "cm = ConfusionMatrix(y_test_strings, y_pred_strings)\n",
+    "print(cm)\n",
+    "cm.plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/07.auto-ml-exploring-previous-runs.ipynb

@@ -0,0 +1,336 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 07: Exploring Previous Runs\n",
+    "\n",
+    "In this example we present some examples on navigating previously executed runs. We also show how you can download a fitted model for any previous run.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you will learn how to:\n",
+    "1. List all experiments in a workspace.\n",
+    "2. List all AutoML runs in an experiment.\n",
+    "3. Get details for an AutoML run, including settings, run widget, and all metrics.\n",
+    "4. Download a fitted pipeline for any iteration.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# List all AutoML Experiments in a Workspace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "import re\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.run import Run\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "experiment_list = Experiment.list(workspace=ws)\n",
+    "\n",
+    "summary_df = pd.DataFrame(index = ['No of Runs'])\n",
+    "pattern = re.compile('^AutoML_[^_]*$')\n",
+    "for experiment in experiment_list:\n",
+    "    all_runs = list(experiment.get_runs())\n",
+    "    automl_runs = []\n",
+    "    for run in all_runs:\n",
+    "        if(pattern.match(run.id)):\n",
+    "            automl_runs.append(run)    \n",
+    "    summary_df[experiment.name] = [len(automl_runs)]\n",
+    "    \n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "summary_df.T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# List AutoML runs for an experiment\n",
+    "Set `experiment_name` to any experiment name from the result of the Experiment.list cell to load the AutoML runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment_name = 'automl-local-classification' # Replace this with any project name from previous cell.\n",
+    "\n",
+    "proj = ws.experiments[experiment_name]\n",
+    "summary_df = pd.DataFrame(index = ['Type', 'Status', 'Primary Metric', 'Iterations', 'Compute', 'Name'])\n",
+    "pattern = re.compile('^AutoML_[^_]*$')\n",
+    "all_runs = list(proj.get_runs(properties={'azureml.runsource': 'automl'}))\n",
+    "automl_runs_project = []\n",
+    "for run in all_runs:\n",
+    "    if(pattern.match(run.id)):\n",
+    "        properties = run.get_properties()\n",
+    "        tags = run.get_tags()\n",
+    "        amlsettings = eval(properties['RawAMLSettingsString'])\n",
+    "        if 'iterations' in tags:\n",
+    "            iterations = tags['iterations']\n",
+    "        else:\n",
+    "            iterations = properties['num_iterations']\n",
+    "        summary_df[run.id] = [amlsettings['task_type'], run.get_details()['status'], properties['primary_metric'], iterations, properties['target'], amlsettings['name']]\n",
+    "        if run.get_details()['status'] == 'Completed':\n",
+    "            automl_runs_project.append(run.id)\n",
+    "    \n",
+    "from IPython.display import HTML\n",
+    "projname_html = HTML(\"<h3>{}</h3>\".format(proj.name))\n",
+    "\n",
+    "from IPython.display import display\n",
+    "display(projname_html)\n",
+    "display(summary_df.T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Get details for an AutoML run\n",
+    "\n",
+    "Copy the project name and run id from the previous cell output to find more details on a particular run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_id = automl_runs_project[0]  # Replace with your own run_id from above run ids\n",
+    "assert (run_id in summary_df.keys()), \"Run id not found! Please set run id to a value from above run ids\"\n",
+    "\n",
+    "from azureml.widgets import RunDetails\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "ml_run = AutoMLRun(experiment = experiment, run_id = run_id)\n",
+    "\n",
+    "summary_df = pd.DataFrame(index = ['Type', 'Status', 'Primary Metric', 'Iterations', 'Compute', 'Name', 'Start Time', 'End Time'])\n",
+    "properties = ml_run.get_properties()\n",
+    "tags = ml_run.get_tags()\n",
+    "status = ml_run.get_details()\n",
+    "amlsettings = eval(properties['RawAMLSettingsString'])\n",
+    "if 'iterations' in tags:\n",
+    "    iterations = tags['iterations']\n",
+    "else:\n",
+    "    iterations = properties['num_iterations']\n",
+    "start_time = None\n",
+    "if 'startTimeUtc' in status:\n",
+    "    start_time = status['startTimeUtc']\n",
+    "end_time = None\n",
+    "if 'endTimeUtc' in status:\n",
+    "    end_time = status['endTimeUtc']\n",
+    "summary_df[ml_run.id] = [amlsettings['task_type'], status['status'], properties['primary_metric'], iterations, properties['target'], amlsettings['name'], start_time, end_time]\n",
+    "display(HTML('<h3>Runtime Details</h3>'))\n",
+    "display(summary_df)\n",
+    "\n",
+    "#settings_df = pd.DataFrame(data = amlsettings, index = [''])\n",
+    "display(HTML('<h3>AutoML Settings</h3>'))\n",
+    "display(amlsettings)\n",
+    "\n",
+    "display(HTML('<h3>Iterations</h3>'))\n",
+    "RunDetails(ml_run).show() \n",
+    "\n",
+    "children = list(ml_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "display(HTML('<h3>Metrics</h3>'))\n",
+    "display(rundata)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Download fitted models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download the Best Model for Any Given Metric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metric = 'AUC_weighted' # Replace with a metric name.\n",
+    "best_run, fitted_model = ml_run.get_output(metric = metric)\n",
+    "fitted_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download the Model for Any Given Iteration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 1 # Replace with an iteration number.\n",
+    "best_run, fitted_model = ml_run.get_output(iteration = iteration)\n",
+    "fitted_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Register fitted model for deployment\n",
+    "If neither `metric` nor `iteration` are specified in the `register_model` call, the iteration with the best primary metric is registered."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "description = 'AutoML Model'\n",
+    "tags = None\n",
+    "ml_run.register_model(description = description, tags = tags)\n",
+    "ml_run.model_id # Use this id to deploy the model as a web service in Azure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Register the Best Model for Any Given Metric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metric = 'AUC_weighted' # Replace with a metric name.\n",
+    "description = 'AutoML Model'\n",
+    "tags = None\n",
+    "ml_run.register_model(description = description, tags = tags, metric = metric)\n",
+    "print(ml_run.model_id) # Use this id to deploy the model as a web service in Azure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Register the Model for Any Given Iteration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 1 # Replace with an iteration number.\n",
+    "description = 'AutoML Model'\n",
+    "tags = None\n",
+    "ml_run.register_model(description = description, tags = tags, iteration = iteration)\n",
+    "print(ml_run.model_id) # Use this id to deploy the model as a web service in Azure."
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/08.auto-ml-remote-execution-with-DataStore.ipynb

@@ -0,0 +1,588 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 08: Remote Execution with DataStore\n",
+    "\n",
+    "This sample accesses a data file on a remote DSVM through DataStore. Advantages of using data store are:\n",
+    "1. DataStore secures the access details.\n",
+    "2. DataStore supports read, write to blob and file store\n",
+    "3. AutoML natively supports copying data from DataStore to DSVM\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you would see\n",
+    "1. Storing data in DataStore.\n",
+    "2. get_data returning data from DataStore.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Experiment\n",
+    "\n",
+    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "import time\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.compute import DsvmCompute\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# choose a name for experiment\n",
+    "experiment_name = 'automl-remote-datastore-file'\n",
+    "# project folder\n",
+    "project_folder = './sample_projects/automl-remote-dsvm-file'\n",
+    "\n",
+    "experiment=Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data=output, index=['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a Remote Linux DSVM\n",
+    "Note: If creation fails with a message about Marketplace purchase eligibilty, go to portal.azure.com, start creating DSVM there, and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled it, you can exit without actually creating VM.\n",
+    "\n",
+    "**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you can switch to a different port (such as 5022), you can append the port number to the address. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "compute_target_name = 'mydsvmc'\n",
+    "\n",
+    "try:\n",
+    "    while ws.compute_targets[compute_target_name].provisioning_state == 'Creating':\n",
+    "        time.sleep(1)\n",
+    "        \n",
+    "    dsvm_compute = DsvmCompute(workspace=ws, name=compute_target_name)\n",
+    "    print('found existing:', dsvm_compute.name)\n",
+    "except:\n",
+    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size=\"Standard_D2_v2\")\n",
+    "    dsvm_compute = DsvmCompute.create(ws, name=compute_target_name, provisioning_configuration=dsvm_config)\n",
+    "    dsvm_compute.wait_for_completion(show_output=True)\n",
+    "    print(\"Waiting one minute for ssh to be accessible\")\n",
+    "    time.sleep(60) # Wait for ssh to be accessible"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Copy data file to local\n",
+    "\n",
+    "Download the data file.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mkdir data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
+    "                     delimiter=\"\\t\", quotechar='\"')\n",
+    "df.to_csv(\"data/data.tsv\", sep=\"\\t\", quotechar='\"', index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Upload data to the cloud"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now make the data accessible remotely by uploading that data from your local machine into Azure so it can be accessed for remote training. The datastore is a convenient construct associated with your workspace for you to upload/download data, and interact with it from your remote compute targets. It is backed by Azure blob storage account.\n",
+    "\n",
+    "The data.tsv files are uploaded into a directory named data at the root of the datastore."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace, Datastore\n",
+    "#blob_datastore = Datastore(ws, blob_datastore_name)\n",
+    "ds = ws.get_default_datastore()\n",
+    "print(ds.datastore_type, ds.account_name, ds.container_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ds.upload_files(\"data.tsv\")\n",
+    "ds.upload(src_dir='./data', target_path='data', overwrite=True, show_progress=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure & Run\n",
+    "\n",
+    "First let's create a DataReferenceConfigruation object to inform the system what data folder to download to the compute target.\n",
+    "The path_on_compute should be an absolute path to ensure that the data files are downloaded only once.   The get_data method should use this same path to access the data files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.runconfig import DataReferenceConfiguration\n",
+    "dr = DataReferenceConfiguration(datastore_name=ds.name, \n",
+    "                   path_on_datastore='data', \n",
+    "                   path_on_compute='/tmp/azureml_runs',\n",
+    "                   mode='download', # download files from datastore to compute target\n",
+    "                   overwrite=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.runconfig import RunConfiguration\n",
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "\n",
+    "# create a new RunConfig object\n",
+    "conda_run_config = RunConfiguration(framework=\"python\")\n",
+    "\n",
+    "# Set compute target to the Linux DSVM\n",
+    "conda_run_config.target = dsvm_compute\n",
+    "# set the data reference of the run coonfiguration\n",
+    "conda_run_config.data_references = {ds.name: dr}\n",
+    "\n",
+    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
+    "conda_run_config.environment.python.conda_dependencies = cd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Get Data File\n",
+    "For remote executions you should author a get_data.py file containing a get_data() function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
+    "\n",
+    "The *get_data()* function returns a [dictionary](README.md#getdata).\n",
+    "\n",
+    "The read_csv uses the path_on_compute value specified in the DataReferenceConfiguration call plus the path_on_datastore folder and then the actual file name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(project_folder):\n",
+    "    os.makedirs(project_folder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile $project_folder/get_data.py\n",
+    "\n",
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "import os\n",
+    "from os.path import expanduser, join, dirname\n",
+    "\n",
+    "def get_data():\n",
+    "    # Burning man 2016 data\n",
+    "    df = pd.read_csv(\"/tmp/azureml_runs/data/data.tsv\", delimiter=\"\\t\", quotechar='\"')\n",
+    "    # get integer labels\n",
+    "    le = LabelEncoder()\n",
+    "    le.fit(df[\"Label\"].values)\n",
+    "    y = le.transform(df[\"Label\"].values)\n",
+    "    X = df.drop([\"Label\"], axis=1)\n",
+    "\n",
+    "    X_train, _, y_train, _ = train_test_split(X, y, test_size=0.1, random_state=42)\n",
+    "\n",
+    "    return { \"X\" : X_train.values, \"y\" : y_train }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
+    "\n",
+    "You can specify automl_settings as **kwargs** as well. Also note that you can use the get_data() symantic for local excutions too. \n",
+    "\n",
+    "<i>Note: For Remote DSVM and Batch AI you cannot pass Numpy arrays directly to AutoMLConfig.</i>\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration|\n",
+    "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data|\n",
+    "|**n_cross_validations**|Number of cross validation splits|\n",
+    "|**max_concurrent_iterations**|Max number of iterations that would be executed in parallel.  This should be less than the number of cores on the DSVM\n",
+    "|**preprocess**| *True/False* <br>Setting this to *True* enables Auto ML to perform preprocessing <br>on the input to handle *missing data*, and perform some common *feature extraction*|\n",
+    "|**enable_cache**|Setting this to *True* enables preprocess done once and reuse the same preprocessed data for all the iterations. Default value is True.|\n",
+    "|**max_cores_per_iteration**| Indicates how many cores on the compute target would be used to train a single pipeline.<br> Default is *1*, you can set it to *-1* to use all cores|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_settings = {\n",
+    "    \"iteration_timeout_minutes\": 60,\n",
+    "    \"iterations\": 4,\n",
+    "    \"n_cross_validations\": 5,\n",
+    "    \"primary_metric\": 'AUC_weighted',\n",
+    "    \"preprocess\": True,\n",
+    "    \"max_cores_per_iteration\": 1,\n",
+    "    \"verbosity\": logging.INFO\n",
+    "}\n",
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             path=project_folder,\n",
+    "                             run_configuration=conda_run_config,\n",
+    "                             #compute_target = dsvm_compute,\n",
+    "                             data_script = project_folder + \"/get_data.py\",\n",
+    "                             **automl_settings\n",
+    "                            )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the Models <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
+    "\n",
+    "For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets/models even when the experiment is running to retreive the best model up to that point. Once you are satisfied with the model you can cancel a particular iteration or the whole run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remote_run = experiment.submit(automl_config, show_output=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
+    "#### Widget for monitoring runs\n",
+    "\n",
+    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "You can click on a pipeline to see run properties and output logs. Logs are also available on the DSVM under /tmp/azureml_run/{iterationid}/azureml-logs\n",
+    "\n",
+    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(remote_run).show() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Wait until the run finishes.\n",
+    "remote_run.wait_for_completion(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use sdk methods to fetch all the child runs and see individual metrics that we log. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(remote_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Canceling Runs\n",
+    "You can cancel ongoing remote runs using the *cancel()* and *cancel_iteration()* functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cancel the ongoing experiment and stop scheduling new iterations\n",
+    "# remote_run.cancel()\n",
+    "\n",
+    "# Cancel iteration 1 and move onto iteration 2\n",
+    "# remote_run.cancel_iteration(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pre-process cache cleanup\n",
+    "The preprocess data gets cache at user default file store. When the run is completed the cache can be cleaned by running below cell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remote_run.clean_preprocessor_cache()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The *get_output* method returns the best run and the fitted model. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = remote_run.get_output()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model based on any other metric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lookup_metric = \"accuracy\"\n",
+    "# best_run, fitted_model = remote_run.get_output(metric=lookup_metric)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a specific iteration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# iteration = 1\n",
+    "# best_run, fitted_model = remote_run.get_output(iteration=iteration)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing the Best Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sklearn\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from pandas_ml import ConfusionMatrix\n",
+    "\n",
+    "df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
+    "                     delimiter=\"\\t\", quotechar='\"')\n",
+    "\n",
+    "# get integer labels\n",
+    "le = LabelEncoder()\n",
+    "le.fit(df[\"Label\"].values)\n",
+    "y = le.transform(df[\"Label\"].values)\n",
+    "X = df.drop([\"Label\"], axis=1)\n",
+    "\n",
+    "_, X_test, _, y_test = train_test_split(X, y, test_size=0.1, random_state=42)\n",
+    "\n",
+    "ypred = fitted_model.predict(X_test.values)\n",
+    "\n",
+    "ypred_strings = le.inverse_transform(ypred)\n",
+    "ytest_strings = le.inverse_transform(y_test)\n",
+    "\n",
+    "cm = ConfusionMatrix(ytest_strings, ypred_strings)\n",
+    "\n",
+    "print(cm)\n",
+    "\n",
+    "cm.plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/08b.auto-ml-remote-execution-with-dataprep.ipynb

@@ -0,0 +1,568 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 08b: Remote Execution with DataPrep\n",
+    "\n",
+    "This sample accesses a data file on a remote DSVM through Datastore using DataPrep. Advantages of using DataPrep are:\n",
+    "1. DataPrep supports reading from and writing to datastores.\n",
+    "2. DataPrep supports automatic file type and column type detection.\n",
+    "3. DataPrep makes passing data into AutoML really simple.\n",
+    "\n",
+    "More DataPrep documentation and examples can be found [here](https://github.com/Microsoft/AMLDataPrepDocs).\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you would see\n",
+    "1. Storing data in DataStore.\n",
+    "2. Doing some basic data preparation using DataPrep and passing the prepared data (DataFlow) to AutoML for training (classficiation).\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Experiment\n",
+    "\n",
+    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "import time\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.compute import DsvmCompute\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# choose a name for experiment\n",
+    "experiment_name = 'automl-remote-datastore-file'\n",
+    "# project folder\n",
+    "project_folder = './sample_projects/automl-remote-dsvm-file'\n",
+    "\n",
+    "experiment=Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data=output, index=['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a Remote Linux DSVM\n",
+    "Note: If creation fails with a message about Marketplace purchase eligibilty, go to portal.azure.com, start creating DSVM there, and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled it, you can exit without actually creating VM.\n",
+    "\n",
+    "**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you can switch to a different port (such as 5022), you can append the port number to the address. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "compute_target_name = 'automl-dataprep'\n",
+    "\n",
+    "try:\n",
+    "    while ws.compute_targets[compute_target_name].provisioning_state == 'Creating':\n",
+    "        time.sleep(1)\n",
+    "        \n",
+    "    dsvm_compute = DsvmCompute(workspace=ws, name=compute_target_name)\n",
+    "    print('found existing:', dsvm_compute.name)\n",
+    "except:\n",
+    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size=\"Standard_D2_v2\")\n",
+    "    dsvm_compute = DsvmCompute.create(ws, name=compute_target_name, provisioning_configuration=dsvm_config)\n",
+    "    dsvm_compute.wait_for_completion(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Copy data file to local\n",
+    "\n",
+    "We will download a 1MB simple random sample of the Chicago Crime data into a local temporary directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tempfile\n",
+    "import requests\n",
+    "\n",
+    "temp_folder = tempfile.mkdtemp()\n",
+    "temp_tsv = os.path.join(temp_folder, 'crime0.csv')\n",
+    "\n",
+    "request = requests.get('https://dprepdata.blob.core.windows.net/demo/crime0-random.csv')\n",
+    "with open(temp_tsv, 'w', encoding='utf-8') as f:\n",
+    "    f.write(request.text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Upload data to the cloud"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's make the data available in your datastore. Datastore is a convenient construct associated with your workspace for you to  reference different types of cloud storage locations (e.g. Azure Blob Containers, Azure File Shares, Azure Data Lake Stores, etc.). The benefit Datastore brings is you only need to register datastores once and you will be able to access them by name and will not need to expose secrets in your code. When you first create a workspace, a default datastore is registered for you which references the Azure Blob Container that was provisioned with the workspace. Let's upload the data we just got from the public location to the default datastore.\n",
+    "\n",
+    "The `csv` file is uploaded into a directory named `datasets` at the root of the datastore."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace, Datastore\n",
+    "\n",
+    "ds = ws.get_default_datastore()\n",
+    "print(ds.datastore_type, ds.account_name, ds.container_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.upload(src_dir=temp_folder, target_path='datasets', overwrite=True, show_progress=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Dataflow using DataPrep\n",
+    "Let's use DataPrep to read the `csv` file from the datastore we just uploaded to and get the data profile to make sure our data looks good. We will predict the type of the offense (`Primary Type`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import azureml.dataprep as dprep\n",
+    "\n",
+    "dflow = dprep.read_csv(path=ds.path('datasets/crime0.csv'))\n",
+    "dflow.get_profile()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's also take a look at the first 5 rows of the data to give ourselves an idea of what the data looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dflow.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the first 5 rows, we see that there are some rows that have no value in the label column (`Primary Type`). Let's remove those rows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dflow = dflow.drop_nulls('Primary Type')\n",
+    "dflow.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we've removed those rows, let's split the dataflow into a features dataflow and a label dataflow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = dflow.drop_columns(columns=['Primary Type', 'FBI Code'])\n",
+    "y = dflow.keep_columns(columns=['Primary Type'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
+    "\n",
+    "You can specify automl_settings as **kwargs** as well. Also note that you can use the get_data() symantic for local excutions too. \n",
+    "\n",
+    "<i>Note: For Remote DSVM and Batch AI you cannot pass Numpy arrays directly to AutoMLConfig.</i>\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration|\n",
+    "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data|\n",
+    "|**n_cross_validations**|Number of cross validation splits|\n",
+    "|**max_concurrent_iterations**|Max number of iterations that would be executed in parallel.  This should be less than the number of cores on the DSVM\n",
+    "|**preprocess**| *True/False* <br>Setting this to *True* enables Auto ML to perform preprocessing <br>on the input to handle *missing data*, and perform some common *feature extraction*|\n",
+    "|**enable_cache**|Setting this to *True* enables preprocess done once and reuse the same preprocessed data for all the iterations. Default value is True.|\n",
+    "|**max_cores_per_iteration**| Indicates how many cores on the compute target would be used to train a single pipeline.<br> Default is *1*, you can set it to *-1* to use all cores|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.runconfig import RunConfiguration\n",
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "\n",
+    "conda_run_config = RunConfiguration(framework=\"python\")\n",
+    "\n",
+    "conda_run_config.target = dsvm_compute\n",
+    "\n",
+    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]==0.1.0.1918169'], conda_packages=['numpy'], pin_sdk_version=False, pip_indexurl='https://azuremlsdktestpypi.azureedge.net/sdk-release/master/588E708E0DF342C4A80BD954289657CF')\n",
+    "conda_run_config.environment.python.conda_dependencies = cd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_settings = {\n",
+    "    \"iteration_timeout_minutes\": 60,\n",
+    "    \"iterations\": 4,\n",
+    "    \"n_cross_validations\": 5,\n",
+    "    \"primary_metric\": 'accuracy',\n",
+    "    \"preprocess\": True,\n",
+    "    \"max_cores_per_iteration\": 1,\n",
+    "    \"verbosity\": logging.INFO\n",
+    "}\n",
+    "automl_config = AutoMLConfig(task='classification',\n",
+    "                             debug_log='automl_errors.log',\n",
+    "                             path=project_folder,\n",
+    "                             run_configuration=conda_run_config,\n",
+    "                             X=X,\n",
+    "                             y=y,\n",
+    "                             **automl_settings)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the Models <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
+    "\n",
+    "For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets/models even when the experiment is running to retreive the best model up to that point. Once you are satisfied with the model you can cancel a particular iteration or the whole run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remote_run = experiment.submit(automl_config, show_output=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
+    "#### Widget for monitoring runs\n",
+    "\n",
+    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "You can click on a pipeline to see run properties and output logs. Logs are also available on the DSVM under /tmp/azureml_run/{iterationid}/azureml-logs\n",
+    "\n",
+    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(remote_run).show() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Wait until the run finishes.\n",
+    "remote_run.wait_for_completion(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remote_run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use sdk methods to fetch all the child runs and see individual metrics that we log. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(remote_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Canceling Runs\n",
+    "You can cancel ongoing remote runs using the *cancel()* and *cancel_iteration()* functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cancel the ongoing experiment and stop scheduling new iterations\n",
+    "# remote_run.cancel()\n",
+    "\n",
+    "# Cancel iteration 1 and move onto iteration 2\n",
+    "# remote_run.cancel_iteration(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pre-process cache cleanup\n",
+    "The preprocess data gets cache at user default file store. When the run is completed the cache can be cleaned by running below cell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remote_run.clean_preprocessor_cache()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The *get_output* method returns the best run and the fitted model. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = remote_run.get_output()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model based on any other metric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lookup_metric = \"accuracy\"\n",
+    "# best_run, fitted_model = remote_run.get_output(metric=lookup_metric)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a specific iteration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# iteration = 1\n",
+    "# best_run, fitted_model = remote_run.get_output(iteration=iteration)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing the Best Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dflow = dprep.read_csv(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n",
+    "dflow.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas_ml import ConfusionMatrix\n",
+    "\n",
+    "y_test = dflow.keep_columns(columns=['Primary Type']).to_pandas_dataframe()\n",
+    "X_test = dflow.drop_columns(columns=['Primary Type', 'FBI Code']).to_pandas_dataframe()\n",
+    "\n",
+    "ypred = fitted_model.predict(X_test.values)\n",
+    "\n",
+    "cm = ConfusionMatrix(y_test['Primary Type'], ypred)\n",
+    "\n",
+    "print(cm)\n",
+    "\n",
+    "cm.plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python [conda env:cli_dev]",
+   "language": "python",
+   "name": "conda-env-cli_dev-py"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/09.auto-ml-classification-with-deployment.ipynb

@@ -0,0 +1,501 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 09: Classification with Deployment\n",
+    "\n",
+    "In this example we use the scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) to showcase how you can use AutoML for a simple classification problem and deploy it to an Azure Container Instance (ACI).\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you will learn how to:\n",
+    "1. Create an experiment using an existing workspace.\n",
+    "2. Configure AutoML using `AutoMLConfig`.\n",
+    "3. Train the model using local compute.\n",
+    "4. Explore the results.\n",
+    "5. Register the model.\n",
+    "6. Create a container image.\n",
+    "7. Create an Azure Container Instance (ACI) service.\n",
+    "8. Test the ACI service.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# choose a name for experiment\n",
+    "experiment_name = 'automl-local-classification'\n",
+    "# project folder\n",
+    "project_folder = './sample_projects/automl-local-classification'\n",
+    "\n",
+    "experiment=Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data=output, index=['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|classification or regression|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**n_cross_validations**|Number of cross validation splits.|\n",
+    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
+    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_train = digits.data[10:,:]\n",
+    "y_train = digits.target[10:]\n",
+    "\n",
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             name = experiment_name,\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             primary_metric = 'AUC_weighted',\n",
+    "                             iteration_timeout_minutes = 20,\n",
+    "                             iterations = 10,\n",
+    "                             n_cross_validations = 2,\n",
+    "                             verbosity = logging.INFO,\n",
+    "                             X = X_train, \n",
+    "                             y = y_train,\n",
+    "                             path = project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
+    "In this example, we specify `show_output = True` to print currently running iterations to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Register the Fitted Model for Deployment\n",
+    "If neither `metric` nor `iteration` are specified in the `register_model` call, the iteration with the best primary metric is registered."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "description = 'AutoML Model'\n",
+    "tags = None\n",
+    "model = local_run.register_model(description = description, tags = tags)\n",
+    "local_run.model_id # This will be written to the script file later in the notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create Scoring Script"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile score.py\n",
+    "import pickle\n",
+    "import json\n",
+    "import numpy\n",
+    "import azureml.train.automl\n",
+    "from sklearn.externals import joblib\n",
+    "from azureml.core.model import Model\n",
+    "\n",
+    "\n",
+    "def init():\n",
+    "    global model\n",
+    "    model_path = Model.get_model_path(model_name = '<<modelid>>') # this name is model.id of model that we want to deploy\n",
+    "    # deserialize the model file back into a sklearn model\n",
+    "    model = joblib.load(model_path)\n",
+    "\n",
+    "def run(rawdata):\n",
+    "    try:\n",
+    "        data = json.loads(rawdata)['data']\n",
+    "        data = numpy.array(data)\n",
+    "        result = model.predict(data)\n",
+    "    except Exception as e:\n",
+    "        result = str(e)\n",
+    "        return json.dumps({\"error\": result})\n",
+    "    return json.dumps({\"result\":result.tolist()})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a YAML File for the Environment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To ensure the fit results are consistent with the training results, the SDK dependency versions need to be the same as the environment that trains the model. Details about retrieving the versions can be found in notebook [12.auto-ml-retrieve-the-training-sdk-versions](12.auto-ml-retrieve-the-training-sdk-versions.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment_name = 'automl-local-classification'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "ml_run = AutoMLRun(experiment = experiment, run_id = local_run.id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dependencies = ml_run.get_run_sdk_dependencies(iteration = 7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
+    "    print('{}\\t{}'.format(p, dependencies[p]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "\n",
+    "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-sdk[automl]'])\n",
+    "\n",
+    "conda_env_file_name = 'myenv.yml'\n",
+    "myenv.save_to_file('.', conda_env_file_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Substitute the actual version number in the environment file.\n",
+    "# This is not strictly needed in this notebook because the model should have been generated using the current SDK version.\n",
+    "# However, we include this in case this code is used on an experiment from a previous SDK version.\n",
+    "\n",
+    "with open(conda_env_file_name, 'r') as cefr:\n",
+    "    content = cefr.read()\n",
+    "\n",
+    "with open(conda_env_file_name, 'w') as cefw:\n",
+    "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
+    "\n",
+    "# Substitute the actual model id in the script file.\n",
+    "\n",
+    "script_file_name = 'score.py'\n",
+    "\n",
+    "with open(script_file_name, 'r') as cefr:\n",
+    "    content = cefr.read()\n",
+    "\n",
+    "with open(script_file_name, 'w') as cefw:\n",
+    "    cefw.write(content.replace('<<modelid>>', local_run.model_id))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a Container Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.image import Image, ContainerImage\n",
+    "\n",
+    "image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
+    "                                 execution_script = script_file_name,\n",
+    "                                 conda_file = conda_env_file_name,\n",
+    "                                 tags = {'area': \"digits\", 'type': \"automl_classification\"},\n",
+    "                                 description = \"Image for automl classification sample\")\n",
+    "\n",
+    "image = Image.create(name = \"automlsampleimage\",\n",
+    "                     # this is the model object \n",
+    "                     models = [model],\n",
+    "                     image_config = image_config, \n",
+    "                     workspace = ws)\n",
+    "\n",
+    "image.wait_for_creation(show_output = True)\n",
+    "\n",
+    "if image.creation_state == 'Failed':\n",
+    "    print(\"Image build log at: \" + image.image_build_log_uri)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deploy the Image as a Web Service on Azure Container Instance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.webservice import AciWebservice\n",
+    "\n",
+    "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
+    "                                               memory_gb = 1, \n",
+    "                                               tags = {'area': \"digits\", 'type': \"automl_classification\"}, \n",
+    "                                               description = 'sample service for Automl Classification')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.webservice import Webservice\n",
+    "\n",
+    "aci_service_name = 'automl-sample-01'\n",
+    "print(aci_service_name)\n",
+    "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
+    "                                           image = image,\n",
+    "                                           name = aci_service_name,\n",
+    "                                           workspace = ws)\n",
+    "aci_service.wait_for_deployment(True)\n",
+    "print(aci_service.state)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Delete a Web Service"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#aci_service.delete()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get Logs from a Deployed Web Service"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#aci_service.get_logs()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test a Web Service"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Randomly select digits and test\n",
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:10, :]\n",
+    "y_test = digits.target[:10]\n",
+    "images = digits.images[:10]\n",
+    "\n",
+    "for index in np.random.choice(len(y_test), 3, replace = False):\n",
+    "    print(index)\n",
+    "    test_sample = json.dumps({'data':X_test[index:index + 1].tolist()})\n",
+    "    predicted = aci_service.run(input_data = test_sample)\n",
+    "    label = y_test[index]\n",
+    "    predictedDict = json.loads(predicted)\n",
+    "    title = \"Label value = %d  Predicted value = %s \" % ( label,predictedDict['result'][0])\n",
+    "    fig = plt.figure(1, figsize = (3,3))\n",
+    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "    ax1.set_title(title)\n",
+    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "    plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/10.auto-ml-multi-output-example.ipynb

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

how-to-use-azureml/automated-machine-learning/11.auto-ml-sample-weight.ipynb

@@ -0,0 +1,251 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 11: Sample Weight\n",
+    "\n",
+    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use sample weight with AutoML. Sample weight is used where some sample values are more important than others.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you will learn how to configure AutoML to use `sample_weight` and you will see the difference sample weight makes to the test results.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose names for the regular and the sample weight experiments.\n",
+    "experiment_name = 'non_sample_weight_experiment'\n",
+    "sample_weight_experiment_name = 'sample_weight_experiment'\n",
+    "\n",
+    "project_folder = './sample_projects/automl-local-classification'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "sample_weight_experiment=Experiment(ws, sample_weight_experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "Instantiate two `AutoMLConfig` objects. One will be used with `sample_weight` and one without."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_train = digits.data[100:,:]\n",
+    "y_train = digits.target[100:]\n",
+    "\n",
+    "# The example makes the sample weight 0.9 for the digit 4 and 0.1 for all other digits.\n",
+    "# This makes the model more likely to classify as 4 if the image it not clear.\n",
+    "sample_weight = np.array([(0.9 if x == 4 else 0.01) for x in y_train])\n",
+    "\n",
+    "automl_classifier = AutoMLConfig(task = 'classification',\n",
+    "                                 debug_log = 'automl_errors.log',\n",
+    "                                 primary_metric = 'AUC_weighted',\n",
+    "                                 iteration_timeout_minutes = 60,\n",
+    "                                 iterations = 10,\n",
+    "                                 n_cross_validations = 2,\n",
+    "                                 verbosity = logging.INFO,\n",
+    "                                 X = X_train, \n",
+    "                                 y = y_train,\n",
+    "                                 path = project_folder)\n",
+    "\n",
+    "automl_sample_weight = AutoMLConfig(task = 'classification',\n",
+    "                                    debug_log = 'automl_errors.log',\n",
+    "                                    primary_metric = 'AUC_weighted',\n",
+    "                                    iteration_timeout_minutes = 60,\n",
+    "                                    iterations = 10,\n",
+    "                                    n_cross_validations = 2,\n",
+    "                                    verbosity = logging.INFO,\n",
+    "                                    X = X_train, \n",
+    "                                    y = y_train,\n",
+    "                                    sample_weight = sample_weight,\n",
+    "                                    path = project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models\n",
+    "\n",
+    "Call the `submit` method on the experiment objects and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
+    "In this example, we specify `show_output = True` to print currently running iterations to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_classifier, show_output = True)\n",
+    "sample_weight_run = sample_weight_experiment.submit(automl_sample_weight, show_output = True)\n",
+    "\n",
+    "best_run, fitted_model = local_run.get_output()\n",
+    "best_run_sample_weight, fitted_model_sample_weight = sample_weight_run.get_output()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test the Best Fitted Model\n",
+    "\n",
+    "#### Load Test Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:100, :]\n",
+    "y_test = digits.target[:100]\n",
+    "images = digits.images[:100]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Compare the Models\n",
+    "The prediction from the sample weight model is more likely to correctly predict 4's.  However, it is also more likely to predict 4 for some images that are not labelled as 4."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Randomly select digits and test.\n",
+    "for index in range(0,len(y_test)):\n",
+    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+    "    predicted_sample_weight = fitted_model_sample_weight.predict(X_test[index:index + 1])[0]\n",
+    "    label = y_test[index]\n",
+    "    if predicted == 4 or predicted_sample_weight == 4 or label == 4:\n",
+    "        title = \"Label value = %d  Predicted value = %d Prediced with sample weight = %d\" % (label, predicted, predicted_sample_weight)\n",
+    "        fig = plt.figure(1, figsize=(3,3))\n",
+    "        ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "        ax1.set_title(title)\n",
+    "        plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "        plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/12.auto-ml-retrieve-the-training-sdk-versions.ipynb

@@ -0,0 +1,227 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 12: Retrieving Training SDK Versions\n",
+    "\n",
+    "This example shows how to find the SDK versions used for an experiment.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train models using AutoML"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose a name for the experiment and specify the project folder.\n",
+    "experiment_name = 'automl-local-classification'\n",
+    "project_folder = './sample_projects/automl-local-classification'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data=output, index=['']).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_train = digits.data[10:,:]\n",
+    "y_train = digits.target[10:]\n",
+    "\n",
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             primary_metric = 'AUC_weighted',\n",
+    "                             iterations = 3,\n",
+    "                             n_cross_validations = 2,\n",
+    "                             verbosity = logging.INFO,\n",
+    "                             X = X_train, \n",
+    "                             y = y_train,\n",
+    "                             path = project_folder)\n",
+    "\n",
+    "local_run = experiment.submit(automl_config, show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Retrieve the SDK versions from RunHistory"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get the SDK versions from RunHistory, first the run id needs to be recorded. This can either be done by copying it from the output message or by retrieving it after each run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use a run id copied from an output message.\n",
+    "#run_id = 'AutoML_c0585b1f-a0e6-490b-84c7-3a099468b28e'\n",
+    "\n",
+    "# Retrieve the run id from a run.\n",
+    "run_id = local_run.id\n",
+    "print(run_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initialize a new `AutoMLRun` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment_name = 'automl-local-classification'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "ml_run = AutoMLRun(experiment = experiment, run_id = run_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get parent training SDK versions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ml_run.get_run_sdk_dependencies()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the traning SDK versions of a specific run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ml_run.get_run_sdk_dependencies(iteration = 2)"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/13a.auto-ml-dataprep.ipynb

@@ -0,0 +1,446 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 13: Prepare Data using `azureml.dataprep` for Local Execution\n",
+    "In this example we showcase how you can use the `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone; full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
+    "\n",
+    "Make sure you have executed the [setup](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you will learn how to:\n",
+    "1. Define data loading and preparation steps in a `Dataflow` using `azureml.dataprep`.\n",
+    "2. Pass the `Dataflow` to AutoML for a local run.\n",
+    "3. Pass the `Dataflow` to AutoML for a remote run."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "import azureml.dataprep as dprep\n",
+    "from azureml.train.automl import AutoMLConfig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    " \n",
+    "# choose a name for experiment\n",
+    "experiment_name = 'automl-dataprep-local'\n",
+    "# project folder\n",
+    "project_folder = './sample_projects/automl-dataprep-local'\n",
+    " \n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    " \n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading Data using DataPrep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
+    "# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
+    "simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
+    "X = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1)  # Remove the header row.\n",
+    "\n",
+    "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
+    "# and convert column types manually.\n",
+    "# Here we read a comma delimited file and convert all columns to integers.\n",
+    "y = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Review the Data Preparation Result\n",
+    "\n",
+    "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X.skip(1).head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "This creates a general AutoML settings object applicable for both local and remote runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_settings = {\n",
+    "    \"iteration_timeout_minutes\" : 10,\n",
+    "    \"iterations\" : 2,\n",
+    "    \"primary_metric\" : 'AUC_weighted',\n",
+    "    \"preprocess\" : False,\n",
+    "    \"verbosity\" : logging.INFO,\n",
+    "    \"n_cross_validations\": 3\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Local Run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pass Data with `Dataflow` Objects\n",
+    "\n",
+    "The `Dataflow` objects captured above can be passed to the `submit` method for a local run. AutoML will retrieve the results from the `Dataflow` for model training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             X = X,\n",
+    "                             y = y,\n",
+    "                             **automl_settings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(local_run).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(local_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "    \n",
+    "import pandas as pd\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model that has the smallest `log_loss` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lookup_metric = \"log_loss\"\n",
+    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration\n",
+    "Show the run and the model from the first iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 0\n",
+    "best_run, fitted_model = local_run.get_output(iteration = iteration)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test the Best Fitted Model\n",
+    "\n",
+    "#### Load Test Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "\n",
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:10, :]\n",
+    "y_test = digits.target[:10]\n",
+    "images = digits.images[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Testing Our Best Fitted Model\n",
+    "We will try to predict 2 digits and see how our model works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Randomly select digits and test\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import random\n",
+    "import numpy as np\n",
+    "\n",
+    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+    "    print(index)\n",
+    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+    "    label = y_test[index]\n",
+    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
+    "    fig = plt.figure(1, figsize=(3,3))\n",
+    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "    ax1.set_title(title)\n",
+    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Appendix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Capture the `Dataflow` Objects for Later Use in AutoML\n",
+    "\n",
+    "`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object can be branched at any point for further usage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sklearn.digits.data + target\n",
+    "digits_complete = dprep.auto_read_file('https://dprepdata.blob.core.windows.net/automl-notebook-data/digits-complete.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`digits_complete` (sourced from `sklearn.datasets.load_digits()`) is forked into `dflow_X` to capture all the feature columns and `dflow_y` to capture the label column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits_complete.to_pandas_dataframe().shape\n",
+    "labels_column = 'Column64'\n",
+    "dflow_X = digits_complete.drop_columns(columns = [labels_column])\n",
+    "dflow_y = digits_complete.keep_columns(columns = [labels_column])"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/13b.auto-ml-dataprep-remote-execution.ipynb

@@ -0,0 +1,497 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 13: Prepare Data using `azureml.dataprep` for Remote Execution (DSVM)\n",
+    "In this example we showcase how you can use the `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone; full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
+    "\n",
+    "Make sure you have executed the [setup](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you will learn how to:\n",
+    "1. Define data loading and preparation steps in a `Dataflow` using `azureml.dataprep`.\n",
+    "2. Pass the `Dataflow` to AutoML for a local run.\n",
+    "3. Pass the `Dataflow` to AutoML for a remote run."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import time\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.compute import DsvmCompute\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "import azureml.dataprep as dprep\n",
+    "from azureml.train.automl import AutoMLConfig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    " \n",
+    "# choose a name for experiment\n",
+    "experiment_name = 'automl-dataprep-remote-dsvm'\n",
+    "# project folder\n",
+    "project_folder = './sample_projects/automl-dataprep-remote-dsvm'\n",
+    " \n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    " \n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading Data using DataPrep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
+    "# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
+    "simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
+    "X = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1)  # Remove the header row.\n",
+    "\n",
+    "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
+    "# and convert column types manually.\n",
+    "# Here we read a comma delimited file and convert all columns to integers.\n",
+    "y = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Review the Data Preparation Result\n",
+    "\n",
+    "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X.skip(1).head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "This creates a general AutoML settings object applicable for both local and remote runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_settings = {\n",
+    "    \"iteration_timeout_minutes\" : 10,\n",
+    "    \"iterations\" : 2,\n",
+    "    \"primary_metric\" : 'AUC_weighted',\n",
+    "    \"preprocess\" : False,\n",
+    "    \"verbosity\" : logging.INFO,\n",
+    "    \"n_cross_validations\": 3\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Remote Run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create or Attach a Remote Linux DSVM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dsvm_name = 'mydsvmc'\n",
+    "\n",
+    "try:\n",
+    "    while ws.compute_targets[dsvm_name].provisioning_state == 'Creating':\n",
+    "        time.sleep(1)\n",
+    "        \n",
+    "    dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
+    "    print('Found existing DVSM.')\n",
+    "except:\n",
+    "    print('Creating a new DSVM.')\n",
+    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
+    "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
+    "    dsvm_compute.wait_for_completion(show_output = True)\n",
+    "    print(\"Waiting one minute for ssh to be accessible\")\n",
+    "    time.sleep(60) # Wait for ssh to be accessible"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.runconfig import RunConfiguration\n",
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "\n",
+    "conda_run_config = RunConfiguration(framework=\"python\")\n",
+    "\n",
+    "conda_run_config.target = dsvm_compute\n",
+    "\n",
+    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
+    "conda_run_config.environment.python.conda_dependencies = cd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pass Data with `Dataflow` Objects\n",
+    "\n",
+    "The `Dataflow` objects captured above can also be passed to the `submit` method for a remote run. AutoML will serialize the `Dataflow` object and send it to the remote compute target. The `Dataflow` will not be evaluated locally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             path = project_folder,\n",
+    "                             run_configuration=conda_run_config,\n",
+    "                             X = X,\n",
+    "                             y = y,\n",
+    "                             **automl_settings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remote_run = experiment.submit(automl_config, show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(remote_run).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(remote_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "    \n",
+    "import pandas as pd\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = remote_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model that has the smallest `log_loss` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lookup_metric = \"log_loss\"\n",
+    "best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration\n",
+    "Show the run and the model from the first iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 0\n",
+    "best_run, fitted_model = remote_run.get_output(iteration = iteration)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test the Best Fitted Model\n",
+    "\n",
+    "#### Load Test Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "\n",
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:10, :]\n",
+    "y_test = digits.target[:10]\n",
+    "images = digits.images[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Testing Our Best Fitted Model\n",
+    "We will try to predict 2 digits and see how our model works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Randomly select digits and test\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import random\n",
+    "import numpy as np\n",
+    "\n",
+    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+    "    print(index)\n",
+    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+    "    label = y_test[index]\n",
+    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
+    "    fig = plt.figure(1, figsize=(3,3))\n",
+    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "    ax1.set_title(title)\n",
+    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Appendix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Capture the `Dataflow` Objects for Later Use in AutoML\n",
+    "\n",
+    "`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object can be branched at any point for further usage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sklearn.digits.data + target\n",
+    "digits_complete = dprep.auto_read_file('https://dprepdata.blob.core.windows.net/automl-notebook-data/digits-complete.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`digits_complete` (sourced from `sklearn.datasets.load_digits()`) is forked into `dflow_X` to capture all the feature columns and `dflow_y` to capture the label column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits_complete.to_pandas_dataframe().shape\n",
+    "labels_column = 'Column64'\n",
+    "dflow_X = digits_complete.drop_columns(columns = [labels_column])\n",
+    "dflow_y = digits_complete.keep_columns(columns = [labels_column])"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/14.auto-ml-model-explanation.ipynb

@@ -0,0 +1,348 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 14: Explain classification model and visualize the explanation\n",
+    "\n",
+    "In this example we use the sklearn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) to showcase how you can use the AutoML Classifier for a simple classification problem.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you would see\n",
+    "1. Creating an Experiment in an existing Workspace\n",
+    "2. Instantiating AutoMLConfig\n",
+    "3. Training the Model using local compute and explain the model\n",
+    "4. Visualization model's feature importance in widget\n",
+    "5. Explore best model's explanation\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Experiment\n",
+    "\n",
+    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "import pandas as pd\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# choose a name for experiment\n",
+    "experiment_name = 'automl-local-classification'\n",
+    "# project folder\n",
+    "project_folder = './sample_projects/automl-local-classification-model-explanation'\n",
+    "\n",
+    "experiment=Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Iris Data Set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "\n",
+    "iris = datasets.load_iris()\n",
+    "y = iris.target\n",
+    "X = iris.data\n",
+    "\n",
+    "features = iris.feature_names\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
+    "                                                    y,\n",
+    "                                                    test_size=0.1,\n",
+    "                                                    random_state=100,\n",
+    "                                                    stratify=y)\n",
+    "\n",
+    "X_train = pd.DataFrame(X_train, columns=features)\n",
+    "X_test = pd.DataFrame(X_test, columns=features)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiate Auto ML Config\n",
+    "\n",
+    "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|classification or regression|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
+    "|**max_time_sec**|Time limit in minutes for each iterations|\n",
+    "|**iterations**|Number of iterations. In each iteration Auto ML trains the data with a specific pipeline|\n",
+    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
+    "|**X_valid**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y_valid**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]|\n",
+    "|**model_explainability**|Indicate to explain each trained pipeline or not |\n",
+    "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             primary_metric = 'AUC_weighted',\n",
+    "                             iteration_timeout_minutes = 200,\n",
+    "                             iterations = 10,\n",
+    "                             verbosity = logging.INFO,\n",
+    "                             X = X_train, \n",
+    "                             y = y_train,\n",
+    "                             X_valid = X_test,\n",
+    "                             y_valid = y_test,\n",
+    "                             model_explainability=True,\n",
+    "                             path=project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the Model\n",
+    "\n",
+    "You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
+    "You will see the currently running iterations printing to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exploring the results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Widget for monitoring runs\n",
+    "\n",
+    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(local_run).show() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The *get_output* method on automl_classifier returns the best run and the fitted model for the last *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Best Model 's explanation\n",
+    "\n",
+    "Retrieve the explanation from the best_run. And explanation information includes:\n",
+    "\n",
+    "1.\tshap_values: The explanation information generated by shap lib\n",
+    "2.\texpected_values: The expected value of the model applied to set of X_train data.\n",
+    "3.\toverall_summary: The model level feature importance values sorted in descending order\n",
+    "4.\toverall_imp: The feature names sorted in the same order as in overall_summary\n",
+    "5.\tper_class_summary: The class level feature importance values sorted in descending order. Only available for the classification case\n",
+    "6.\tper_class_imp: The feature names sorted in the same order as in per_class_summary. Only available for the classification case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.train.automl.automlexplainer import retrieve_model_explanation\n",
+    "\n",
+    "shap_values, expected_values, overall_summary, overall_imp, per_class_summary, per_class_imp = \\\n",
+    "    retrieve_model_explanation(best_run)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(overall_summary)\n",
+    "print(overall_imp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(per_class_summary)\n",
+    "print(per_class_imp)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Beside retrieve the existed model explanation information, explain the model with different train/test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.train.automl.automlexplainer import explain_model\n",
+    "\n",
+    "shap_values, expected_values, overall_summary, overall_imp, per_class_summary, per_class_imp = \\\n",
+    "    explain_model(fitted_model, X_train, X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(overall_summary)\n",
+    "print(overall_imp)"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "xif"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/17.auto-ml-classification_with_tensorflow.ipynb

@@ -0,0 +1,390 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 17: Classification with Local Compute with Tesnorflow DNNClassifier and LinearClassifier using whitelist models feature.\n",
+    "\n",
+    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "This notebooks shows how can automl can be trained on a a selected list of models,see the readme.md for the models.\n",
+    "This trains the model exclusively on tensorflow based models.\n",
+    "\n",
+    "In this notebook you will learn how to:\n",
+    "1. Create an `Experiment` in an existing `Workspace`.\n",
+    "2. Configure AutoML using `AutoMLConfig`.\n",
+    "3. Train the model on a whilelisted models using local compute. \n",
+    "4. Explore the results.\n",
+    "5. Test the best fitted model.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose a name for the experiment and specify the project folder.\n",
+    "experiment_name = 'automl-local-classification'\n",
+    "project_folder = './sample_projects/automl-local-classification'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Training Data\n",
+    "\n",
+    "This uses scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "\n",
+    "digits = datasets.load_digits()\n",
+    "\n",
+    "# Exclude the first 100 rows from training so that they can be used for test.\n",
+    "X_train = digits.data[100:,:]\n",
+    "y_train = digits.target[100:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|classification or regression|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**n_cross_validations**|Number of cross validation splits.|\n",
+    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
+    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             primary_metric = 'AUC_weighted',\n",
+    "                             iteration_timeout_minutes = 60,\n",
+    "                             iterations = 10,\n",
+    "                             n_cross_validations = 3,\n",
+    "                             verbosity = logging.INFO,\n",
+    "                             X = X_train, \n",
+    "                             y = y_train,\n",
+    "                             enable_tf=True,\n",
+    "                             whitelist_models=[\"TensorFlowLinearClassifier\", \"TensorFlowDNN\"],\n",
+    "                             path = project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
+    "In this example, we specify `show_output = True` to print currently running iterations to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(local_run).show() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(local_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model that has the smallest `log_loss` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lookup_metric = \"log_loss\"\n",
+    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration\n",
+    "Show the run and the model from the third iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 3\n",
+    "third_run, third_model = local_run.get_output(iteration = iteration)\n",
+    "print(third_run)\n",
+    "print(third_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test the Best Fitted Model\n",
+    "\n",
+    "#### Load Test Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:10, :]\n",
+    "y_test = digits.target[:10]\n",
+    "images = digits.images[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Testing Our Best Fitted Model\n",
+    "We will try to predict 2 digits and see how our model works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Randomly select digits and test.\n",
+    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+    "    print(index)\n",
+    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+    "    label = y_test[index]\n",
+    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
+    "    fig = plt.figure(1, figsize = (3,3))\n",
+    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "    ax1.set_title(title)\n",
+    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "    plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/18a.auto-ml-forecasting.ipynb

@@ -0,0 +1,397 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 18: Energy Demand Forecasting\n",
+    "\n",
+    "In this example, we show how AutoML can be used for energy demand forecasting.\n",
+    "\n",
+    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you would see\n",
+    "1. Creating an Experiment in an existing Workspace\n",
+    "2. Instantiating AutoMLConfig with new task type \"forecasting\" for timeseries data training, and other timeseries related settings: for this dataset we use the basic one: \"time_column_name\" \n",
+    "3. Training the Model using local compute\n",
+    "4. Exploring the results\n",
+    "5. Testing the fitted model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Experiment\n",
+    "\n",
+    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import azureml.core\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import logging\n",
+    "import warnings\n",
+    "warnings.showwarning = lambda *args, **kwargs: None\n",
+    "\n",
+    "\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# choose a name for the run history container in the workspace\n",
+    "experiment_name = 'automl-energydemandforecasting'\n",
+    "# project folder\n",
+    "project_folder = './sample_projects/automl-local-energydemandforecasting'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Run History Name'] = experiment_name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data=output, index=['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read Data\n",
+    "Read energy demanding data from file, and preview data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"nyc_energy.csv\", parse_dates=['timeStamp'])\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Split the data to train and test\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train = data[data['timeStamp'] < '2017-02-01']\n",
+    "test = data[data['timeStamp'] >= '2017-02-01']\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Prepare the test data, we will feed X_test to the fitted model and get prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_test = test.pop('demand').values\n",
+    "X_test = test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Split the train data to train and valid\n",
+    "\n",
+    "Use one month's data as valid data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = train[train['timeStamp'] < '2017-01-01']\n",
+    "X_valid = train[train['timeStamp'] >= '2017-01-01']\n",
+    "y_train = X_train.pop('demand').values\n",
+    "y_valid = X_valid.pop('demand').values\n",
+    "print(X_train.shape)\n",
+    "print(y_train.shape)\n",
+    "print(X_valid.shape)\n",
+    "print(y_valid.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiate Auto ML Config\n",
+    "\n",
+    "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|forecasting|\n",
+    "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
+    "|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
+    "|**X_valid**|Data used to evaluate a model in a iteration. (sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y_valid**|Data used to evaluate a model in a iteration. (sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
+    "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time_column_name = 'timeStamp'\n",
+    "automl_settings = {\n",
+    "    \"time_column_name\": time_column_name,\n",
+    "}\n",
+    "\n",
+    "\n",
+    "automl_config = AutoMLConfig(task = 'forecasting',\n",
+    "                             debug_log = 'automl_nyc_energy_errors.log',\n",
+    "                             primary_metric='normalized_root_mean_squared_error',\n",
+    "                             iterations = 10,\n",
+    "                             iteration_timeout_minutes = 5,\n",
+    "                             X = X_train,\n",
+    "                             y = y_train,\n",
+    "                             X_valid = X_valid,\n",
+    "                             y_valid = y_valid,\n",
+    "                             path=project_folder,\n",
+    "                             verbosity = logging.INFO,\n",
+    "                            **automl_settings)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the Model\n",
+    "\n",
+    "You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
+    "You will see the currently running iterations printing to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "Below we select the best pipeline from our iterations. The get_output method on automl_classifier returns the best run and the fitted model for the last fit invocation. There are overloads on get_output that allow you to retrieve the best run and fitted model for any logged metric or a particular iteration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()\n",
+    "fitted_model.steps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test the Best Fitted Model\n",
+    "\n",
+    "Predict on training and test set, and calculate residual values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = fitted_model.predict(X_test)\n",
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define a Check Data Function\n",
+    "\n",
+    "Remove the nan values from y_test to avoid error when calculate metrics "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _check_calc_input(y_true, y_pred, rm_na=True):\n",
+    "    \"\"\"\n",
+    "    Check that 'y_true' and 'y_pred' are non-empty and\n",
+    "    have equal length.\n",
+    "\n",
+    "    :param y_true: Vector of actual values\n",
+    "    :type y_true: array-like\n",
+    "\n",
+    "    :param y_pred: Vector of predicted values\n",
+    "    :type y_pred: array-like\n",
+    "\n",
+    "    :param rm_na:\n",
+    "        If rm_na=True, remove entries where y_true=NA and y_pred=NA.\n",
+    "    :type rm_na: boolean\n",
+    "\n",
+    "    :return:\n",
+    "        Tuple (y_true, y_pred). if rm_na=True,\n",
+    "        the returned vectors may differ from their input values.\n",
+    "    :rtype: Tuple with 2 entries\n",
+    "    \"\"\"\n",
+    "    if len(y_true) != len(y_pred):\n",
+    "        raise ValueError(\n",
+    "            'the true values and prediction values do not have equal length.')\n",
+    "    elif len(y_true) == 0:\n",
+    "        raise ValueError(\n",
+    "            'y_true and y_pred are empty.')\n",
+    "    # if there is any non-numeric element in the y_true or y_pred,\n",
+    "    # the ValueError exception will be thrown.\n",
+    "    y_true = np.array(y_true).astype(float)\n",
+    "    y_pred = np.array(y_pred).astype(float)\n",
+    "    if rm_na:\n",
+    "        # remove entries both in y_true and y_pred where at least\n",
+    "        # one element in y_true or y_pred is missing\n",
+    "        y_true_rm_na = y_true[~(np.isnan(y_true) | np.isnan(y_pred))]\n",
+    "        y_pred_rm_na = y_pred[~(np.isnan(y_true) | np.isnan(y_pred))]\n",
+    "        return (y_true_rm_na, y_pred_rm_na)\n",
+    "    else:\n",
+    "        return y_true, y_pred"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Use the Check Data Function to remove the nan values from y_test to avoid error when calculate metrics "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_test,y_pred =  _check_calc_input(y_test,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate metrics for the prediction\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
+    "# Explained variance score: 1 is perfect prediction\n",
+    "print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
+    "print('R2 score: %.2f' % r2_score(y_test, y_pred))\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Plot outputs\n",
+    "%matplotlib notebook\n",
+    "test_pred = plt.scatter(y_test, y_pred, color='b')\n",
+    "test_test = plt.scatter(y_test, y_test, color='g')\n",
+    "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "xiaga"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/18b.auto-ml-forecasting.ipynb

@@ -0,0 +1,390 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML 18B: Orange Juice Sales Forecasting\n",
+    "\n",
+    "In this example, we use AutoML to find and tune a time-series forecasting model.\n",
+    "\n",
+    "Make sure you have executed the [configuration notebook](00.configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook, you will:\n",
+    "1. Create an Experiment in an existing Workspace\n",
+    "2. Instantiate an AutoMLConfig \n",
+    "3. Find and train a forecasting model using local compute\n",
+    "4. Evaluate the performance of the model\n",
+    "\n",
+    "## Sample Data\n",
+    "The examples in the follow code samples use the [University of Chicago's Dominick's Finer Foods dataset](https://research.chicagobooth.edu/kilts/marketing-databases/dominicks) to forecast orange juice sales. Dominick's was a grocery chain in the Chicago metropolitan area."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Experiment\n",
+    "\n",
+    "As part of the setup you have already created a <b>Workspace</b>. To run AutoML, you also need to create an <b>Experiment</b>. An Experiment is a named object in a Workspace which represents a predictive task, the output of which is a trained model and a set of evaluation metrics for the model. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import azureml.core\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import logging\n",
+    "\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# choose a name for the run history container in the workspace\n",
+    "experiment_name = 'automl-ojsalesforecasting'\n",
+    "# project folder\n",
+    "project_folder = './sample_projects/automl-local-ojsalesforecasting'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Run History Name'] = experiment_name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data=output, index=['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read Data\n",
+    "You are now ready to load the historical orange juice sales data. We will load the CSV file into a plain pandas DataFrame; the time column in the CSV is called _WeekStarting_, so it will be specially parsed into the datetime type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time_column_name = 'WeekStarting'\n",
+    "data = pd.read_csv(\"dominicks_OJ.csv\", parse_dates=[time_column_name])\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each row in the DataFrame holds a quantity of weekly sales for an OJ brand at a single store. The data also includes the sales price, a flag indicating if the OJ brand was advertised in the store that week, and some customer demographic information based on the store location. For historical reasons, the data also include the logarithm of the sales quantity. The Dominick's grocery data is commonly used to illustrate econometric modeling techniques where logarithms of quantities are generally preferred.    \n",
+    "\n",
+    "The task is now to build a time-series model for the _Quantity_ column. It is important to note that this dataset is comprised of many individual time-series - one for each unique combination of _Store_ and _Brand_. To distinguish the individual time-series, we thus define the **grain** - the columns whose values determine the boundaries between time-series: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grain_column_names = ['Store', 'Brand']\n",
+    "nseries = data.groupby(grain_column_names).ngroups\n",
+    "print('Data contains {0} individual time-series.'.format(nseries))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Splitting\n",
+    "For the purposes of demonstration and later forecast evaluation, we now split the data into a training and a testing set. The test set will contain the final 20 weeks of observed sales for each time-series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ntest_periods = 20\n",
+    "\n",
+    "def split_last_n_by_grain(df, n):\n",
+    "    \"\"\"\n",
+    "    Group df by grain and split on last n rows for each group\n",
+    "    \"\"\"\n",
+    "    df_grouped = (df.sort_values(time_column_name) # Sort by ascending time\n",
+    "                  .groupby(grain_column_names, group_keys=False))\n",
+    "    df_head = df_grouped.apply(lambda dfg: dfg.iloc[:-n])\n",
+    "    df_tail = df_grouped.apply(lambda dfg: dfg.iloc[-n:])\n",
+    "    return df_head, df_tail\n",
+    "\n",
+    "X_train, X_test = split_last_n_by_grain(data, ntest_periods)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Modeling\n",
+    "\n",
+    "For forecasting tasks, AutoML uses pre-processing and estimation steps that are specific to time-series. AutoML will undertake the following pre-processing steps:\n",
+    "* Detect time-series sample frequency (e.g. hourly, daily, weekly) and create new records for absent time points to make the series regular. A regular time series has a well-defined frequency and has a value at every sample point in a contiguous time span \n",
+    "* Impute missing values in the target (via forward-fill) and feature columns (using median column values) \n",
+    "* Create grain-based features to enable fixed effects across different series\n",
+    "* Create time-based features to assist in learning seasonal patterns\n",
+    "* Encode categorical variables to numeric quantities\n",
+    "\n",
+    "AutoML will currently train a single, regression-type model across **all** time-series in a given training set. This allows the model to generalize across related series.\n",
+    "\n",
+    "You are almost ready to start an AutoML training job. We will first need to create a validation set from the existing training set (i.e. for hyper-parameter tuning): "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nvalidation_periods = 20\n",
+    "X_train, X_validate = split_last_n_by_grain(X_train, nvalidation_periods)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We also need to separate the target column from the rest of the DataFrame: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_column_name = 'Quantity'\n",
+    "y_train = X_train.pop(target_column_name).values\n",
+    "y_validate = X_validate.pop(target_column_name).values "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an AutoMLConfig\n",
+    "\n",
+    "The AutoMLConfig object defines the settings and data for an AutoML training job. Here, we set necessary inputs like the task type, the number of AutoML iterations to try, and the training and validation data. \n",
+    "\n",
+    "For forecasting tasks, there are some additional parameters that can be set: the name of the input data column, holding the date/time and the grain column names. A time column is required for forecasting, while the grain is optional. If a grain is not given, the forecaster assumes that the whole dataset is a single time-series. We also pass a list of columns to drop prior to modeling. The _logQuantity_ column is completely correlated with the target quantity, so it must be removed to prevent a target leak. \n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|forecasting|\n",
+    "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
+    "|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
+    "|**X**|Training matrix of features, shape = [n_training_samples, n_features]|\n",
+    "|**y**|Target values, shape = [n_training_samples, ]|\n",
+    "|**X_valid**|Validation matrix of features, shape = [n_validation_samples, n_features]|\n",
+    "|**y_valid**|Target values for validation, shape = [n_validation_samples, ]\n",
+    "|**enable_ensembling**|Allow AutoML to create ensembles of the best performing models\n",
+    "|**debug_log**|Log file path for writing debugging information\n",
+    "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_settings = {\n",
+    "    'time_column_name': time_column_name,\n",
+    "    'grain_column_names': grain_column_names,\n",
+    "    'drop_column_names': ['logQuantity']\n",
+    "}\n",
+    "\n",
+    "automl_config = AutoMLConfig(task='forecasting',\n",
+    "                             debug_log='automl_oj_sales_errors.log',\n",
+    "                             primary_metric='normalized_root_mean_squared_error',\n",
+    "                             iterations=10,\n",
+    "                             X=X_train,\n",
+    "                             y=y_train,\n",
+    "                             X_valid=X_validate,\n",
+    "                             y_valid=y_validate,\n",
+    "                             enable_ensembling=False,\n",
+    "                             path=project_folder,\n",
+    "                             verbosity=logging.INFO,\n",
+    "                            **automl_settings)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the Model\n",
+    "\n",
+    "You can now submit a new training run. For local runs, the execution is synchronous. Depending on the data and number of iterations this operation may take several minutes.\n",
+    "Information from each iteration will be printed to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "Each run within an Experiment stores serialized (i.e. pickled) pipelines from the AutoML iterations. We can now retrieve the pipeline with the best performance on the validation dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_pipeline = local_run.get_output()\n",
+    "fitted_pipeline.steps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Make Predictions from the Best Fitted Model\n",
+    "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. First, we remove the target values from the test set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_test = X_test.pop(target_column_name).values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_test.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To produce predictions on the test set, we need to know the feature values at all dates in the test set. This requirement is somewhat reasonable for the OJ sales data since the features mainly consist of price, which is usually set in advance, and customer demographics which are approximately constant for each store over the 20 week forecast horizon in the testing data. \n",
+    "\n",
+    "The target predictions can be retrieved by calling the `predict` method on the best model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = fitted_pipeline.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate evaluation metrics for the prediction\n",
+    "To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def MAPE(actual, pred):\n",
+    "    \"\"\"\n",
+    "    Calculate mean absolute percentage error.\n",
+    "    Remove NA and values where actual is close to zero\n",
+    "    \"\"\"\n",
+    "    not_na = ~(np.isnan(actual) | np.isnan(pred))\n",
+    "    not_zero = ~np.isclose(actual, 0.0)\n",
+    "    actual_safe = actual[not_na & not_zero]\n",
+    "    pred_safe = pred[not_na & not_zero]\n",
+    "    APE = 100*np.abs((actual_safe - pred_safe)/actual_safe)\n",
+    "    return np.mean(APE)\n",
+    "\n",
+    "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
+    "print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
+    "print('MAPE: %.2f' % MAPE(y_test, y_pred))"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "erwright"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -0,0 +1,306 @@
+# Table of Contents
+1. [Automated ML Introduction](#introduction)
+1. [Running samples in Azure Notebooks](#jupyter)
+1. [Running samples in Azure Databricks](#databricks)
+1. [Running samples in a Local Conda environment](#localconda)
+1. [Automated ML SDK Sample Notebooks](#samples)
+1. [Documentation](#documentation)
+1. [Running using python command](#pythoncommand)
+1. [Troubleshooting](#troubleshooting)
+
+<a name="introduction"></a>
+# Automated ML introduction
+Automated machine learning (automated ML) builds high quality machine learning models for you by automating model and hyperparameter selection. Bring a labelled dataset that you want to build a model for, automated ML will give you a high quality machine learning model that you can use for predictions.
+
+
+If you are new to Data Science, AutoML will help you get jumpstarted by simplifying machine learning model building. It abstracts you from needing to perform model selection, hyperparameter selection and in one step creates a high quality trained model for you to use.
+
+If you are an experienced data scientist, AutoML will help increase your productivity by intelligently performing the model and hyperparameter selection for your training and generates high quality models much quicker than manually specifying several combinations of the parameters and running training jobs. AutoML provides visibility and access to all the training jobs and the performance characteristics of the models to help you further tune the pipeline if you desire.
+
+Below are the three execution environments supported by AutoML.
+
+
+ <a name="jupyter"></a>
+## Running samples in Azure Notebooks - Jupyter based notebooks in the Azure cloud
+
+1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
+[Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks.
+1. Follow the instructions in the [configuration](configuration.ipynb) notebook to create and connect to a workspace.
+1. Open one of the sample notebooks.
+
+ <a name="databricks"></a>
+## Running samples in Azure Databricks
+
+**NOTE**: Please create your Azure Databricks cluster as v4.x (high concurrency preferred) with **Python 3** (dropdown).
+**NOTE**: You should at least have contributor access to your Azure subcription to run the notebook.
+- Please remove the previous SDK version if there is any and install the latest SDK by installing **azureml-sdk[automl_databricks]** as a PyPi library in Azure Databricks workspace.
+- Download the sample notebook 16a.auto-ml-classification-local-azuredatabricks from [GitHub](https://github.com/Azure/MachineLearningNotebooks) and import into the Azure databricks workspace.
+- Attach the notebook to the cluster.
+
+<a name="localconda"></a>
+## Running samples in a Local Conda environment
+
+To run these notebook on your own notebook server, use these installation instructions.
+
+The instructions below will install everything you need and then start a Jupyter notebook.  To start your Jupyter notebook manually, use:
+
+```
+conda activate azure_automl
+jupyter notebook
+```
+
+or on Mac:
+
+```
+source activate azure_automl
+jupyter notebook
+```
+
+
+### 1. Install mini-conda from [here](https://conda.io/miniconda.html), choose Python 3.7 or higher.
+- **Note**: if you already have conda installed, you can keep using it but it should be version 4.4.10 or later (as shown by: conda -V).  If you have a previous version installed, you can update it using the command: conda update conda.
+There's no need to install mini-conda specifically.
+
+### 2. Downloading the sample notebooks
+- Download the sample notebooks from [GitHub](https://github.com/Azure/MachineLearningNotebooks) as zip and extract the contents to a local directory.  The AutoML sample notebooks are in the "automl" folder.
+
+### 3. Setup a new conda environment
+The **automl/automl_setup** script creates a new conda environment, installs the necessary packages, configures the widget and starts a jupyter notebook.
+It takes the conda environment name as an optional parameter.  The default conda environment name is azure_automl.  The exact command depends on the operating system.  See the specific sections below for Windows, Mac and Linux.  It can take about 10 minutes to execute.
+## Windows
+Start an **Anaconda Prompt** window, cd to the **automl** folder where the sample notebooks were extracted and then run:
+```
+automl_setup
+```
+## Mac
+Install "Command line developer tools" if it is not already installed (you can use the command: `xcode-select --install`).
+
+Start a Terminal windows, cd to the **automl** folder where the sample notebooks were extracted and then run:
+
+```
+bash automl_setup_mac.sh
+```
+
+## Linux
+cd to the **automl** folder where the sample notebooks were extracted and then run:
+
+```
+bash automl_setup_linux.sh
+```
+
+### 4. Running configuration.ipynb
+- Before running any samples you next need to run the configuration notebook. Click on configuration.ipynb notebook
+- Execute the cells in the notebook to Register Machine Learning Services Resource Provider and create a workspace. (*instructions in notebook*)
+
+### 5. Running Samples
+- Please make sure you use the Python [conda env:azure_automl] kernel when trying the sample Notebooks.
+- Follow the instructions in the individual notebooks to explore various features in AutoML
+
+<a name="samples"></a>
+# Automated ML SDK Sample Notebooks
+- [configuration.ipynb](configuration.ipynb)
+    - Create new Azure ML Workspace
+    - Save Workspace configuration file
+
+- [auto-ml-classification.ipynb](classification/auto-ml-classification.ipynb)
+    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
+    - Simple example of using Auto ML for classification
+    - Uses local compute for training
+
+- [auto-ml-regression.ipynb](regression/auto-ml-regression.ipynb)
+    - Dataset: scikit learn's [diabetes dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html)
+    - Simple example of using Auto ML for regression
+    - Uses local compute for training
+
+- [auto-ml-remote-execution.ipynb](remote-execution/auto-ml-remote-execution.ipynb)
+    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
+    - Example of using Auto ML for classification using a remote linux DSVM for training
+    - Parallel execution of iterations
+    - Async tracking of progress
+    - Cancelling individual iterations or entire run
+    - Retrieving models for any iteration or logged metric
+    - Specify automl settings as kwargs
+
+- [auto-ml-remote-batchai.ipynb](remote-batchai/auto-ml-remote-batchai.ipynb)
+    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
+    - Example of using automated ML for classification using a remote Batch AI compute for training
+    - Parallel execution of iterations
+    - Async tracking of progress
+    - Cancelling individual iterations or entire run
+    - Retrieving models for any iteration or logged metric
+    - Specify automl settings as kwargs
+
+- [auto-ml-remote-attach.ipynb](remote-attach/auto-ml-remote-attach.ipynb)
+    - Dataset: [Burning Man 2016 dataset](https://innovate.burningman.org/datasets-page/)
+    - handling text data with preprocess flag
+    - Reading data from a blob store for remote executions
+    - using pandas dataframes for reading data
+
+- [auto-ml-missing-data-blacklist-early-termination.ipynb](missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.ipynb)
+    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
+    - Blacklist certain pipelines
+    - Specify a target metrics to indicate stopping criteria
+    - Handling Missing Data in the input
+
+- [auto-ml-sparse-data-train-test-split.ipynb](sparse-data-train-test-split/auto-ml-sparse-data-train-test-split.ipynb)
+    - Dataset: Scikit learn's [20newsgroup](http://scikit-learn.org/stable/datasets/twenty_newsgroups.html)
+    - Handle sparse datasets
+    - Specify custom train and validation set
+
+- [auto-ml-exploring-previous-runs.ipynb](exploring-previous-runs/auto-ml-exploring-previous-runs.ipynb)
+    - List all projects for the workspace
+    - List all AutoML Runs for a given project
+    - Get details for a AutoML Run. (Automl settings, run widget & all metrics)
+    - Download fitted pipeline for any iteration
+
+- [auto-ml-remote-execution-with-datastore.ipynb](remote-execution-with-datastore/auto-ml-remote-execution-with-datastore.ipynb)
+    - Dataset: scikit learn's [digit dataset](https://innovate.burningman.org/datasets-page/)
+    - Download the data and store it in DataStore.
+
+- [auto-ml-classification-with-deployment.ipynb](classification-with-deployment/auto-ml-classification-with-deployment.ipynb)
+    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
+    - Simple example of using Auto ML for classification
+    - Registering the model
+    - Creating Image and creating aci service
+    - Testing the aci service
+
+- [auto-ml-sample-weight.ipynb](sample-weight/auto-ml-sample-weight.ipynb)
+    - How to specifying sample_weight
+    - The difference that it makes to test results
+
+- [auto-ml-dataprep.ipynb](dataprep/auto-ml-dataprep.ipynb)
+    - Using DataPrep for reading data
+
+- [auto-ml-dataprep-remote-execution.ipynb](dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb)
+    - Using DataPrep for reading data with remote execution
+
+- [auto-ml-classification-local-azuredatabricks.ipynb](classification-local-azuredatabricks/auto-ml-classification-local-azuredatabricks.ipynb)
+    - Dataset: scikit learn's [digit dataset](https://innovate.burningman.org/datasets-page/)
+    - Example of using AutoML for classification using Azure Databricks as the platform for training
+
+- [auto-ml-classification_with_tensorflow.ipynb](classification_with_tensorflow/auto-ml-classification_with_tensorflow.ipynb)
+    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
+    - Simple example of using Auto ML for classification with whitelisting tensorflow models.checkout
+    - Uses local compute for training
+
+- [auto-ml-timeseries.ipynb](timeseries/auto-ml-timeseries.ipynb)
+    - Dataset: NYC energy demanding data
+    - Example of using AutoML for timeseries data training
+
+<a name="documentation"></a>
+# Documentation
+## Table of Contents
+1. [Automated ML Settings ](#automlsettings)
+1. [Cross validation split options](#cvsplits)
+1. [Get Data Syntax](#getdata)
+1. [Data pre-processing and featurization](#preprocessing)
+
+<a name="automlsettings"></a>
+## Automated ML Settings
+
+|Property|Description|Default|
+|-|-|-|
+|**primary_metric**|This is the metric that you want to optimize.<br><br> Classification supports the following primary metrics <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i><br><br> Regression supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i><br><i>normalized_root_mean_squared_log_error</i>| Classification: accuracy <br><br> Regression: spearman_correlation
+|**iteration_timeout_minutes**|Time limit in minutes for each iteration|None|
+|**iterations**|Number of iterations. In each iteration trains the data with a specific pipeline.  To get the best result, use at least 100. |100|
+|**n_cross_validations**|Number of cross validation splits|None|
+|**validation_size**|Size of validation set as percentage of all training samples|None|
+|**max_concurrent_iterations**|Max number of iterations that would be executed in parallel|1|
+|**preprocess**|*True/False* <br>Setting this to *True* enables preprocessing <br>on the input to handle missing data, and perform some common feature extraction<br>*Note: If input data is Sparse you cannot use preprocess=True*|False|
+|**max_cores_per_iteration**| Indicates how many cores on the compute target would be used to train a single pipeline.<br> You can set it to *-1* to use all cores|1|
+|**experiment_exit_score**|*double* value indicating the target for *primary_metric*. <br> Once the target is surpassed the run terminates|None|
+|**blacklist_models**|*Array* of *strings* indicating models to ignore for Auto ML from the list of models.|None|
+|**whitelist_models**|*Array* of *strings* use only models listed for Auto ML from the list of models..|None|
+ <a name="cvsplits"></a>
+## List of models for white list/blacklist
+**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>**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>
+
+## Cross validation split options
+### K-Folds Cross Validation
+Use *n_cross_validations* setting to specify the number of cross validations. The training data set will be randomly split into *n_cross_validations* folds of equal size. During each cross validation round, one of the folds will be used for validation of the model trained on the remaining folds. This process repeats for *n_cross_validations* rounds until each fold is used once as validation set. Finally, the average scores accross all *n_cross_validations* rounds will be reported, and the corresponding model will be retrained on the whole training data set.
+
+### Monte Carlo Cross Validation (a.k.a. Repeated Random Sub-Sampling)
+Use *validation_size* to specify the percentage of the training data set that should be used for validation, and use *n_cross_validations* to specify the number of cross validations. During each cross validation round, a subset of size *validation_size* will be randomly selected for validation of the model trained on the remaining data. Finally, the average scores accross all *n_cross_validations* rounds will be reported, and the corresponding model will be retrained on the whole training data set.
+
+### Custom train and validation set
+You can specify seperate train and validation set either through the get_data() or directly to the fit method.
+
+<a name="getdata"></a>
+## get_data() syntax
+The *get_data()* function can be used to return a dictionary with these values:
+
+|Key|Type|Dependency|Mutually Exclusive with|Description|
+|:-|:-|:-|:-|:-|
+|X|Pandas Dataframe or Numpy Array|y|data_train, label, columns|All features to train with|
+|y|Pandas Dataframe or Numpy Array|X|label|Label data to train with.  For classification, this should be an array of integers. |
+|X_valid|Pandas Dataframe or Numpy Array|X, y, y_valid|data_train, label|*Optional* All features to validate with.  If this is not specified, X is split between train and validate|
+|y_valid|Pandas Dataframe or Numpy Array|X, y, X_valid|data_train, label|*Optional* The label data to validate with.  If this is not specified, y is split between train and validate|
+|sample_weight|Pandas Dataframe or Numpy Array|y|data_train, label, columns|*Optional* A weight value for each label. Higher values indicate that the sample is more important.|
+|sample_weight_valid|Pandas Dataframe or Numpy Array|y_valid|data_train, label, columns|*Optional* A weight value for each validation label. Higher values indicate that the sample is more important.  If this is not specified, sample_weight is split between train and validate|
+|data_train|Pandas Dataframe|label|X, y, X_valid, y_valid|All data (features+label) to train with|
+|label|string|data_train|X, y, X_valid, y_valid|Which column in data_train represents the label|
+|columns|Array of strings|data_train||*Optional* Whitelist of columns to use for features|
+|cv_splits_indices|Array of integers|data_train||*Optional* List of indexes to split the data for cross validation|
+
+<a name="preprocessing"></a>
+## Data pre-processing and featurization
+If you use `preprocess=True`, the following data preprocessing steps are performed automatically for you:
+
+1. Dropping high cardinality or no variance features
+    - Features with no useful information are dropped from training and validation sets. These include features with all values missing, same value across all rows or with extremely high cardinality (e.g., hashes, IDs or GUIDs).
+2. Missing value imputation
+    - For numerical features, missing values are imputed with average of values in the column.
+    - For categorical features, missing values are imputed with most frequent value.
+3. Generating additional features
+    - For DateTime features: Year, Month, Day, Day of week, Day of year, Quarter, Week of the year, Hour, Minute, Second.
+    - For Text features: Term frequency based on bi-grams and tri-grams, Count vectorizer.
+4. Transformations and encodings
+    - Numeric features with very few unique values are transformed into categorical features.
+
+<a name="pythoncommand"></a>
+# Running using python command
+Jupyter notebook provides a File / Download as / Python (.py) option for saving the notebook as a Python file.
+You can then run this file using the python command.
+However, on Windows the file needs to be modified before it can be run.
+The following condition must be added to the main code in the file:
+
+    if __name__ == "__main__":
+
+The main code of the file must be indented so that it is under this condition.
+
+<a name="troubleshooting"></a>
+# Troubleshooting
+## Iterations fail and the log contains "MemoryError"
+This can be caused by insufficient memory on the DSVM.  AutoML loads all training data into memory.  So, the available memory should be more than the training data size.
+If you are using a remote DSVM, memory is needed for each concurrent iteration.  The 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.
+
+## 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

@@ -0,0 +1,32 @@
+name: azure_automl
+dependencies:
+  # The python interpreter version.
+  # Currently Azure ML only supports 3.5.2 and later.
+- python=3.6
+- nb_conda
+- matplotlib==2.1.0
+- numpy>=1.11.0,<1.15.0
+- cython
+- urllib3<1.24
+- scipy>=1.0.0,<=1.1.0
+- scikit-learn>=0.18.0,<=0.19.1
+- pandas>=0.22.0,<0.23.0
+- tensorflow>=1.12.0
+
+# Required for azuremlftk
+- dill
+- pyodbc
+- statsmodels 
+- numexpr  
+- keras
+- distributed>=1.21.5,<1.24
+  
+- pip:
+
+  # Required for azuremlftk
+  - https://azuremlpackages.blob.core.windows.net/forecasting/azuremlftk-0.1.18323.5a1-py3-none-any.whl
+
+  # Required packages for AzureML execution, history, and data preparation.
+  - azureml-sdk[automl,notebooks,explain]
+  - pandas_ml
+  

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

@@ -0,0 +1,33 @@
+name: azure_automl
+dependencies:
+  # The python interpreter version.
+  # Currently Azure ML only supports 3.5.2 and later.
+- python=3.6
+- nb_conda
+- matplotlib==2.1.0
+- numpy>=1.15.3
+- cython
+- urllib3<1.24
+- scipy>=1.0.0,<=1.1.0
+- scikit-learn>=0.18.0,<=0.19.1
+- pandas>=0.22.0,<0.23.0
+- tensorflow>=1.12.0
+
+# Required for azuremlftk
+- dill
+- pyodbc
+- statsmodels 
+- numexpr  
+- keras
+- distributed>=1.21.5,<1.24
+  
+- pip:
+
+  # Required for azuremlftk
+  - https://azuremlpackages.blob.core.windows.net/forecasting/azuremlftk-0.1.18323.5a1-py3-none-any.whl
+
+  # Required packages for AzureML execution, history, and data preparation.
+  - azureml-sdk[automl,notebooks,explain]
+  - pandas_ml
+  
+

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

@@ -0,0 +1,52 @@
+@echo off
+set conda_env_name=%1
+set automl_env_file=%2
+set PIP_NO_WARN_SCRIPT_LOCATION=0
+
+IF "%conda_env_name%"=="" SET conda_env_name="azure_automl"
+IF "%automl_env_file%"=="" SET automl_env_file="automl_env.yml"
+
+IF NOT EXIST %automl_env_file% GOTO YmlMissing
+
+call conda activate %conda_env_name% 2>nul:
+
+if not errorlevel 1 (
+  echo Upgrading azureml-sdk[automl] in existing conda environment %conda_env_name%
+  call pip install --upgrade azureml-sdk[automl,notebooks]
+  if errorlevel 1 goto ErrorExit
+) else (
+  call conda env create -f %automl_env_file% -n %conda_env_name%
+)
+
+call conda activate %conda_env_name% 2>nul:
+if errorlevel 1 goto ErrorExit
+
+call pip install psutil
+
+call python -m ipykernel install --user --name %conda_env_name% --display-name "Python (%conda_env_name%)"
+
+call jupyter nbextension install --py azureml.widgets --user
+if errorlevel 1 goto ErrorExit
+
+call jupyter nbextension enable --py azureml.widgets --user
+if errorlevel 1 goto ErrorExit
+
+echo.
+echo.
+echo ***************************************
+echo * AutoML setup completed successfully *
+echo ***************************************
+echo.
+echo Starting jupyter notebook - please run notebook 00.configuration
+echo.
+jupyter notebook --log-level=50
+
+goto End
+
+:YmlMissing
+echo File %automl_env_file% not found.
+
+:ErrorExit
+echo Install failed
+
+:End

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

@@ -0,0 +1,48 @@
+#!/bin/bash
+
+CONDA_ENV_NAME=$1
+AUTOML_ENV_FILE=$2
+PIP_NO_WARN_SCRIPT_LOCATION=0
+
+if [ "$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] in existing conda environment" $CONDA_ENV_NAME
+   pip install --upgrade azureml-sdk[automl,notebooks]
+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 install --py azureml.widgets --user &&
+   jupyter nbextension enable --py azureml.widgets --user &&
+   echo "" &&
+   echo "" &&
+   echo "***************************************" &&
+   echo "* AutoML setup completed successfully *" &&
+   echo "***************************************" &&
+   echo "" &&
+   echo "Starting jupyter notebook - please run notebook 00.configuration" &&
+   echo "" &&
+   jupyter notebook --log-level=50
+fi
+
+if [ $? -gt 0 ]
+then
+   echo "Installation failed"
+fi
+
+

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

@@ -0,0 +1,51 @@
+#!/bin/bash
+
+CONDA_ENV_NAME=$1
+AUTOML_ENV_FILE=$2
+PIP_NO_WARN_SCRIPT_LOCATION=0
+
+if [ "$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] in existing conda environment" $CONDA_ENV_NAME
+   pip install --upgrade azureml-sdk[automl,notebooks]
+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 install --py azureml.widgets --user &&
+   jupyter nbextension enable --py azureml.widgets --user &&
+   pip install numpy==1.15.3
+   echo "" &&
+   echo "" &&
+   echo "***************************************" &&
+   echo "* AutoML setup completed successfully *" &&
+   echo "***************************************" &&
+   echo "" &&
+   echo "Starting jupyter notebook - please run notebook 00.configuration" &&
+   echo "" &&
+   jupyter notebook --log-level=50
+fi
+
+if [ $? -gt 0 ]
+then
+   echo "Installation failed"
+fi
+
+
+

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

@@ -0,0 +1,501 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Automated Machine Learning: Classification with Deployment\n",
+    "\n",
+    "In this example we use the scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) to showcase how you can use AutoML for a simple classification problem and deploy it to an Azure Container Instance (ACI).\n",
+    "\n",
+    "Make sure you have executed the [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.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# choose a name for experiment\n",
+    "experiment_name = 'automl-local-classification'\n",
+    "# project folder\n",
+    "project_folder = './sample_projects/automl-local-classification'\n",
+    "\n",
+    "experiment=Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data=output, index=['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|classification or regression|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**n_cross_validations**|Number of cross validation splits.|\n",
+    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
+    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_train = digits.data[10:,:]\n",
+    "y_train = digits.target[10:]\n",
+    "\n",
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             name = experiment_name,\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             primary_metric = 'AUC_weighted',\n",
+    "                             iteration_timeout_minutes = 20,\n",
+    "                             iterations = 10,\n",
+    "                             n_cross_validations = 2,\n",
+    "                             verbosity = logging.INFO,\n",
+    "                             X = X_train, \n",
+    "                             y = y_train,\n",
+    "                             path = project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
+    "In this example, we specify `show_output = True` to print currently running iterations to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Register the Fitted Model for Deployment\n",
+    "If neither `metric` nor `iteration` are specified in the `register_model` call, the iteration with the best primary metric is registered."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "description = 'AutoML Model'\n",
+    "tags = None\n",
+    "model = local_run.register_model(description = description, tags = tags)\n",
+    "local_run.model_id # This will be written to the script file later in the notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create Scoring Script"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile score.py\n",
+    "import pickle\n",
+    "import json\n",
+    "import numpy\n",
+    "import azureml.train.automl\n",
+    "from sklearn.externals import joblib\n",
+    "from azureml.core.model import Model\n",
+    "\n",
+    "\n",
+    "def init():\n",
+    "    global model\n",
+    "    model_path = Model.get_model_path(model_name = '<<modelid>>') # this name is model.id of model that we want to deploy\n",
+    "    # deserialize the model file back into a sklearn model\n",
+    "    model = joblib.load(model_path)\n",
+    "\n",
+    "def run(rawdata):\n",
+    "    try:\n",
+    "        data = json.loads(rawdata)['data']\n",
+    "        data = numpy.array(data)\n",
+    "        result = model.predict(data)\n",
+    "    except Exception as e:\n",
+    "        result = str(e)\n",
+    "        return json.dumps({\"error\": result})\n",
+    "    return json.dumps({\"result\":result.tolist()})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a YAML File for the Environment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To ensure the fit results are consistent with the training results, the SDK dependency versions need to be the same as the environment that trains the model. Details about retrieving the versions can be found in notebook [12.auto-ml-retrieve-the-training-sdk-versions](12.auto-ml-retrieve-the-training-sdk-versions.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment_name = 'automl-local-classification'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "ml_run = AutoMLRun(experiment = experiment, run_id = local_run.id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dependencies = ml_run.get_run_sdk_dependencies(iteration = 7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
+    "    print('{}\\t{}'.format(p, dependencies[p]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "\n",
+    "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-sdk[automl]'])\n",
+    "\n",
+    "conda_env_file_name = 'myenv.yml'\n",
+    "myenv.save_to_file('.', conda_env_file_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Substitute the actual version number in the environment file.\n",
+    "# This is not strictly needed in this notebook because the model should have been generated using the current SDK version.\n",
+    "# However, we include this in case this code is used on an experiment from a previous SDK version.\n",
+    "\n",
+    "with open(conda_env_file_name, 'r') as cefr:\n",
+    "    content = cefr.read()\n",
+    "\n",
+    "with open(conda_env_file_name, 'w') as cefw:\n",
+    "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
+    "\n",
+    "# Substitute the actual model id in the script file.\n",
+    "\n",
+    "script_file_name = 'score.py'\n",
+    "\n",
+    "with open(script_file_name, 'r') as cefr:\n",
+    "    content = cefr.read()\n",
+    "\n",
+    "with open(script_file_name, 'w') as cefw:\n",
+    "    cefw.write(content.replace('<<modelid>>', local_run.model_id))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a Container Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.image import Image, ContainerImage\n",
+    "\n",
+    "image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
+    "                                 execution_script = script_file_name,\n",
+    "                                 conda_file = conda_env_file_name,\n",
+    "                                 tags = {'area': \"digits\", 'type': \"automl_classification\"},\n",
+    "                                 description = \"Image for automl classification sample\")\n",
+    "\n",
+    "image = Image.create(name = \"automlsampleimage\",\n",
+    "                     # this is the model object \n",
+    "                     models = [model],\n",
+    "                     image_config = image_config, \n",
+    "                     workspace = ws)\n",
+    "\n",
+    "image.wait_for_creation(show_output = True)\n",
+    "\n",
+    "if image.creation_state == 'Failed':\n",
+    "    print(\"Image build log at: \" + image.image_build_log_uri)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deploy the Image as a Web Service on Azure Container Instance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.webservice import AciWebservice\n",
+    "\n",
+    "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
+    "                                               memory_gb = 1, \n",
+    "                                               tags = {'area': \"digits\", 'type': \"automl_classification\"}, \n",
+    "                                               description = 'sample service for Automl Classification')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.webservice import Webservice\n",
+    "\n",
+    "aci_service_name = 'automl-sample-01'\n",
+    "print(aci_service_name)\n",
+    "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
+    "                                           image = image,\n",
+    "                                           name = aci_service_name,\n",
+    "                                           workspace = ws)\n",
+    "aci_service.wait_for_deployment(True)\n",
+    "print(aci_service.state)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Delete a Web Service"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#aci_service.delete()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get Logs from a Deployed Web Service"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#aci_service.get_logs()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test a Web Service"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Randomly select digits and test\n",
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:10, :]\n",
+    "y_test = digits.target[:10]\n",
+    "images = digits.images[:10]\n",
+    "\n",
+    "for index in np.random.choice(len(y_test), 3, replace = False):\n",
+    "    print(index)\n",
+    "    test_sample = json.dumps({'data':X_test[index:index + 1].tolist()})\n",
+    "    predicted = aci_service.run(input_data = test_sample)\n",
+    "    label = y_test[index]\n",
+    "    predictedDict = json.loads(predicted)\n",
+    "    title = \"Label value = %d  Predicted value = %s \" % ( label,predictedDict['result'][0])\n",
+    "    fig = plt.figure(1, figsize = (3,3))\n",
+    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "    ax1.set_title(title)\n",
+    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "    plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -0,0 +1,414 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Automated Machine Learning: Classification with Local Compute\n",
+    "\n",
+    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
+    "\n",
+    "Make sure you have executed the [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.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose a name for the experiment and specify the project folder.\n",
+    "experiment_name = 'automl-local-classification'\n",
+    "project_folder = './sample_projects/automl-local-classification'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Training Data\n",
+    "\n",
+    "This uses scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "\n",
+    "digits = datasets.load_digits()\n",
+    "\n",
+    "# Exclude the first 100 rows from training so that they can be used for test.\n",
+    "X_train = digits.data[100:,:]\n",
+    "y_train = digits.target[100:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|classification or regression|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**n_cross_validations**|Number of cross validation splits.|\n",
+    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
+    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             primary_metric = 'AUC_weighted',\n",
+    "                             iteration_timeout_minutes = 60,\n",
+    "                             iterations = 25,\n",
+    "                             n_cross_validations = 3,\n",
+    "                             verbosity = logging.INFO,\n",
+    "                             X = X_train, \n",
+    "                             y = y_train,\n",
+    "                             path = project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
+    "In this example, we specify `show_output = True` to print currently running iterations to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Optionally, you can continue an interrupted local run by calling `continue_experiment` without the `iterations` parameter, or run more iterations for a completed run by specifying the `iterations` parameter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = local_run.continue_experiment(X = X_train, \n",
+    "                                          y = y_train, \n",
+    "                                          show_output = True,\n",
+    "                                          iterations = 5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(local_run).show() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(local_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model that has the smallest `log_loss` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lookup_metric = \"log_loss\"\n",
+    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration\n",
+    "Show the run and the model from the third iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 3\n",
+    "third_run, third_model = local_run.get_output(iteration = iteration)\n",
+    "print(third_run)\n",
+    "print(third_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test the Best Fitted Model\n",
+    "\n",
+    "#### Load Test Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:10, :]\n",
+    "y_test = digits.target[:10]\n",
+    "images = digits.images[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Testing Our Best Fitted Model\n",
+    "We will try to predict 2 digits and see how our model works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Randomly select digits and test.\n",
+    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+    "    print(index)\n",
+    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+    "    label = y_test[index]\n",
+    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
+    "    fig = plt.figure(1, figsize = (3,3))\n",
+    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "    ax1.set_title(title)\n",
+    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "    plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -0,0 +1,390 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Automated Machine Learning: Classification with Local Compute with Tesnorflow DNNClassifier and LinearClassifier using whitelist models\n",
+    "\n",
+    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
+    "\n",
+    "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.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose a name for the experiment and specify the project folder.\n",
+    "experiment_name = 'automl-local-classification'\n",
+    "project_folder = './sample_projects/automl-local-classification'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Training Data\n",
+    "\n",
+    "This uses scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "\n",
+    "digits = datasets.load_digits()\n",
+    "\n",
+    "# Exclude the first 100 rows from training so that they can be used for test.\n",
+    "X_train = digits.data[100:,:]\n",
+    "y_train = digits.target[100:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|classification or regression|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**n_cross_validations**|Number of cross validation splits.|\n",
+    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
+    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             primary_metric = 'AUC_weighted',\n",
+    "                             iteration_timeout_minutes = 60,\n",
+    "                             iterations = 10,\n",
+    "                             n_cross_validations = 3,\n",
+    "                             verbosity = logging.INFO,\n",
+    "                             X = X_train, \n",
+    "                             y = y_train,\n",
+    "                             enable_tf=True,\n",
+    "                             whitelist_models=[\"TensorFlowLinearClassifier\", \"TensorFlowDNN\"],\n",
+    "                             path = project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
+    "In this example, we specify `show_output = True` to print currently running iterations to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(local_run).show() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(local_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model that has the smallest `log_loss` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lookup_metric = \"log_loss\"\n",
+    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration\n",
+    "Show the run and the model from the third iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 3\n",
+    "third_run, third_model = local_run.get_output(iteration = iteration)\n",
+    "print(third_run)\n",
+    "print(third_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test the Best Fitted Model\n",
+    "\n",
+    "#### Load Test Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:10, :]\n",
+    "y_test = digits.target[:10]\n",
+    "images = digits.images[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Testing Our Best Fitted Model\n",
+    "We will try to predict 2 digits and see how our model works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Randomly select digits and test.\n",
+    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+    "    print(index)\n",
+    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+    "    label = y_test[index]\n",
+    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
+    "    fig = plt.figure(1, figsize = (3,3))\n",
+    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "    ax1.set_title(title)\n",
+    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "    plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -0,0 +1,497 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Automated Machine Learning: Prepare Data using `azureml.dataprep` for Remote Execution (DSVM)\n",
+    "In this example we showcase how you can use the `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone; full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
+    "\n",
+    "Make sure you have executed the [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": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import time\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.compute import DsvmCompute\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "import azureml.dataprep as dprep\n",
+    "from azureml.train.automl import AutoMLConfig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    " \n",
+    "# choose a name for experiment\n",
+    "experiment_name = 'automl-dataprep-remote-dsvm'\n",
+    "# project folder\n",
+    "project_folder = './sample_projects/automl-dataprep-remote-dsvm'\n",
+    " \n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    " \n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading Data using DataPrep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
+    "# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
+    "simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
+    "X = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1)  # Remove the header row.\n",
+    "\n",
+    "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
+    "# and convert column types manually.\n",
+    "# Here we read a comma delimited file and convert all columns to integers.\n",
+    "y = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Review the Data Preparation Result\n",
+    "\n",
+    "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X.skip(1).head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "This creates a general AutoML settings object applicable for both local and remote runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_settings = {\n",
+    "    \"iteration_timeout_minutes\" : 10,\n",
+    "    \"iterations\" : 2,\n",
+    "    \"primary_metric\" : 'AUC_weighted',\n",
+    "    \"preprocess\" : False,\n",
+    "    \"verbosity\" : logging.INFO,\n",
+    "    \"n_cross_validations\": 3\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Remote Run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create or Attach a Remote Linux DSVM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dsvm_name = 'mydsvmc'\n",
+    "\n",
+    "try:\n",
+    "    while ws.compute_targets[dsvm_name].provisioning_state == 'Creating':\n",
+    "        time.sleep(1)\n",
+    "        \n",
+    "    dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
+    "    print('Found existing DVSM.')\n",
+    "except:\n",
+    "    print('Creating a new DSVM.')\n",
+    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
+    "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
+    "    dsvm_compute.wait_for_completion(show_output = True)\n",
+    "    print(\"Waiting one minute for ssh to be accessible\")\n",
+    "    time.sleep(60) # Wait for ssh to be accessible"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.runconfig import RunConfiguration\n",
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "\n",
+    "conda_run_config = RunConfiguration(framework=\"python\")\n",
+    "\n",
+    "conda_run_config.target = dsvm_compute\n",
+    "\n",
+    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
+    "conda_run_config.environment.python.conda_dependencies = cd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pass Data with `Dataflow` Objects\n",
+    "\n",
+    "The `Dataflow` objects captured above can also be passed to the `submit` method for a remote run. AutoML will serialize the `Dataflow` object and send it to the remote compute target. The `Dataflow` will not be evaluated locally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             path = project_folder,\n",
+    "                             run_configuration=conda_run_config,\n",
+    "                             X = X,\n",
+    "                             y = y,\n",
+    "                             **automl_settings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remote_run = experiment.submit(automl_config, show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(remote_run).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(remote_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "    \n",
+    "import pandas as pd\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = remote_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model that has the smallest `log_loss` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lookup_metric = \"log_loss\"\n",
+    "best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration\n",
+    "Show the run and the model from the first iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 0\n",
+    "best_run, fitted_model = remote_run.get_output(iteration = iteration)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test the Best Fitted Model\n",
+    "\n",
+    "#### Load Test Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "\n",
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:10, :]\n",
+    "y_test = digits.target[:10]\n",
+    "images = digits.images[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Testing Our Best Fitted Model\n",
+    "We will try to predict 2 digits and see how our model works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Randomly select digits and test\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import random\n",
+    "import numpy as np\n",
+    "\n",
+    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+    "    print(index)\n",
+    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+    "    label = y_test[index]\n",
+    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
+    "    fig = plt.figure(1, figsize=(3,3))\n",
+    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "    ax1.set_title(title)\n",
+    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Appendix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Capture the `Dataflow` Objects for Later Use in AutoML\n",
+    "\n",
+    "`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object can be branched at any point for further usage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sklearn.digits.data + target\n",
+    "digits_complete = dprep.auto_read_file('https://dprepdata.blob.core.windows.net/automl-notebook-data/digits-complete.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`digits_complete` (sourced from `sklearn.datasets.load_digits()`) is forked into `dflow_X` to capture all the feature columns and `dflow_y` to capture the label column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits_complete.to_pandas_dataframe().shape\n",
+    "labels_column = 'Column64'\n",
+    "dflow_X = digits_complete.drop_columns(columns = [labels_column])\n",
+    "dflow_y = digits_complete.keep_columns(columns = [labels_column])"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -0,0 +1,446 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Automated Machine Learning: Prepare Data using `azureml.dataprep` for Local Execution\n",
+    "In this example we showcase how you can use the `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone; full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
+    "\n",
+    "Make sure you have executed the [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": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "import azureml.dataprep as dprep\n",
+    "from azureml.train.automl import AutoMLConfig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    " \n",
+    "# choose a name for experiment\n",
+    "experiment_name = 'automl-dataprep-local'\n",
+    "# project folder\n",
+    "project_folder = './sample_projects/automl-dataprep-local'\n",
+    " \n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    " \n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading Data using DataPrep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
+    "# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
+    "simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
+    "X = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1)  # Remove the header row.\n",
+    "\n",
+    "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
+    "# and convert column types manually.\n",
+    "# Here we read a comma delimited file and convert all columns to integers.\n",
+    "y = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Review the Data Preparation Result\n",
+    "\n",
+    "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X.skip(1).head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "This creates a general AutoML settings object applicable for both local and remote runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_settings = {\n",
+    "    \"iteration_timeout_minutes\" : 10,\n",
+    "    \"iterations\" : 2,\n",
+    "    \"primary_metric\" : 'AUC_weighted',\n",
+    "    \"preprocess\" : False,\n",
+    "    \"verbosity\" : logging.INFO,\n",
+    "    \"n_cross_validations\": 3\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Local Run"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pass Data with `Dataflow` Objects\n",
+    "\n",
+    "The `Dataflow` objects captured above can be passed to the `submit` method for a local run. AutoML will retrieve the results from the `Dataflow` for model training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             X = X,\n",
+    "                             y = y,\n",
+    "                             **automl_settings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(local_run).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(local_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "    \n",
+    "import pandas as pd\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model that has the smallest `log_loss` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lookup_metric = \"log_loss\"\n",
+    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration\n",
+    "Show the run and the model from the first iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 0\n",
+    "best_run, fitted_model = local_run.get_output(iteration = iteration)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test the Best Fitted Model\n",
+    "\n",
+    "#### Load Test Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "\n",
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:10, :]\n",
+    "y_test = digits.target[:10]\n",
+    "images = digits.images[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Testing Our Best Fitted Model\n",
+    "We will try to predict 2 digits and see how our model works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Randomly select digits and test\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import random\n",
+    "import numpy as np\n",
+    "\n",
+    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+    "    print(index)\n",
+    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+    "    label = y_test[index]\n",
+    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
+    "    fig = plt.figure(1, figsize=(3,3))\n",
+    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "    ax1.set_title(title)\n",
+    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Appendix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Capture the `Dataflow` Objects for Later Use in AutoML\n",
+    "\n",
+    "`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object can be branched at any point for further usage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sklearn.digits.data + target\n",
+    "digits_complete = dprep.auto_read_file('https://dprepdata.blob.core.windows.net/automl-notebook-data/digits-complete.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`digits_complete` (sourced from `sklearn.datasets.load_digits()`) is forked into `dflow_X` to capture all the feature columns and `dflow_y` to capture the label column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits_complete.to_pandas_dataframe().shape\n",
+    "labels_column = 'Column64'\n",
+    "dflow_X = digits_complete.drop_columns(columns = [labels_column])\n",
+    "dflow_y = digits_complete.keep_columns(columns = [labels_column])"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/exploring-previous-runs/auto-ml-exploring-previous-runs.ipynb

@@ -0,0 +1,336 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Automated Machine Learning: Exploring Previous Runs\n",
+    "\n",
+    "In this example we present some examples on navigating previously executed runs. We also show how you can download a fitted model for any previous run.\n",
+    "\n",
+    "Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you will learn how to:\n",
+    "1. List all experiments in a workspace.\n",
+    "2. List all AutoML runs in an experiment.\n",
+    "3. Get details for an AutoML run, including settings, run widget, and all metrics.\n",
+    "4. Download a fitted pipeline for any iteration.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# List all AutoML Experiments in a Workspace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "import re\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.run import Run\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "experiment_list = Experiment.list(workspace=ws)\n",
+    "\n",
+    "summary_df = pd.DataFrame(index = ['No of Runs'])\n",
+    "pattern = re.compile('^AutoML_[^_]*$')\n",
+    "for experiment in experiment_list:\n",
+    "    all_runs = list(experiment.get_runs())\n",
+    "    automl_runs = []\n",
+    "    for run in all_runs:\n",
+    "        if(pattern.match(run.id)):\n",
+    "            automl_runs.append(run)    \n",
+    "    summary_df[experiment.name] = [len(automl_runs)]\n",
+    "    \n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "summary_df.T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# List AutoML runs for an experiment\n",
+    "Set `experiment_name` to any experiment name from the result of the Experiment.list cell to load the AutoML runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment_name = 'automl-local-classification' # Replace this with any project name from previous cell.\n",
+    "\n",
+    "proj = ws.experiments[experiment_name]\n",
+    "summary_df = pd.DataFrame(index = ['Type', 'Status', 'Primary Metric', 'Iterations', 'Compute', 'Name'])\n",
+    "pattern = re.compile('^AutoML_[^_]*$')\n",
+    "all_runs = list(proj.get_runs(properties={'azureml.runsource': 'automl'}))\n",
+    "automl_runs_project = []\n",
+    "for run in all_runs:\n",
+    "    if(pattern.match(run.id)):\n",
+    "        properties = run.get_properties()\n",
+    "        tags = run.get_tags()\n",
+    "        amlsettings = eval(properties['RawAMLSettingsString'])\n",
+    "        if 'iterations' in tags:\n",
+    "            iterations = tags['iterations']\n",
+    "        else:\n",
+    "            iterations = properties['num_iterations']\n",
+    "        summary_df[run.id] = [amlsettings['task_type'], run.get_details()['status'], properties['primary_metric'], iterations, properties['target'], amlsettings['name']]\n",
+    "        if run.get_details()['status'] == 'Completed':\n",
+    "            automl_runs_project.append(run.id)\n",
+    "    \n",
+    "from IPython.display import HTML\n",
+    "projname_html = HTML(\"<h3>{}</h3>\".format(proj.name))\n",
+    "\n",
+    "from IPython.display import display\n",
+    "display(projname_html)\n",
+    "display(summary_df.T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Get details for an AutoML run\n",
+    "\n",
+    "Copy the project name and run id from the previous cell output to find more details on a particular run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_id = automl_runs_project[0]  # Replace with your own run_id from above run ids\n",
+    "assert (run_id in summary_df.keys()), \"Run id not found! Please set run id to a value from above run ids\"\n",
+    "\n",
+    "from azureml.widgets import RunDetails\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "ml_run = AutoMLRun(experiment = experiment, run_id = run_id)\n",
+    "\n",
+    "summary_df = pd.DataFrame(index = ['Type', 'Status', 'Primary Metric', 'Iterations', 'Compute', 'Name', 'Start Time', 'End Time'])\n",
+    "properties = ml_run.get_properties()\n",
+    "tags = ml_run.get_tags()\n",
+    "status = ml_run.get_details()\n",
+    "amlsettings = eval(properties['RawAMLSettingsString'])\n",
+    "if 'iterations' in tags:\n",
+    "    iterations = tags['iterations']\n",
+    "else:\n",
+    "    iterations = properties['num_iterations']\n",
+    "start_time = None\n",
+    "if 'startTimeUtc' in status:\n",
+    "    start_time = status['startTimeUtc']\n",
+    "end_time = None\n",
+    "if 'endTimeUtc' in status:\n",
+    "    end_time = status['endTimeUtc']\n",
+    "summary_df[ml_run.id] = [amlsettings['task_type'], status['status'], properties['primary_metric'], iterations, properties['target'], amlsettings['name'], start_time, end_time]\n",
+    "display(HTML('<h3>Runtime Details</h3>'))\n",
+    "display(summary_df)\n",
+    "\n",
+    "#settings_df = pd.DataFrame(data = amlsettings, index = [''])\n",
+    "display(HTML('<h3>AutoML Settings</h3>'))\n",
+    "display(amlsettings)\n",
+    "\n",
+    "display(HTML('<h3>Iterations</h3>'))\n",
+    "RunDetails(ml_run).show() \n",
+    "\n",
+    "children = list(ml_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "display(HTML('<h3>Metrics</h3>'))\n",
+    "display(rundata)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Download fitted models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download the Best Model for Any Given Metric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metric = 'AUC_weighted' # Replace with a metric name.\n",
+    "best_run, fitted_model = ml_run.get_output(metric = metric)\n",
+    "fitted_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download the Model for Any Given Iteration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 1 # Replace with an iteration number.\n",
+    "best_run, fitted_model = ml_run.get_output(iteration = iteration)\n",
+    "fitted_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Register fitted model for deployment\n",
+    "If neither `metric` nor `iteration` are specified in the `register_model` call, the iteration with the best primary metric is registered."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "description = 'AutoML Model'\n",
+    "tags = None\n",
+    "ml_run.register_model(description = description, tags = tags)\n",
+    "ml_run.model_id # Use this id to deploy the model as a web service in Azure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Register the Best Model for Any Given Metric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metric = 'AUC_weighted' # Replace with a metric name.\n",
+    "description = 'AutoML Model'\n",
+    "tags = None\n",
+    "ml_run.register_model(description = description, tags = tags, metric = metric)\n",
+    "print(ml_run.model_id) # Use this id to deploy the model as a web service in Azure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Register the Model for Any Given Iteration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 1 # Replace with an iteration number.\n",
+    "description = 'AutoML Model'\n",
+    "tags = None\n",
+    "ml_run.register_model(description = description, tags = tags, iteration = iteration)\n",
+    "print(ml_run.model_id) # Use this id to deploy the model as a web service in Azure."
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -0,0 +1,398 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Automated Machine Learning: Energy Demand Forecasting\n",
+    "\n",
+    "In this example, we show how AutoML can be used for energy demand forecasting.\n",
+    "\n",
+    "Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you would see\n",
+    "1. Creating an Experiment in an existing Workspace\n",
+    "2. Instantiating AutoMLConfig with new task type \"forecasting\" for timeseries data training, and other timeseries related settings: for this dataset we use the basic one: \"time_column_name\" \n",
+    "3. Training the Model using local compute\n",
+    "4. Exploring the results\n",
+    "5. Testing the fitted model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Experiment\n",
+    "\n",
+    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import azureml.core\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import logging\n",
+    "import warnings\n",
+    "# Squash warning messages for cleaner output in the notebook\n",
+    "warnings.showwarning = lambda *args, **kwargs: None\n",
+    "\n",
+    "\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# choose a name for the run history container in the workspace\n",
+    "experiment_name = 'automl-energydemandforecasting'\n",
+    "# project folder\n",
+    "project_folder = './sample_projects/automl-local-energydemandforecasting'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Run History Name'] = experiment_name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data=output, index=['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read Data\n",
+    "Read energy demanding data from file, and preview data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"nyc_energy.csv\", parse_dates=['timeStamp'])\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Split the data to train and test\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train = data[data['timeStamp'] < '2017-02-01']\n",
+    "test = data[data['timeStamp'] >= '2017-02-01']\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Prepare the test data, we will feed X_test to the fitted model and get prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_test = test.pop('demand').values\n",
+    "X_test = test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Split the train data to train and valid\n",
+    "\n",
+    "Use one month's data as valid data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = train[train['timeStamp'] < '2017-01-01']\n",
+    "X_valid = train[train['timeStamp'] >= '2017-01-01']\n",
+    "y_train = X_train.pop('demand').values\n",
+    "y_valid = X_valid.pop('demand').values\n",
+    "print(X_train.shape)\n",
+    "print(y_train.shape)\n",
+    "print(X_valid.shape)\n",
+    "print(y_valid.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiate Auto ML Config\n",
+    "\n",
+    "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|forecasting|\n",
+    "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
+    "|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
+    "|**X_valid**|Data used to evaluate a model in a iteration. (sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y_valid**|Data used to evaluate a model in a iteration. (sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
+    "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time_column_name = 'timeStamp'\n",
+    "automl_settings = {\n",
+    "    \"time_column_name\": time_column_name,\n",
+    "}\n",
+    "\n",
+    "\n",
+    "automl_config = AutoMLConfig(task = 'forecasting',\n",
+    "                             debug_log = 'automl_nyc_energy_errors.log',\n",
+    "                             primary_metric='normalized_root_mean_squared_error',\n",
+    "                             iterations = 10,\n",
+    "                             iteration_timeout_minutes = 5,\n",
+    "                             X = X_train,\n",
+    "                             y = y_train,\n",
+    "                             X_valid = X_valid,\n",
+    "                             y_valid = y_valid,\n",
+    "                             path=project_folder,\n",
+    "                             verbosity = logging.INFO,\n",
+    "                            **automl_settings)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the Model\n",
+    "\n",
+    "You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
+    "You will see the currently running iterations printing to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "Below we select the best pipeline from our iterations. The get_output method on automl_classifier returns the best run and the fitted model for the last fit invocation. There are overloads on get_output that allow you to retrieve the best run and fitted model for any logged metric or a particular iteration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()\n",
+    "fitted_model.steps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test the Best Fitted Model\n",
+    "\n",
+    "Predict on training and test set, and calculate residual values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = fitted_model.predict(X_test)\n",
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define a Check Data Function\n",
+    "\n",
+    "Remove the nan values from y_test to avoid error when calculate metrics "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _check_calc_input(y_true, y_pred, rm_na=True):\n",
+    "    \"\"\"\n",
+    "    Check that 'y_true' and 'y_pred' are non-empty and\n",
+    "    have equal length.\n",
+    "\n",
+    "    :param y_true: Vector of actual values\n",
+    "    :type y_true: array-like\n",
+    "\n",
+    "    :param y_pred: Vector of predicted values\n",
+    "    :type y_pred: array-like\n",
+    "\n",
+    "    :param rm_na:\n",
+    "        If rm_na=True, remove entries where y_true=NA and y_pred=NA.\n",
+    "    :type rm_na: boolean\n",
+    "\n",
+    "    :return:\n",
+    "        Tuple (y_true, y_pred). if rm_na=True,\n",
+    "        the returned vectors may differ from their input values.\n",
+    "    :rtype: Tuple with 2 entries\n",
+    "    \"\"\"\n",
+    "    if len(y_true) != len(y_pred):\n",
+    "        raise ValueError(\n",
+    "            'the true values and prediction values do not have equal length.')\n",
+    "    elif len(y_true) == 0:\n",
+    "        raise ValueError(\n",
+    "            'y_true and y_pred are empty.')\n",
+    "    # if there is any non-numeric element in the y_true or y_pred,\n",
+    "    # the ValueError exception will be thrown.\n",
+    "    y_true = np.array(y_true).astype(float)\n",
+    "    y_pred = np.array(y_pred).astype(float)\n",
+    "    if rm_na:\n",
+    "        # remove entries both in y_true and y_pred where at least\n",
+    "        # one element in y_true or y_pred is missing\n",
+    "        y_true_rm_na = y_true[~(np.isnan(y_true) | np.isnan(y_pred))]\n",
+    "        y_pred_rm_na = y_pred[~(np.isnan(y_true) | np.isnan(y_pred))]\n",
+    "        return (y_true_rm_na, y_pred_rm_na)\n",
+    "    else:\n",
+    "        return y_true, y_pred"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Use the Check Data Function to remove the nan values from y_test to avoid error when calculate metrics "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_test,y_pred =  _check_calc_input(y_test,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate metrics for the prediction\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
+    "# Explained variance score: 1 is perfect prediction\n",
+    "print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
+    "print('R2 score: %.2f' % r2_score(y_test, y_pred))\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Plot outputs\n",
+    "%matplotlib notebook\n",
+    "test_pred = plt.scatter(y_test, y_pred, color='b')\n",
+    "test_test = plt.scatter(y_test, y_test, color='g')\n",
+    "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "xiaga"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -0,0 +1,394 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Automated Machine Learning: Orange Juice Sales Forecasting\n",
+    "\n",
+    "In this example, we use AutoML to find and tune a time-series forecasting model.\n",
+    "\n",
+    "Make sure you have executed the [configuration notebook](../configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook, you will:\n",
+    "1. Create an Experiment in an existing Workspace\n",
+    "2. Instantiate an AutoMLConfig \n",
+    "3. Find and train a forecasting model using local compute\n",
+    "4. Evaluate the performance of the model\n",
+    "\n",
+    "## Sample Data\n",
+    "The examples in the follow code samples use the [University of Chicago's Dominick's Finer Foods dataset](https://research.chicagobooth.edu/kilts/marketing-databases/dominicks) to forecast orange juice sales. Dominick's was a grocery chain in the Chicago metropolitan area."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Experiment\n",
+    "\n",
+    "As part of the setup you have already created a <b>Workspace</b>. To run AutoML, you also need to create an <b>Experiment</b>. An Experiment is a named object in a Workspace which represents a predictive task, the output of which is a trained model and a set of evaluation metrics for the model. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import azureml.core\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import logging\n",
+    "import warnings\n",
+    "# Squash warning messages for cleaner output in the notebook\n",
+    "warnings.showwarning = lambda *args, **kwargs: None\n",
+    "\n",
+    "\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# choose a name for the run history container in the workspace\n",
+    "experiment_name = 'automl-ojsalesforecasting'\n",
+    "# project folder\n",
+    "project_folder = './sample_projects/automl-local-ojsalesforecasting'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Run History Name'] = experiment_name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data=output, index=['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read Data\n",
+    "You are now ready to load the historical orange juice sales data. We will load the CSV file into a plain pandas DataFrame; the time column in the CSV is called _WeekStarting_, so it will be specially parsed into the datetime type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time_column_name = 'WeekStarting'\n",
+    "data = pd.read_csv(\"dominicks_OJ.csv\", parse_dates=[time_column_name])\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each row in the DataFrame holds a quantity of weekly sales for an OJ brand at a single store. The data also includes the sales price, a flag indicating if the OJ brand was advertised in the store that week, and some customer demographic information based on the store location. For historical reasons, the data also include the logarithm of the sales quantity. The Dominick's grocery data is commonly used to illustrate econometric modeling techniques where logarithms of quantities are generally preferred.    \n",
+    "\n",
+    "The task is now to build a time-series model for the _Quantity_ column. It is important to note that this dataset is comprised of many individual time-series - one for each unique combination of _Store_ and _Brand_. To distinguish the individual time-series, we thus define the **grain** - the columns whose values determine the boundaries between time-series: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grain_column_names = ['Store', 'Brand']\n",
+    "nseries = data.groupby(grain_column_names).ngroups\n",
+    "print('Data contains {0} individual time-series.'.format(nseries))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Splitting\n",
+    "For the purposes of demonstration and later forecast evaluation, we now split the data into a training and a testing set. The test set will contain the final 20 weeks of observed sales for each time-series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ntest_periods = 20\n",
+    "\n",
+    "def split_last_n_by_grain(df, n):\n",
+    "    \"\"\"\n",
+    "    Group df by grain and split on last n rows for each group\n",
+    "    \"\"\"\n",
+    "    df_grouped = (df.sort_values(time_column_name) # Sort by ascending time\n",
+    "                  .groupby(grain_column_names, group_keys=False))\n",
+    "    df_head = df_grouped.apply(lambda dfg: dfg.iloc[:-n])\n",
+    "    df_tail = df_grouped.apply(lambda dfg: dfg.iloc[-n:])\n",
+    "    return df_head, df_tail\n",
+    "\n",
+    "X_train, X_test = split_last_n_by_grain(data, ntest_periods)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Modeling\n",
+    "\n",
+    "For forecasting tasks, AutoML uses pre-processing and estimation steps that are specific to time-series. AutoML will undertake the following pre-processing steps:\n",
+    "* Detect time-series sample frequency (e.g. hourly, daily, weekly) and create new records for absent time points to make the series regular. A regular time series has a well-defined frequency and has a value at every sample point in a contiguous time span \n",
+    "* Impute missing values in the target (via forward-fill) and feature columns (using median column values) \n",
+    "* Create grain-based features to enable fixed effects across different series\n",
+    "* Create time-based features to assist in learning seasonal patterns\n",
+    "* Encode categorical variables to numeric quantities\n",
+    "\n",
+    "AutoML will currently train a single, regression-type model across **all** time-series in a given training set. This allows the model to generalize across related series.\n",
+    "\n",
+    "You are almost ready to start an AutoML training job. We will first need to create a validation set from the existing training set (i.e. for hyper-parameter tuning): "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nvalidation_periods = 20\n",
+    "X_train, X_validate = split_last_n_by_grain(X_train, nvalidation_periods)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We also need to separate the target column from the rest of the DataFrame: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_column_name = 'Quantity'\n",
+    "y_train = X_train.pop(target_column_name).values\n",
+    "y_validate = X_validate.pop(target_column_name).values "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an AutoMLConfig\n",
+    "\n",
+    "The AutoMLConfig object defines the settings and data for an AutoML training job. Here, we set necessary inputs like the task type, the number of AutoML iterations to try, and the training and validation data. \n",
+    "\n",
+    "For forecasting tasks, there are some additional parameters that can be set: the name of the input data column, holding the date/time and the grain column names. A time column is required for forecasting, while the grain is optional. If a grain is not given, the forecaster assumes that the whole dataset is a single time-series. We also pass a list of columns to drop prior to modeling. The _logQuantity_ column is completely correlated with the target quantity, so it must be removed to prevent a target leak. \n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|forecasting|\n",
+    "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
+    "|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
+    "|**X**|Training matrix of features, shape = [n_training_samples, n_features]|\n",
+    "|**y**|Target values, shape = [n_training_samples, ]|\n",
+    "|**X_valid**|Validation matrix of features, shape = [n_validation_samples, n_features]|\n",
+    "|**y_valid**|Target values for validation, shape = [n_validation_samples, ]\n",
+    "|**enable_ensembling**|Allow AutoML to create ensembles of the best performing models\n",
+    "|**debug_log**|Log file path for writing debugging information\n",
+    "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_settings = {\n",
+    "    'time_column_name': time_column_name,\n",
+    "    'grain_column_names': grain_column_names,\n",
+    "    'drop_column_names': ['logQuantity']\n",
+    "}\n",
+    "\n",
+    "automl_config = AutoMLConfig(task='forecasting',\n",
+    "                             debug_log='automl_oj_sales_errors.log',\n",
+    "                             primary_metric='normalized_root_mean_squared_error',\n",
+    "                             iterations=10,\n",
+    "                             X=X_train,\n",
+    "                             y=y_train,\n",
+    "                             X_valid=X_validate,\n",
+    "                             y_valid=y_validate,\n",
+    "                             enable_ensembling=False,\n",
+    "                             path=project_folder,\n",
+    "                             verbosity=logging.INFO,\n",
+    "                            **automl_settings)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the Model\n",
+    "\n",
+    "You can now submit a new training run. For local runs, the execution is synchronous. Depending on the data and number of iterations this operation may take several minutes.\n",
+    "Information from each iteration will be printed to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "Each run within an Experiment stores serialized (i.e. pickled) pipelines from the AutoML iterations. We can now retrieve the pipeline with the best performance on the validation dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_pipeline = local_run.get_output()\n",
+    "fitted_pipeline.steps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Make Predictions from the Best Fitted Model\n",
+    "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. First, we remove the target values from the test set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_test = X_test.pop(target_column_name).values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_test.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To produce predictions on the test set, we need to know the feature values at all dates in the test set. This requirement is somewhat reasonable for the OJ sales data since the features mainly consist of price, which is usually set in advance, and customer demographics which are approximately constant for each store over the 20 week forecast horizon in the testing data. \n",
+    "\n",
+    "The target predictions can be retrieved by calling the `predict` method on the best model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = fitted_pipeline.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate evaluation metrics for the prediction\n",
+    "To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def MAPE(actual, pred):\n",
+    "    \"\"\"\n",
+    "    Calculate mean absolute percentage error.\n",
+    "    Remove NA and values where actual is close to zero\n",
+    "    \"\"\"\n",
+    "    not_na = ~(np.isnan(actual) | np.isnan(pred))\n",
+    "    not_zero = ~np.isclose(actual, 0.0)\n",
+    "    actual_safe = actual[not_na & not_zero]\n",
+    "    pred_safe = pred[not_na & not_zero]\n",
+    "    APE = 100*np.abs((actual_safe - pred_safe)/actual_safe)\n",
+    "    return np.mean(APE)\n",
+    "\n",
+    "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
+    "print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
+    "print('MAPE: %.2f' % MAPE(y_test, y_pred))"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "erwright"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.ipynb

@@ -0,0 +1,381 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Automated Machine Learning: Blacklisting Models, Early Termination, and Handling Missing Data\n",
+    "\n",
+    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for handling missing values in data. We also provide a stopping metric indicating a target for the primary metrics so that AutoML can terminate the run without necessarly going through all the iterations. Finally, if you want to avoid a certain pipeline, we allow you to specify a blacklist of algorithms that AutoML will ignore for this run.\n",
+    "\n",
+    "Make sure you have executed the [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",
+    "4. Train the model.\n",
+    "5. Explore the results.\n",
+    "6. Test the best fitted model.\n",
+    "\n",
+    "In addition this notebook showcases the following features\n",
+    "- **Blacklisting** certain pipelines\n",
+    "- Specifying **target metrics** to indicate stopping criteria\n",
+    "- Handling **missing data** in the input\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose a name for the experiment.\n",
+    "experiment_name = 'automl-local-missing-data'\n",
+    "project_folder = './sample_projects/automl-local-missing-data'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data=output, index=['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating missing data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy import sparse\n",
+    "\n",
+    "digits = datasets.load_digits()\n",
+    "X_train = digits.data[10:,:]\n",
+    "y_train = digits.target[10:]\n",
+    "\n",
+    "# Add missing values in 75% of the lines.\n",
+    "missing_rate = 0.75\n",
+    "n_missing_samples = int(np.floor(X_train.shape[0] * missing_rate))\n",
+    "missing_samples = np.hstack((np.zeros(X_train.shape[0] - n_missing_samples, dtype=np.bool), np.ones(n_missing_samples, dtype=np.bool)))\n",
+    "rng = np.random.RandomState(0)\n",
+    "rng.shuffle(missing_samples)\n",
+    "missing_features = rng.randint(0, X_train.shape[1], n_missing_samples)\n",
+    "X_train[np.where(missing_samples)[0], missing_features] = np.nan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(data = X_train)\n",
+    "df['Label'] = pd.Series(y_train, index=df.index)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.  This includes setting `experiment_exit_score`, which should cause the run to complete before the `iterations` count is reached.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|classification or regression|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**n_cross_validations**|Number of cross validation splits.|\n",
+    "|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.|\n",
+    "|**experiment_exit_score**|*double* value indicating the target for *primary_metric*. <br>Once the target is surpassed the run terminates.|\n",
+    "|**blacklist_models**|*List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run.<br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGD</i><br><i>MultinomialNaiveBayes</i><br><i>BernoulliNaiveBayes</i><br><i>SVM</i><br><i>LinearSVM</i><br><i>KNN</i><br><i>DecisionTree</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>GradientBoosting</i><br><i>TensorFlowDNN</i><br><i>TensorFlowLinearClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i>|\n",
+    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers.|\n",
+    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             primary_metric = 'AUC_weighted',\n",
+    "                             iteration_timeout_minutes = 60,\n",
+    "                             iterations = 20,\n",
+    "                             n_cross_validations = 5,\n",
+    "                             preprocess = True,\n",
+    "                             experiment_exit_score = 0.9984,\n",
+    "                             blacklist_models = ['KNN','LinearSVM'],\n",
+    "                             verbosity = logging.INFO,\n",
+    "                             X = X_train, \n",
+    "                             y = y_train,\n",
+    "                             path = project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
+    "In this example, we specify `show_output = True` to print currently running iterations to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(local_run).show() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(local_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model which has the smallest `accuracy` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lookup_metric = \"accuracy\"\n",
+    "# best_run, fitted_model = local_run.get_output(metric = lookup_metric)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration\n",
+    "Show the run and the model from the third iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# iteration = 3\n",
+    "# best_run, fitted_model = local_run.get_output(iteration = iteration)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing the best Fitted Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:10, :]\n",
+    "y_test = digits.target[:10]\n",
+    "images = digits.images[:10]\n",
+    "\n",
+    "# Randomly select digits and test.\n",
+    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+    "    print(index)\n",
+    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+    "    label = y_test[index]\n",
+    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
+    "    fig = plt.figure(1, figsize=(3,3))\n",
+    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "    ax1.set_title(title)\n",
+    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "    plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -0,0 +1,348 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Automated Machine Learning: Explain classification model and visualize the explanation\n",
+    "\n",
+    "In this example we use the sklearn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) to showcase how you can use the AutoML Classifier for a simple classification problem.\n",
+    "\n",
+    "Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you would see\n",
+    "1. Creating an Experiment in an existing Workspace\n",
+    "2. Instantiating AutoMLConfig\n",
+    "3. Training the Model using local compute and explain the model\n",
+    "4. Visualization model's feature importance in widget\n",
+    "5. Explore best model's explanation\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Experiment\n",
+    "\n",
+    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "import pandas as pd\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# choose a name for experiment\n",
+    "experiment_name = 'automl-local-classification'\n",
+    "# project folder\n",
+    "project_folder = './sample_projects/automl-local-classification-model-explanation'\n",
+    "\n",
+    "experiment=Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Iris Data Set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "\n",
+    "iris = datasets.load_iris()\n",
+    "y = iris.target\n",
+    "X = iris.data\n",
+    "\n",
+    "features = iris.feature_names\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
+    "                                                    y,\n",
+    "                                                    test_size=0.1,\n",
+    "                                                    random_state=100,\n",
+    "                                                    stratify=y)\n",
+    "\n",
+    "X_train = pd.DataFrame(X_train, columns=features)\n",
+    "X_test = pd.DataFrame(X_test, columns=features)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiate Auto ML Config\n",
+    "\n",
+    "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|classification or regression|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
+    "|**max_time_sec**|Time limit in minutes for each iterations|\n",
+    "|**iterations**|Number of iterations. In each iteration Auto ML trains the data with a specific pipeline|\n",
+    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
+    "|**X_valid**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y_valid**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]|\n",
+    "|**model_explainability**|Indicate to explain each trained pipeline or not |\n",
+    "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             primary_metric = 'AUC_weighted',\n",
+    "                             iteration_timeout_minutes = 200,\n",
+    "                             iterations = 10,\n",
+    "                             verbosity = logging.INFO,\n",
+    "                             X = X_train, \n",
+    "                             y = y_train,\n",
+    "                             X_valid = X_test,\n",
+    "                             y_valid = y_test,\n",
+    "                             model_explainability=True,\n",
+    "                             path=project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the Model\n",
+    "\n",
+    "You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
+    "You will see the currently running iterations printing to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exploring the results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Widget for monitoring runs\n",
+    "\n",
+    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(local_run).show() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The *get_output* method on automl_classifier returns the best run and the fitted model for the last *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Best Model 's explanation\n",
+    "\n",
+    "Retrieve the explanation from the best_run. And explanation information includes:\n",
+    "\n",
+    "1.\tshap_values: The explanation information generated by shap lib\n",
+    "2.\texpected_values: The expected value of the model applied to set of X_train data.\n",
+    "3.\toverall_summary: The model level feature importance values sorted in descending order\n",
+    "4.\toverall_imp: The feature names sorted in the same order as in overall_summary\n",
+    "5.\tper_class_summary: The class level feature importance values sorted in descending order. Only available for the classification case\n",
+    "6.\tper_class_imp: The feature names sorted in the same order as in per_class_summary. Only available for the classification case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.train.automl.automlexplainer import retrieve_model_explanation\n",
+    "\n",
+    "shap_values, expected_values, overall_summary, overall_imp, per_class_summary, per_class_imp = \\\n",
+    "    retrieve_model_explanation(best_run)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(overall_summary)\n",
+    "print(overall_imp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(per_class_summary)\n",
+    "print(per_class_imp)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Beside retrieve the existed model explanation information, explain the model with different train/test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.train.automl.automlexplainer import explain_model\n",
+    "\n",
+    "shap_values, expected_values, overall_summary, overall_imp, per_class_summary, per_class_imp = \\\n",
+    "    explain_model(fitted_model, X_train, X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(overall_summary)\n",
+    "print(overall_imp)"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "xif"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

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

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

@@ -0,0 +1,517 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Automated Machine Learning: Remote Execution using attach\n",
+    "\n",
+    "In this example we use the scikit-learn's [20newsgroup](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_20newsgroups.html) to showcase how you can use AutoML to handle text data with remote attach.\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. Attach an existing DSVM to a workspace.\n",
+    "3. Configure AutoML using `AutoMLConfig`.\n",
+    "4. Train the model using the DSVM.\n",
+    "5. Explore the results.\n",
+    "6. Test the best fitted model.\n",
+    "\n",
+    "In addition this notebook showcases the following features\n",
+    "- **Parallel** executions for iterations\n",
+    "- **Asynchronous** tracking of progress\n",
+    "- **Cancellation** of individual iterations or the entire run\n",
+    "- Retrieving models for any iteration or logged metric\n",
+    "- Specifying AutoML settings as `**kwargs`\n",
+    "- Handling **text** data using the `preprocess` flag\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose a name for the run history container in the workspace.\n",
+    "experiment_name = 'automl-remote-dsvm-blobstore'\n",
+    "project_folder = './sample_projects/automl-remote-dsvm-blobstore'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data=output, index=['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Attach a Remote Linux DSVM\n",
+    "To use a remote Docker compute target:\n",
+    "1. Create a Linux DSVM in Azure, following these [quick instructions](https://docs.microsoft.com/en-us/azure/machine-learning/desktop-workbench/how-to-create-dsvm-hdi). Make sure you use the Ubuntu flavor (not CentOS). Make sure that disk space is available under `/tmp` because AutoML creates files under `/tmp/azureml_run`s. The DSVM should have more cores than the number of parallel runs that you plan to enable. It should also have at least 4GB per core.\n",
+    "2. Enter the IP address, user name and password below.\n",
+    "\n",
+    "**Note:** By default, SSH runs on port 22 and you don't need to change the port number below. If you've configured SSH to use a different port, change `dsvm_ssh_port` accordinglyaddress. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on changing SSH ports for security reasons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import ComputeTarget, RemoteCompute\n",
+    "import time\n",
+    "\n",
+    "# Add your VM information below\n",
+    "# If a compute with the specified compute_name already exists, it will be used and the dsvm_ip_addr, dsvm_ssh_port, \n",
+    "# dsvm_username and dsvm_password will be ignored.\n",
+    "compute_name  = 'mydsvmb'\n",
+    "dsvm_ip_addr  = '<<ip_addr>>'\n",
+    "dsvm_ssh_port = 22\n",
+    "dsvm_username = '<<username>>'\n",
+    "dsvm_password = '<<password>>'\n",
+    "\n",
+    "if compute_name in ws.compute_targets:\n",
+    "    print('Using existing compute.')\n",
+    "    dsvm_compute = ws.compute_targets[compute_name]\n",
+    "else:\n",
+    "    attach_config = RemoteCompute.attach_configuration(address=dsvm_ip_addr, username=dsvm_username, password=dsvm_password, ssh_port=dsvm_ssh_port)\n",
+    "    ComputeTarget.attach(workspace=ws, name=compute_name, attach_configuration=attach_config)\n",
+    "\n",
+    "    while ws.compute_targets[compute_name].provisioning_state == 'Creating':\n",
+    "        time.sleep(1)\n",
+    "\n",
+    "    dsvm_compute = ws.compute_targets[compute_name]\n",
+    "    \n",
+    "    if dsvm_compute.provisioning_state == 'Failed':\n",
+    "        print('Attached failed.')\n",
+    "        print(dsvm_compute.provisioning_errors)\n",
+    "        dsvm_compute.detach()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.runconfig import RunConfiguration\n",
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "\n",
+    "# create a new RunConfig object\n",
+    "conda_run_config = RunConfiguration(framework=\"python\")\n",
+    "\n",
+    "# Set compute target to the Linux DSVM\n",
+    "conda_run_config.target = dsvm_compute\n",
+    "\n",
+    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
+    "conda_run_config.environment.python.conda_dependencies = cd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Get Data File\n",
+    "For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
+    "In this example, the `get_data()` function returns a [dictionary](README.md#getdata)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(project_folder):\n",
+    "    os.makedirs(project_folder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile $project_folder/get_data.py\n",
+    "\n",
+    "import numpy as np\n",
+    "from sklearn.datasets import fetch_20newsgroups\n",
+    "\n",
+    "def get_data():\n",
+    "    remove = ('headers', 'footers', 'quotes')\n",
+    "    categories = [\n",
+    "        'alt.atheism',\n",
+    "        'talk.religion.misc',\n",
+    "        'comp.graphics',\n",
+    "        'sci.space',\n",
+    "    ]\n",
+    "    data_train = fetch_20newsgroups(subset = 'train', categories = categories,\n",
+    "                                    shuffle = True, random_state = 42,\n",
+    "                                    remove = remove)\n",
+    "    \n",
+    "    X_train = np.array(data_train.data).reshape((len(data_train.data),1))\n",
+    "    y_train = np.array(data_train.target)\n",
+    "    \n",
+    "    return { \"X\" : X_train, \"y\" : y_train }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
+    "\n",
+    "You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
+    "\n",
+    "**Note:** When using Remote DSVM, you can't pass Numpy arrays directly to the fit method.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**n_cross_validations**|Number of cross validation splits.|\n",
+    "|**max_concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of cores on the DSVM.|\n",
+    "|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.|\n",
+    "|**enable_cache**|Setting this to *True* enables preprocess done once and reuse the same preprocessed data for all the iterations. Default value is True.\n",
+    "|**max_cores_per_iteration**|Indicates how many cores on the compute target would be used to train a single pipeline.<br>Default is *1*; you can set it to *-1* to use all cores.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_settings = {\n",
+    "    \"iteration_timeout_minutes\": 60,\n",
+    "    \"iterations\": 4,\n",
+    "    \"n_cross_validations\": 5,\n",
+    "    \"primary_metric\": 'AUC_weighted',\n",
+    "    \"preprocess\": True,\n",
+    "    \"max_cores_per_iteration\": 2\n",
+    "}\n",
+    "\n",
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             path = project_folder,\n",
+    "                             run_configuration=conda_run_config,\n",
+    "                             data_script = project_folder + \"/get_data.py\",\n",
+    "                             **automl_settings\n",
+    "                            )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remote_run = experiment.submit(automl_config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "You can click on a pipeline to see run properties and output logs.  Logs are also available on the DSVM under `/tmp/azureml_run/{iterationid}/azureml-logs`\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(remote_run).show() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Wait until the run finishes.\n",
+    "remote_run.wait_for_completion(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pre-process cache cleanup\n",
+    "The preprocess data gets cache at user default file store. When the run is completed the cache can be cleaned by running below cell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remote_run.clean_preprocessor_cache()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(remote_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cancelling Runs\n",
+    "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cancel the ongoing experiment and stop scheduling new iterations.\n",
+    "# remote_run.cancel()\n",
+    "\n",
+    "# Cancel iteration 1 and move onto iteration 2.\n",
+    "# remote_run.cancel_iteration(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = remote_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model which has the smallest `accuracy` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lookup_metric = \"accuracy\"\n",
+    "# best_run, fitted_model = remote_run.get_output(metric = lookup_metric)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 0\n",
+    "zero_run, zero_model = remote_run.get_output(iteration = iteration)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load test data.\n",
+    "from pandas_ml import ConfusionMatrix\n",
+    "from sklearn.datasets import fetch_20newsgroups\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_test = fetch_20newsgroups(subset = 'test', categories = categories,\n",
+    "                               shuffle = True, random_state = 42,\n",
+    "                               remove = remove)\n",
+    "\n",
+    "X_test = np.array(data_test.data).reshape((len(data_test.data),1))\n",
+    "y_test = data_test.target\n",
+    "\n",
+    "# Test our best pipeline.\n",
+    "\n",
+    "y_pred = fitted_model.predict(X_test)\n",
+    "y_pred_strings = [data_test.target_names[i] for i in y_pred]\n",
+    "y_test_strings = [data_test.target_names[i] for i in y_test]\n",
+    "\n",
+    "cm = ConfusionMatrix(y_test_strings, y_pred_strings)\n",
+    "print(cm)\n",
+    "cm.plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -0,0 +1,528 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Automated Machine Learning: Remote Execution using Batch AI\n",
+    "\n",
+    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
+    "\n",
+    "Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you would see\n",
+    "1. Create an `Experiment` in an existing `Workspace`.\n",
+    "2. Attach an existing Batch AI compute to a workspace.\n",
+    "3. Configure AutoML using `AutoMLConfig`.\n",
+    "4. Train the model using Batch AI.\n",
+    "5. Explore the results.\n",
+    "6. Test the best fitted model.\n",
+    "\n",
+    "In addition this notebook showcases the following features\n",
+    "- **Parallel** executions for iterations\n",
+    "- **Asynchronous** tracking of progress\n",
+    "- **Cancellation** of individual iterations or the entire run\n",
+    "- Retrieving models for any iteration or logged metric\n",
+    "- Specifying AutoML settings as `**kwargs`\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose a name for the run history container in the workspace.\n",
+    "experiment_name = 'automl-remote-batchai'\n",
+    "project_folder = './sample_projects/automl-remote-batchai'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Batch AI Cluster\n",
+    "The cluster is created as Machine Learning Compute and will appear under your workspace.\n",
+    "\n",
+    "**Note:** The creation of the Batch AI cluster can take over 10 minutes, please be patient.\n",
+    "\n",
+    "As with other Azure services, there are limits on certain resources (e.g. Batch AI cluster size) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import AmlCompute\n",
+    "from azureml.core.compute import ComputeTarget\n",
+    "\n",
+    "# Choose a name for your cluster.\n",
+    "batchai_cluster_name = \"automlcl\"\n",
+    "\n",
+    "found = False\n",
+    "# Check if this compute target already exists in the workspace.\n",
+    "cts = ws.compute_targets\n",
+    "if batchai_cluster_name in cts and cts[batchai_cluster_name].type == 'BatchAI':\n",
+    "    found = True\n",
+    "    print('Found existing compute target.')\n",
+    "    compute_target = cts[batchai_cluster_name]\n",
+    "    \n",
+    "if not found:\n",
+    "    print('Creating a new compute target...')\n",
+    "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
+    "                                                                #vm_priority = 'lowpriority', # optional\n",
+    "                                                                max_nodes = 6)\n",
+    "\n",
+    "    # Create the cluster.\n",
+    "    compute_target = ComputeTarget.create(ws, batchai_cluster_name, provisioning_config)\n",
+    "    \n",
+    "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
+    "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
+    "    compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
+    "    \n",
+    "     # For a more detailed view of current Batch AI cluster status, use the 'status' property."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.runconfig import RunConfiguration\n",
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "\n",
+    "# create a new RunConfig object\n",
+    "conda_run_config = RunConfiguration(framework=\"python\")\n",
+    "\n",
+    "# Set compute target to the Batch AI cluster\n",
+    "conda_run_config.target = compute_target\n",
+    "conda_run_config.environment.docker.enabled = True\n",
+    "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
+    "\n",
+    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
+    "conda_run_config.environment.python.conda_dependencies = cd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Get Data File\n",
+    "For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
+    "In this example, the `get_data()` function returns data using scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(project_folder):\n",
+    "    os.makedirs(project_folder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile $project_folder/get_data.py\n",
+    "\n",
+    "from sklearn import datasets\n",
+    "from scipy import sparse\n",
+    "import numpy as np\n",
+    "\n",
+    "def get_data():\n",
+    "    \n",
+    "    digits = datasets.load_digits()\n",
+    "    X_train = digits.data\n",
+    "    y_train = digits.target\n",
+    "\n",
+    "    return { \"X\" : X_train, \"y\" : y_train }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
+    "\n",
+    "You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
+    "\n",
+    "**Note:** When using Batch AI, you can't pass Numpy arrays directly to the fit method.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**n_cross_validations**|Number of cross validation splits.|\n",
+    "|**max_concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of cores on the DSVM.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_settings = {\n",
+    "    \"iteration_timeout_minutes\": 2,\n",
+    "    \"iterations\": 20,\n",
+    "    \"n_cross_validations\": 5,\n",
+    "    \"primary_metric\": 'AUC_weighted',\n",
+    "    \"preprocess\": False,\n",
+    "    \"max_concurrent_iterations\": 5,\n",
+    "    \"verbosity\": logging.INFO\n",
+    "}\n",
+    "\n",
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             path = project_folder,\n",
+    "                             run_configuration=conda_run_config,\n",
+    "                             data_script = project_folder + \"/get_data.py\",\n",
+    "                             **automl_settings\n",
+    "                            )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run.\n",
+    "In this example, we specify `show_output = False` to suppress console output while the run is in progress."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remote_run = experiment.submit(automl_config, show_output = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results\n",
+    "\n",
+    "#### Loading executed runs\n",
+    "In case you need to load a previously executed run, enable the cell below and replace the `run_id` value."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "remote_run = AutoMLRun(experiment = experiment, run_id = 'AutoML_5db13491-c92a-4f1d-b622-8ab8d973a058')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "You can click on a pipeline to see run properties and output logs.  Logs are also available on the DSVM under `/tmp/azureml_run/{iterationid}/azureml-logs`\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remote_run"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(remote_run).show() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Wait until the run finishes.\n",
+    "remote_run.wait_for_completion(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(remote_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cancelling Runs\n",
+    "\n",
+    "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cancel the ongoing experiment and stop scheduling new iterations.\n",
+    "# remote_run.cancel()\n",
+    "\n",
+    "# Cancel iteration 1 and move onto iteration 2.\n",
+    "# remote_run.cancel_iteration(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = remote_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model which has the smallest `log_loss` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lookup_metric = \"log_loss\"\n",
+    "best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration\n",
+    "Show the run and the model from the third iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 3\n",
+    "third_run, third_model = remote_run.get_output(iteration=iteration)\n",
+    "print(third_run)\n",
+    "print(third_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n",
+    "\n",
+    "#### Load Test Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:10, :]\n",
+    "y_test = digits.target[:10]\n",
+    "images = digits.images[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Testing Our Best Fitted Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Randomly select digits and test.\n",
+    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+    "    print(index)\n",
+    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+    "    label = y_test[index]\n",
+    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
+    "    fig = plt.figure(1, figsize=(3,3))\n",
+    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "    ax1.set_title(title)\n",
+    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "    plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "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/remote-execution-with-datastore/auto-ml-remote-execution-with-datastore.ipynb

@@ -0,0 +1,583 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Automated Machine Learning: Remote Execution with DataStore\n",
+    "\n",
+    "This sample accesses a data file on a remote DSVM through DataStore. Advantages of using data store are:\n",
+    "1. DataStore secures the access details.\n",
+    "2. DataStore supports read, write to blob and file store\n",
+    "3. AutoML natively supports copying data from DataStore to DSVM\n",
+    "\n",
+    "Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you would see\n",
+    "1. Storing data in DataStore.\n",
+    "2. get_data returning data from DataStore.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Experiment\n",
+    "\n",
+    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "import time\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.compute import DsvmCompute\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# choose a name for experiment\n",
+    "experiment_name = 'automl-remote-datastore-file'\n",
+    "# project folder\n",
+    "project_folder = './sample_projects/automl-remote-dsvm-file'\n",
+    "\n",
+    "experiment=Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data=output, index=['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a Remote Linux DSVM\n",
+    "Note: If creation fails with a message about Marketplace purchase eligibilty, go to portal.azure.com, start creating DSVM there, and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled it, you can exit without actually creating VM.\n",
+    "\n",
+    "**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you can switch to a different port (such as 5022), you can append the port number to the address. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "compute_target_name = 'mydsvmc'\n",
+    "\n",
+    "try:\n",
+    "    while ws.compute_targets[compute_target_name].provisioning_state == 'Creating':\n",
+    "        time.sleep(1)\n",
+    "        \n",
+    "    dsvm_compute = DsvmCompute(workspace=ws, name=compute_target_name)\n",
+    "    print('found existing:', dsvm_compute.name)\n",
+    "except:\n",
+    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size=\"Standard_D2_v2\")\n",
+    "    dsvm_compute = DsvmCompute.create(ws, name=compute_target_name, provisioning_configuration=dsvm_config)\n",
+    "    dsvm_compute.wait_for_completion(show_output=True)\n",
+    "    print(\"Waiting one minute for ssh to be accessible\")\n",
+    "    time.sleep(60) # Wait for ssh to be accessible"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Copy data file to local\n",
+    "\n",
+    "Download the data file.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mkdir data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_20newsgroups\n",
+    "import csv\n",
+    "\n",
+    "remove = ('headers', 'footers', 'quotes')\n",
+    "categories = [\n",
+    "        'alt.atheism',\n",
+    "        'talk.religion.misc',\n",
+    "        'comp.graphics',\n",
+    "        'sci.space',\n",
+    "    ]\n",
+    "data_train = fetch_20newsgroups(subset = 'train', categories = categories,\n",
+    "                                    shuffle = True, random_state = 42,\n",
+    "                                    remove = remove)\n",
+    "    \n",
+    "pd.DataFrame(data_train.data).to_csv(\"data/X_train.tsv\", index=False, header=False, quoting=csv.QUOTE_ALL, sep=\"\\t\")\n",
+    "pd.DataFrame(data_train.target).to_csv(\"data/y_train.tsv\", index=False, header=False, sep=\"\\t\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Upload data to the cloud"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now make the data accessible remotely by uploading that data from your local machine into Azure so it can be accessed for remote training. The datastore is a convenient construct associated with your workspace for you to upload/download data, and interact with it from your remote compute targets. It is backed by Azure blob storage account.\n",
+    "\n",
+    "The data.tsv files are uploaded into a directory named data at the root of the datastore."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace, Datastore\n",
+    "#blob_datastore = Datastore(ws, blob_datastore_name)\n",
+    "ds = ws.get_default_datastore()\n",
+    "print(ds.datastore_type, ds.account_name, ds.container_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ds.upload_files(\"data.tsv\")\n",
+    "ds.upload(src_dir='./data', target_path='data', overwrite=True, show_progress=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure & Run\n",
+    "\n",
+    "First let's create a DataReferenceConfigruation object to inform the system what data folder to download to the compute target.\n",
+    "The path_on_compute should be an absolute path to ensure that the data files are downloaded only once.   The get_data method should use this same path to access the data files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.runconfig import DataReferenceConfiguration\n",
+    "dr = DataReferenceConfiguration(datastore_name=ds.name, \n",
+    "                   path_on_datastore='data', \n",
+    "                   path_on_compute='/tmp/azureml_runs',\n",
+    "                   mode='download', # download files from datastore to compute target\n",
+    "                   overwrite=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.runconfig import RunConfiguration\n",
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "\n",
+    "# create a new RunConfig object\n",
+    "conda_run_config = RunConfiguration(framework=\"python\")\n",
+    "\n",
+    "# Set compute target to the Linux DSVM\n",
+    "conda_run_config.target = dsvm_compute\n",
+    "# set the data reference of the run coonfiguration\n",
+    "conda_run_config.data_references = {ds.name: dr}\n",
+    "\n",
+    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
+    "conda_run_config.environment.python.conda_dependencies = cd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Get Data File\n",
+    "For remote executions you should author a get_data.py file containing a get_data() function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
+    "\n",
+    "The *get_data()* function returns a [dictionary](README.md#getdata).\n",
+    "\n",
+    "The read_csv uses the path_on_compute value specified in the DataReferenceConfiguration call plus the path_on_datastore folder and then the actual file name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(project_folder):\n",
+    "    os.makedirs(project_folder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile $project_folder/get_data.py\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "def get_data():\n",
+    "    X_train = pd.read_csv(\"/tmp/azureml_runs/data/X_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
+    "    y_train = pd.read_csv(\"/tmp/azureml_runs/data/y_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
+    "\n",
+    "    return { \"X\" : X_train.values, \"y\" : y_train[0].values }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
+    "\n",
+    "You can specify automl_settings as **kwargs** as well. Also note that you can use the get_data() symantic for local excutions too. \n",
+    "\n",
+    "<i>Note: For Remote DSVM and Batch AI you cannot pass Numpy arrays directly to AutoMLConfig.</i>\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration|\n",
+    "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data|\n",
+    "|**n_cross_validations**|Number of cross validation splits|\n",
+    "|**max_concurrent_iterations**|Max number of iterations that would be executed in parallel.  This should be less than the number of cores on the DSVM\n",
+    "|**preprocess**| *True/False* <br>Setting this to *True* enables Auto ML to perform preprocessing <br>on the input to handle *missing data*, and perform some common *feature extraction*|\n",
+    "|**enable_cache**|Setting this to *True* enables preprocess done once and reuse the same preprocessed data for all the iterations. Default value is True.|\n",
+    "|**max_cores_per_iteration**| Indicates how many cores on the compute target would be used to train a single pipeline.<br> Default is *1*, you can set it to *-1* to use all cores|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_settings = {\n",
+    "    \"iteration_timeout_minutes\": 60,\n",
+    "    \"iterations\": 4,\n",
+    "    \"n_cross_validations\": 5,\n",
+    "    \"primary_metric\": 'AUC_weighted',\n",
+    "    \"preprocess\": True,\n",
+    "    \"max_cores_per_iteration\": 1,\n",
+    "    \"verbosity\": logging.INFO\n",
+    "}\n",
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             path=project_folder,\n",
+    "                             run_configuration=conda_run_config,\n",
+    "                             #compute_target = dsvm_compute,\n",
+    "                             data_script = project_folder + \"/get_data.py\",\n",
+    "                             **automl_settings\n",
+    "                            )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the Models <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
+    "\n",
+    "For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets/models even when the experiment is running to retreive the best model up to that point. Once you are satisfied with the model you can cancel a particular iteration or the whole run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remote_run = experiment.submit(automl_config, show_output=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
+    "#### Widget for monitoring runs\n",
+    "\n",
+    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "You can click on a pipeline to see run properties and output logs. Logs are also available on the DSVM under /tmp/azureml_run/{iterationid}/azureml-logs\n",
+    "\n",
+    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(remote_run).show() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Wait until the run finishes.\n",
+    "remote_run.wait_for_completion(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use sdk methods to fetch all the child runs and see individual metrics that we log. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(remote_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Canceling Runs\n",
+    "You can cancel ongoing remote runs using the *cancel()* and *cancel_iteration()* functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cancel the ongoing experiment and stop scheduling new iterations\n",
+    "# remote_run.cancel()\n",
+    "\n",
+    "# Cancel iteration 1 and move onto iteration 2\n",
+    "# remote_run.cancel_iteration(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pre-process cache cleanup\n",
+    "The preprocess data gets cache at user default file store. When the run is completed the cache can be cleaned by running below cell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remote_run.clean_preprocessor_cache()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The *get_output* method returns the best run and the fitted model. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = remote_run.get_output()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model based on any other metric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lookup_metric = \"accuracy\"\n",
+    "# best_run, fitted_model = remote_run.get_output(metric=lookup_metric)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a specific iteration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# iteration = 1\n",
+    "# best_run, fitted_model = remote_run.get_output(iteration=iteration)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing the Best Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load test data.\n",
+    "from pandas_ml import ConfusionMatrix\n",
+    "\n",
+    "data_test = fetch_20newsgroups(subset = 'test', categories = categories,\n",
+    "                               shuffle = True, random_state = 42,\n",
+    "                               remove = remove)\n",
+    "\n",
+    "X_test = np.array(data_test.data).reshape((len(data_test.data),1))\n",
+    "y_test = data_test.target\n",
+    "\n",
+    "# Test our best pipeline.\n",
+    "\n",
+    "y_pred = fitted_model.predict(X_test)\n",
+    "y_pred_strings = [data_test.target_names[i] for i in y_pred]\n",
+    "y_test_strings = [data_test.target_names[i] for i in y_test]\n",
+    "\n",
+    "cm = ConfusionMatrix(y_test_strings, y_pred_strings)\n",
+    "print(cm)\n",
+    "cm.plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -0,0 +1,507 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Automated Machine Learning: Remote Execution using DSVM (Ubuntu)\n",
+    "\n",
+    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
+    "\n",
+    "Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you wiil learn how to:\n",
+    "1. Create an `Experiment` in an existing `Workspace`.\n",
+    "2. Attach an existing DSVM to a workspace.\n",
+    "3. Configure AutoML using `AutoMLConfig`.\n",
+    "4. Train the model using the DSVM.\n",
+    "5. Explore the results.\n",
+    "6. Test the best fitted model.\n",
+    "\n",
+    "In addition, this notebook showcases the following features:\n",
+    "- **Parallel** executions for iterations\n",
+    "- **Asynchronous** tracking of progress\n",
+    "- **Cancellation** of individual iterations or the entire run\n",
+    "- Retrieving models for any iteration or logged metric\n",
+    "- Specifying AutoML settings as `**kwargs`\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "import time\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose a name for the run history container in the workspace.\n",
+    "experiment_name = 'automl-remote-dsvm4'\n",
+    "project_folder = './sample_projects/automl-remote-dsvm4'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a Remote Linux DSVM\n",
+    "**Note:** If creation fails with a message about Marketplace purchase eligibilty, start creation of a DSVM through the [Azure portal](https://portal.azure.com), and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled this setting, you can exit the portal without actually creating the DSVM, and creation of the DSVM through the notebook should work.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import DsvmCompute\n",
+    "\n",
+    "dsvm_name = 'mydsvma'\n",
+    "try:\n",
+    "    dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
+    "    print('Found an existing DSVM.')\n",
+    "except:\n",
+    "    print('Creating a new DSVM.')\n",
+    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
+    "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
+    "    dsvm_compute.wait_for_completion(show_output = True)\n",
+    "    print(\"Waiting one minute for ssh to be accessible\")\n",
+    "    time.sleep(60) # Wait for ssh to be accessible"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.runconfig import RunConfiguration\n",
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "\n",
+    "# create a new RunConfig object\n",
+    "conda_run_config = RunConfiguration(framework=\"python\")\n",
+    "\n",
+    "# Set compute target to the Linux DSVM\n",
+    "conda_run_config.target = dsvm_compute\n",
+    "\n",
+    "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
+    "conda_run_config.environment.python.conda_dependencies = cd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Get Data File\n",
+    "For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
+    "In this example, the `get_data()` function returns data using scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(project_folder):\n",
+    "    os.makedirs(project_folder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile $project_folder/get_data.py\n",
+    "\n",
+    "from sklearn import datasets\n",
+    "from scipy import sparse\n",
+    "import numpy as np\n",
+    "\n",
+    "def get_data():\n",
+    "    \n",
+    "    digits = datasets.load_digits()\n",
+    "    X_train = digits.data[100:,:]\n",
+    "    y_train = digits.target[100:]\n",
+    "\n",
+    "    return { \"X\" : X_train, \"y\" : y_train }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML <a class=\"anchor\" id=\"Instantiate-AutoML-Remote-DSVM\"></a>\n",
+    "\n",
+    "You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
+    "\n",
+    "**Note:** When using Remote DSVM, you can't pass Numpy arrays directly to the fit method.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**n_cross_validations**|Number of cross validation splits.|\n",
+    "|**max_concurrent_iterations**|Maximum number of iterations to execute in parallel. This should be less than the number of cores on the DSVM.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_settings = {\n",
+    "    \"iteration_timeout_minutes\": 10,\n",
+    "    \"iterations\": 20,\n",
+    "    \"n_cross_validations\": 5,\n",
+    "    \"primary_metric\": 'AUC_weighted',\n",
+    "    \"preprocess\": False,\n",
+    "    \"max_concurrent_iterations\": 2,\n",
+    "    \"verbosity\": logging.INFO\n",
+    "}\n",
+    "\n",
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             path = project_folder, \n",
+    "                             run_configuration=conda_run_config,\n",
+    "                             data_script = project_folder + \"/get_data.py\",\n",
+    "                             **automl_settings\n",
+    "                            )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note:** The first run on a new DSVM may take several minutes to prepare the environment."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run.\n",
+    "\n",
+    "In this example, we specify `show_output = False` to suppress console output while the run is in progress."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remote_run = experiment.submit(automl_config, show_output = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results\n",
+    "\n",
+    "#### Loading Executed Runs\n",
+    "In case you need to load a previously executed run, enable the cell below and replace the `run_id` value."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "remote_run = AutoMLRun(experiment=experiment, run_id = 'AutoML_480d3ed6-fc94-44aa-8f4e-0b945db9d3ef')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "You can click on a pipeline to see run properties and output logs.  Logs are also available on the DSVM under `/tmp/azureml_run/{iterationid}/azureml-logs`\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(remote_run).show() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Wait until the run finishes.\n",
+    "remote_run.wait_for_completion(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(remote_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cancelling Runs\n",
+    "\n",
+    "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cancel the ongoing experiment and stop scheduling new iterations.\n",
+    "# remote_run.cancel()\n",
+    "\n",
+    "# Cancel iteration 1 and move onto iteration 2.\n",
+    "# remote_run.cancel_iteration(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = remote_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model which has the smallest `log_loss` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lookup_metric = \"log_loss\"\n",
+    "best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration\n",
+    "Show the run and the model from the third iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iteration = 3\n",
+    "third_run, third_model = remote_run.get_output(iteration = iteration)\n",
+    "print(third_run)\n",
+    "print(third_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test the Best Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n",
+    "\n",
+    "#### Load Test Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:10, :]\n",
+    "y_test = digits.target[:10]\n",
+    "images = digits.images[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Test Our Best Fitted Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Randomly select digits and test.\n",
+    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+    "    print(index)\n",
+    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+    "    label = y_test[index]\n",
+    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
+    "    fig = plt.figure(1, figsize=(3,3))\n",
+    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "    ax1.set_title(title)\n",
+    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "    plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -0,0 +1,251 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Automated Machine Learning: Sample Weight\n",
+    "\n",
+    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use sample weight with AutoML. Sample weight is used where some sample values are more important than others.\n",
+    "\n",
+    "Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
+    "\n",
+    "In this notebook you will learn how to configure AutoML to use `sample_weight` and you will see the difference sample weight makes to the test results.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# Choose names for the regular and the sample weight experiments.\n",
+    "experiment_name = 'non_sample_weight_experiment'\n",
+    "sample_weight_experiment_name = 'sample_weight_experiment'\n",
+    "\n",
+    "project_folder = './sample_projects/automl-local-classification'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "sample_weight_experiment=Experiment(ws, sample_weight_experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "Instantiate two `AutoMLConfig` objects. One will be used with `sample_weight` and one without."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_train = digits.data[100:,:]\n",
+    "y_train = digits.target[100:]\n",
+    "\n",
+    "# The example makes the sample weight 0.9 for the digit 4 and 0.1 for all other digits.\n",
+    "# This makes the model more likely to classify as 4 if the image it not clear.\n",
+    "sample_weight = np.array([(0.9 if x == 4 else 0.01) for x in y_train])\n",
+    "\n",
+    "automl_classifier = AutoMLConfig(task = 'classification',\n",
+    "                                 debug_log = 'automl_errors.log',\n",
+    "                                 primary_metric = 'AUC_weighted',\n",
+    "                                 iteration_timeout_minutes = 60,\n",
+    "                                 iterations = 10,\n",
+    "                                 n_cross_validations = 2,\n",
+    "                                 verbosity = logging.INFO,\n",
+    "                                 X = X_train, \n",
+    "                                 y = y_train,\n",
+    "                                 path = project_folder)\n",
+    "\n",
+    "automl_sample_weight = AutoMLConfig(task = 'classification',\n",
+    "                                    debug_log = 'automl_errors.log',\n",
+    "                                    primary_metric = 'AUC_weighted',\n",
+    "                                    iteration_timeout_minutes = 60,\n",
+    "                                    iterations = 10,\n",
+    "                                    n_cross_validations = 2,\n",
+    "                                    verbosity = logging.INFO,\n",
+    "                                    X = X_train, \n",
+    "                                    y = y_train,\n",
+    "                                    sample_weight = sample_weight,\n",
+    "                                    path = project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models\n",
+    "\n",
+    "Call the `submit` method on the experiment objects and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
+    "In this example, we specify `show_output = True` to print currently running iterations to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_classifier, show_output = True)\n",
+    "sample_weight_run = sample_weight_experiment.submit(automl_sample_weight, show_output = True)\n",
+    "\n",
+    "best_run, fitted_model = local_run.get_output()\n",
+    "best_run_sample_weight, fitted_model_sample_weight = sample_weight_run.get_output()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test the Best Fitted Model\n",
+    "\n",
+    "#### Load Test Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:100, :]\n",
+    "y_test = digits.target[:100]\n",
+    "images = digits.images[:100]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Compare the Models\n",
+    "The prediction from the sample weight model is more likely to correctly predict 4's.  However, it is also more likely to predict 4 for some images that are not labelled as 4."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Randomly select digits and test.\n",
+    "for index in range(0,len(y_test)):\n",
+    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+    "    predicted_sample_weight = fitted_model_sample_weight.predict(X_test[index:index + 1])[0]\n",
+    "    label = y_test[index]\n",
+    "    if predicted == 4 or predicted_sample_weight == 4 or label == 4:\n",
+    "        title = \"Label value = %d  Predicted value = %d Prediced with sample weight = %d\" % (label, predicted, predicted_sample_weight)\n",
+    "        fig = plt.figure(1, figsize=(3,3))\n",
+    "        ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "        ax1.set_title(title)\n",
+    "        plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "        plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

how-to-use-azureml/automated-machine-learning/sparse-data-train-test-split/auto-ml-sparse-data-train-test-split.ipynb

@@ -0,0 +1,384 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Automated Machine Learning: Train Test Split and Handling Sparse Data\n",
+    "\n",
+    "In this example we use the scikit-learn's [20newsgroup](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_20newsgroups.html) to showcase how you can use AutoML for handling sparse data and how to specify custom cross validations splits.\n",
+    "\n",
+    "Make sure you have executed the [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",
+    "4. Train the model.\n",
+    "5. Explore the results.\n",
+    "6. Test the best fitted model.\n",
+    "\n",
+    "In addition this notebook showcases the following features\n",
+    "- Explicit train test splits \n",
+    "- Handling **sparse data** in the input"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# choose a name for the experiment\n",
+    "experiment_name = 'automl-local-missing-data'\n",
+    "# project folder\n",
+    "project_folder = './sample_projects/automl-local-missing-data'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data=output, index=['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating Sparse Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_20newsgroups\n",
+    "from sklearn.feature_extraction.text import HashingVectorizer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "remove = ('headers', 'footers', 'quotes')\n",
+    "categories = [\n",
+    "    'alt.atheism',\n",
+    "    'talk.religion.misc',\n",
+    "    'comp.graphics',\n",
+    "    'sci.space',\n",
+    "]\n",
+    "data_train = fetch_20newsgroups(subset = 'train', categories = categories,\n",
+    "                                shuffle = True, random_state = 42,\n",
+    "                                remove = remove)\n",
+    "\n",
+    "X_train, X_valid, y_train, y_valid = train_test_split(data_train.data, data_train.target, test_size = 0.33, random_state = 42)\n",
+    "\n",
+    "\n",
+    "vectorizer = HashingVectorizer(stop_words = 'english', alternate_sign = False,\n",
+    "                               n_features = 2**16)\n",
+    "X_train = vectorizer.transform(X_train)\n",
+    "X_valid = vectorizer.transform(X_valid)\n",
+    "\n",
+    "summary_df = pd.DataFrame(index = ['No of Samples', 'No of Features'])\n",
+    "summary_df['Train Set'] = [X_train.shape[0], X_train.shape[1]]\n",
+    "summary_df['Validation Set'] = [X_valid.shape[0], X_valid.shape[1]]\n",
+    "summary_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|classification or regression|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.<br>**Note:** If input data is sparse, you cannot use *True*.|\n",
+    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
+    "|**X_valid**|(sparse) array-like, shape = [n_samples, n_features] for the custom validation set.|\n",
+    "|**y_valid**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification for the custom validation set.|\n",
+    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             primary_metric = 'AUC_weighted',\n",
+    "                             iteration_timeout_minutes = 60,\n",
+    "                             iterations = 5,\n",
+    "                             preprocess = False,\n",
+    "                             verbosity = logging.INFO,\n",
+    "                             X = X_train, \n",
+    "                             y = y_train,\n",
+    "                             X_valid = X_valid, \n",
+    "                             y_valid = y_valid, \n",
+    "                             path = project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
+    "In this example, we specify `show_output = True` to print currently running iterations to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Widget for Monitoring Runs\n",
+    "\n",
+    "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+    "\n",
+    "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(local_run).show() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### Retrieve All Child Runs\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(local_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "    \n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model\n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric\n",
+    "Show the run and the model which has the smallest `accuracy` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lookup_metric = \"accuracy\"\n",
+    "# best_run, fitted_model = local_run.get_output(metric = lookup_metric)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model from a Specific Iteration\n",
+    "Show the run and the model from the third iteration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# iteration = 3\n",
+    "# best_run, fitted_model = local_run.get_output(iteration = iteration)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing the Best Fitted Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load test data.\n",
+    "from pandas_ml import ConfusionMatrix\n",
+    "\n",
+    "data_test = fetch_20newsgroups(subset = 'test', categories = categories,\n",
+    "                               shuffle = True, random_state = 42,\n",
+    "                               remove = remove)\n",
+    "\n",
+    "X_test = vectorizer.transform(data_test.data)\n",
+    "y_test = data_test.target\n",
+    "\n",
+    "# Test our best pipeline.\n",
+    "\n",
+    "y_pred = fitted_model.predict(X_test)\n",
+    "y_pred_strings = [data_test.target_names[i] for i in y_pred]\n",
+    "y_test_strings = [data_test.target_names[i] for i in y_test]\n",
+    "\n",
+    "cm = ConfusionMatrix(y_test_strings, y_pred_strings)\n",
+    "print(cm)\n",
+    "cm.plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

how-to-use-azureml/azure-databricks/01.Installation_and_Configuration.ipynb

@@ -0,0 +1,264 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
+    "\n",
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package. You can select the option to attach the library to all clusters or just one cluster.\n",
+    "\n",
+    "**azureml-sdk**\n",
+    "* Source: Upload Python Egg or PyPi\n",
+    "* PyPi Name: `azureml-sdk[databricks]`\n",
+    "* Select Install Library"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import azureml.core\n",
+    "\n",
+    "# Check core SDK version number - based on build number of preview/master.\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![04ACI](files/tables/image2b.JPG)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Please specify the Azure subscription Id, resource group name, workspace name, and the region in which you want to create the Azure Machine Learning Workspace.\n",
+    "\n",
+    "You can get the value of your Azure subscription ID from the Azure Portal, and then selecting Subscriptions from the menu on the left.\n",
+    "\n",
+    "For the resource_group, use the name of the resource group that contains your Azure Databricks Workspace.\n",
+    "\n",
+    "NOTE: If you provide a resource group name that does not exist, the resource group will be automatically created. This may or may not succeed in your environment, depending on the permissions you have on your Azure Subscription."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# subscription_id = \"<your-subscription-id>\"\n",
+    "# resource_group = \"<your-existing-resource-group>\"\n",
+    "# workspace_name = \"<a-new-or-existing-workspace; it is unrelated to Databricks workspace>\"\n",
+    "# workspace_region = \"<your-resource group-region>\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##TESTONLY\n",
+    "# import auth creds from notebook parameters\n",
+    "tenant = dbutils.widgets.get('tenant_id')\n",
+    "username = dbutils.widgets.get('service_principal_id')\n",
+    "password = dbutils.widgets.get('service_principal_password')\n",
+    "\n",
+    "auth = azureml.core.authentication.ServicePrincipalAuthentication(tenant, username, password)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##TESTONLY\n",
+    "subscription_id  = dbutils.widgets.get('subscription_id')\n",
+    "resource_group = dbutils.widgets.get('resource_group')\n",
+    "workspace_name = dbutils.widgets.get('workspace_name')\n",
+    "workspace_region = dbutils.widgets.get('workspace_region')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##TESTONLY\n",
+    "# import the Workspace class and check the azureml SDK version\n",
+    "# exist_ok checks if workspace exists or not.\n",
+    "\n",
+    "from azureml.core import Workspace\n",
+    "\n",
+    "ws = Workspace.create(name = workspace_name,\n",
+    "                      subscription_id = subscription_id,\n",
+    "                      resource_group = resource_group, \n",
+    "                      location = workspace_region,\n",
+    "                      auth = auth,\n",
+    "                      exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##PUBLISHONLY\n",
+    "## import the Workspace class and check the azureml SDK version\n",
+    "## exist_ok checks if workspace exists or not.\n",
+    "#\n",
+    "#from azureml.core import Workspace\n",
+    "#\n",
+    "#ws = Workspace.create(name = workspace_name,\n",
+    "#                      subscription_id = subscription_id,\n",
+    "#                      resource_group = resource_group, \n",
+    "#                      location = workspace_region,\n",
+    "#                      exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#get workspace details\n",
+    "ws.get_details()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##TESTONLY\n",
+    "ws = Workspace(workspace_name = workspace_name,\n",
+    "               subscription_id = subscription_id,\n",
+    "               resource_group = resource_group,\n",
+    "               auth = auth)\n",
+    "\n",
+    "# persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
+    "ws.write_config()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##PUBLISHONLY\n",
+    "#ws = Workspace(workspace_name = workspace_name,\n",
+    "#               subscription_id = subscription_id,\n",
+    "#               resource_group = resource_group)\n",
+    "#\n",
+    "## persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
+    "#ws.write_config()\n",
+    "###if you need to give a different path/filename please use this\n",
+    "###write_config(path=\"/databricks/driver/aml_config/\",file_name=<alias_conf.cfg>)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "help(Workspace)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##TESTONLY\n",
+    "# import the Workspace class and check the azureml SDK version\n",
+    "from azureml.core import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config(auth = auth)\n",
+    "#ws = Workspace.from_config(<full path>)\n",
+    "print('Workspace name: ' + ws.name, \n",
+    "      'Azure region: ' + ws.location, \n",
+    "      'Subscription id: ' + ws.subscription_id, \n",
+    "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##PUBLISHONLY\n",
+    "## import the Workspace class and check the azureml SDK version\n",
+    "#from azureml.core import Workspace\n",
+    "#\n",
+    "#ws = Workspace.from_config()\n",
+    "##ws = Workspace.from_config(<full path>)\n",
+    "#print('Workspace name: ' + ws.name, \n",
+    "#      'Azure region: ' + ws.location, \n",
+    "#      'Subscription id: ' + ws.subscription_id, \n",
+    "#      'Resource group: ' + ws.resource_group, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "pasha"
+   },
+   {
+    "name": "wamartin"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "name": "01.Installation_and_Configuration",
+  "notebookId": 3836944406456490
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

how-to-use-azureml/azure-databricks/02.Ingest_data.ipynb

@@ -0,0 +1,182 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
+    "\n",
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![04ACI](files/tables/image1.JPG)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Data Ingestion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import urllib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Download AdultCensusIncome.csv from Azure CDN. This file has 32,561 rows.\n",
+    "basedataurl = \"https://amldockerdatasets.azureedge.net\"\n",
+    "datafile = \"AdultCensusIncome.csv\"\n",
+    "datafile_dbfs = os.path.join(\"/dbfs\", datafile)\n",
+    "\n",
+    "if os.path.isfile(datafile_dbfs):\n",
+    "    print(\"found {} at {}\".format(datafile, datafile_dbfs))\n",
+    "else:\n",
+    "    print(\"downloading {} to {}\".format(datafile, datafile_dbfs))\n",
+    "    urllib.request.urlretrieve(os.path.join(basedataurl, datafile), datafile_dbfs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a Spark dataframe out of the csv file.\n",
+    "data_all = sqlContext.read.format('csv').options(header='true', inferSchema='true', ignoreLeadingWhiteSpace='true', ignoreTrailingWhiteSpace='true').load(datafile)\n",
+    "print(\"({}, {})\".format(data_all.count(), len(data_all.columns)))\n",
+    "data_all.printSchema()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#renaming columns\n",
+    "columns_new = [col.replace(\"-\", \"_\") for col in data_all.columns]\n",
+    "data_all = data_all.toDF(*columns_new)\n",
+    "data_all.printSchema()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display(data_all.limit(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Data Preparation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Choose feature columns and the label column.\n",
+    "label = \"income\"\n",
+    "xvars = set(data_all.columns) - {label}\n",
+    "\n",
+    "print(\"label = {}\".format(label))\n",
+    "print(\"features = {}\".format(xvars))\n",
+    "\n",
+    "data = data_all.select([*xvars, label])\n",
+    "\n",
+    "# Split data into train and test.\n",
+    "train, test = data.randomSplit([0.75, 0.25], seed=123)\n",
+    "\n",
+    "print(\"train ({}, {})\".format(train.count(), len(train.columns)))\n",
+    "print(\"test ({}, {})\".format(test.count(), len(test.columns)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Data Persistence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Write the train and test data sets to intermediate storage\n",
+    "train_data_path = \"AdultCensusIncomeTrain\"\n",
+    "test_data_path = \"AdultCensusIncomeTest\"\n",
+    "\n",
+    "train_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTrain\")\n",
+    "test_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTest\")\n",
+    "\n",
+    "train.write.mode('overwrite').parquet(train_data_path)\n",
+    "test.write.mode('overwrite').parquet(test_data_path)\n",
+    "print(\"train and test datasets saved to {} and {}\".format(train_data_path_dbfs, test_data_path_dbfs))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "pasha"
+   },
+   {
+    "name": "wamartin"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "name": "02.Ingest_data",
+  "notebookId": 3836944406456362
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

how-to-use-azureml/azure-databricks/03.Build_model_runHistory.ipynb

@@ -0,0 +1,396 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
+    "\n",
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![04ACI](files/tables/image2.JPG)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Model Building"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pprint\n",
+    "import numpy as np\n",
+    "\n",
+    "from pyspark.ml import Pipeline, PipelineModel\n",
+    "from pyspark.ml.feature import OneHotEncoder, StringIndexer, VectorAssembler\n",
+    "from pyspark.ml.classification import LogisticRegression\n",
+    "from pyspark.ml.evaluation import BinaryClassificationEvaluator\n",
+    "from pyspark.ml.tuning import CrossValidator, ParamGridBuilder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import azureml.core\n",
+    "\n",
+    "# Check core SDK version number\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##TESTONLY\n",
+    "# import auth creds from notebook parameters\n",
+    "tenant = dbutils.widgets.get('tenant_id')\n",
+    "username = dbutils.widgets.get('service_principal_id')\n",
+    "password = dbutils.widgets.get('service_principal_password')\n",
+    "\n",
+    "auth = azureml.core.authentication.ServicePrincipalAuthentication(tenant, username, password)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import the Workspace class and check the azureml SDK version\n",
+    "from azureml.core import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config(auth = auth)\n",
+    "print('Workspace name: ' + ws.name, \n",
+    "      'Azure region: ' + ws.location, \n",
+    "      'Subscription id: ' + ws.subscription_id, \n",
+    "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##PUBLISHONLY\n",
+    "## import the Workspace class and check the azureml SDK version\n",
+    "#from azureml.core import Workspace\n",
+    "#\n",
+    "#ws = Workspace.from_config()\n",
+    "#print('Workspace name: ' + ws.name, \n",
+    "#      'Azure region: ' + ws.location, \n",
+    "#      'Subscription id: ' + ws.subscription_id, \n",
+    "#      'Resource group: ' + ws.resource_group, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#get the train and test datasets\n",
+    "train_data_path = \"AdultCensusIncomeTrain\"\n",
+    "test_data_path = \"AdultCensusIncomeTest\"\n",
+    "\n",
+    "train = spark.read.parquet(train_data_path)\n",
+    "test = spark.read.parquet(test_data_path)\n",
+    "\n",
+    "print(\"train: ({}, {})\".format(train.count(), len(train.columns)))\n",
+    "print(\"test: ({}, {})\".format(test.count(), len(test.columns)))\n",
+    "\n",
+    "train.printSchema()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Define Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "label = \"income\"\n",
+    "dtypes = dict(train.dtypes)\n",
+    "dtypes.pop(label)\n",
+    "\n",
+    "si_xvars = []\n",
+    "ohe_xvars = []\n",
+    "featureCols = []\n",
+    "for idx,key in enumerate(dtypes):\n",
+    "    if dtypes[key] == \"string\":\n",
+    "        featureCol = \"-\".join([key, \"encoded\"])\n",
+    "        featureCols.append(featureCol)\n",
+    "        \n",
+    "        tmpCol = \"-\".join([key, \"tmp\"])\n",
+    "        # string-index and one-hot encode the string column\n",
+    "        #https://spark.apache.org/docs/2.3.0/api/java/org/apache/spark/ml/feature/StringIndexer.html\n",
+    "        #handleInvalid: Param for how to handle invalid data (unseen labels or NULL values). \n",
+    "        #Options are 'skip' (filter out rows with invalid data), 'error' (throw an error), \n",
+    "        #or 'keep' (put invalid data in a special additional bucket, at index numLabels). Default: \"error\"\n",
+    "        si_xvars.append(StringIndexer(inputCol=key, outputCol=tmpCol, handleInvalid=\"skip\"))\n",
+    "        ohe_xvars.append(OneHotEncoder(inputCol=tmpCol, outputCol=featureCol))\n",
+    "    else:\n",
+    "        featureCols.append(key)\n",
+    "\n",
+    "# string-index the label column into a column named \"label\"\n",
+    "si_label = StringIndexer(inputCol=label, outputCol='label')\n",
+    "\n",
+    "# assemble the encoded feature columns in to a column named \"features\"\n",
+    "assembler = VectorAssembler(inputCols=featureCols, outputCol=\"features\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.run import Run\n",
+    "from azureml.core.experiment import Experiment\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import shutil\n",
+    "\n",
+    "model_name = \"AdultCensus_runHistory.mml\"\n",
+    "model_dbfs = os.path.join(\"/dbfs\", model_name)\n",
+    "run_history_name = 'spark-ml-notebook'\n",
+    "\n",
+    "# start a training run by defining an experiment\n",
+    "myexperiment = Experiment(ws, \"Ignite_AI_Talk\")\n",
+    "root_run = myexperiment.start_logging()\n",
+    "\n",
+    "# Regularization Rates - \n",
+    "regs = [0.0001, 0.001, 0.01, 0.1]\n",
+    " \n",
+    "# try a bunch of regularization rate in a Logistic Regression model\n",
+    "for reg in regs:\n",
+    "    print(\"Regularization rate: {}\".format(reg))\n",
+    "    # create a bunch of child runs\n",
+    "    with root_run.child_run(\"reg-\" + str(reg)) as run:\n",
+    "        # create a new Logistic Regression model.\n",
+    "        lr = LogisticRegression(regParam=reg)\n",
+    "        \n",
+    "        # put together the pipeline\n",
+    "        pipe = Pipeline(stages=[*si_xvars, *ohe_xvars, si_label, assembler, lr])\n",
+    "\n",
+    "        # train the model\n",
+    "        model_p = pipe.fit(train)\n",
+    "        \n",
+    "        # make prediction\n",
+    "        pred = model_p.transform(test)\n",
+    "        \n",
+    "        # evaluate. note only 2 metrics are supported out of the box by Spark ML.\n",
+    "        bce = BinaryClassificationEvaluator(rawPredictionCol='rawPrediction')\n",
+    "        au_roc = bce.setMetricName('areaUnderROC').evaluate(pred)\n",
+    "        au_prc = bce.setMetricName('areaUnderPR').evaluate(pred)\n",
+    "\n",
+    "        print(\"Area under ROC: {}\".format(au_roc))\n",
+    "        print(\"Area Under PR: {}\".format(au_prc))\n",
+    "      \n",
+    "        # log reg, au_roc, au_prc and feature names in run history\n",
+    "        run.log(\"reg\", reg)\n",
+    "        run.log(\"au_roc\", au_roc)\n",
+    "        run.log(\"au_prc\", au_prc)\n",
+    "        run.log_list(\"columns\", train.columns)\n",
+    "\n",
+    "        # save model\n",
+    "        model_p.write().overwrite().save(model_name)\n",
+    "        \n",
+    "        # upload the serialized model into run history record\n",
+    "        mdl, ext = model_name.split(\".\")\n",
+    "        model_zip = mdl + \".zip\"\n",
+    "        shutil.make_archive(mdl, 'zip', model_dbfs)\n",
+    "        run.upload_file(\"outputs/\" + model_name, model_zip)        \n",
+    "        #run.upload_file(\"outputs/\" + model_name, path_or_stream = model_dbfs) #cannot deal with folders\n",
+    "\n",
+    "        # now delete the serialized model from local folder since it is already uploaded to run history \n",
+    "        shutil.rmtree(model_dbfs)\n",
+    "        os.remove(model_zip)\n",
+    "        \n",
+    "# Declare run completed\n",
+    "root_run.complete()\n",
+    "root_run_id = root_run.id\n",
+    "print (\"run id:\", root_run.id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics = root_run.get_metrics(recursive=True)\n",
+    "best_run_id = max(metrics, key = lambda k: metrics[k]['au_roc'])\n",
+    "print(best_run_id, metrics[best_run_id]['au_roc'], metrics[best_run_id]['reg'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Get the best run\n",
+    "child_runs = {}\n",
+    "\n",
+    "for r in root_run.get_children():\n",
+    "    child_runs[r.id] = r\n",
+    "   \n",
+    "best_run = child_runs[best_run_id]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Download the model from the best run to a local folder\n",
+    "best_model_file_name = \"best_model.zip\"\n",
+    "best_run.download_file(name = 'outputs/' + model_name, output_file_path = best_model_file_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Model Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##unzip the model to dbfs (as load() seems to require that) and load it.\n",
+    "if os.path.isfile(model_dbfs) or os.path.isdir(model_dbfs):\n",
+    "    shutil.rmtree(model_dbfs)\n",
+    "shutil.unpack_archive(best_model_file_name, model_dbfs)\n",
+    "\n",
+    "model_p_best = PipelineModel.load(model_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make prediction\n",
+    "pred = model_p_best.transform(test)\n",
+    "output = pred[['hours_per_week','age','workclass','marital_status','income','prediction']]\n",
+    "display(output.limit(5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# evaluate. note only 2 metrics are supported out of the box by Spark ML.\n",
+    "bce = BinaryClassificationEvaluator(rawPredictionCol='rawPrediction')\n",
+    "au_roc = bce.setMetricName('areaUnderROC').evaluate(pred)\n",
+    "au_prc = bce.setMetricName('areaUnderPR').evaluate(pred)\n",
+    "\n",
+    "print(\"Area under ROC: {}\".format(au_roc))\n",
+    "print(\"Area Under PR: {}\".format(au_prc))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Model Persistence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##NOTE: by default the model is saved to and loaded from /dbfs/ instead of cwd!\n",
+    "model_p_best.write().overwrite().save(model_name)\n",
+    "print(\"saved model to {}\".format(model_dbfs))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%sh\n",
+    "\n",
+    "ls -la /dbfs/AdultCensus_runHistory.mml/*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dbutils.notebook.exit(\"success\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "pasha"
+   },
+   {
+    "name": "wamartin"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "name": "03.Build_model_runHistory",
+  "notebookId": 3836944406456339
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

how-to-use-azureml/azure-databricks/04.Deploy_to_ACI.ipynb

@@ -0,0 +1,354 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
+    "\n",
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Please ensure you have run all previous notebooks in sequence before running this.\n",
+    "\n",
+    "Please Register Azure Container Instance(ACI) using Azure Portal: https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-manager-supported-services#portal in your subscription before using the SDK to deploy your ML model to ACI."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![04ACI](files/tables/image3.JPG)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import azureml.core\n",
+    "\n",
+    "# Check core SDK version number\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##TESTONLY\n",
+    "# import auth creds from notebook parameters\n",
+    "tenant = dbutils.widgets.get('tenant_id')\n",
+    "username = dbutils.widgets.get('service_principal_id')\n",
+    "password = dbutils.widgets.get('service_principal_password')\n",
+    "\n",
+    "auth = azureml.core.authentication.ServicePrincipalAuthentication(tenant, username, password)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace\n",
+    "\n",
+    "#'''\n",
+    "ws = Workspace.from_config(auth = auth)\n",
+    "print('Workspace name: ' + ws.name, \n",
+    "      'Azure region: ' + ws.location, \n",
+    "      'Subscription id: ' + ws.subscription_id, \n",
+    "      'Resource group: ' + ws.resource_group, sep = '\\n')\n",
+    "#'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##PUBLISHONLY\n",
+    "#from azureml.core import Workspace\n",
+    "#import azureml.core\n",
+    "#\n",
+    "## Check core SDK version number\n",
+    "#print(\"SDK version:\", azureml.core.VERSION)\n",
+    "#\n",
+    "##'''\n",
+    "#ws = Workspace.from_config()\n",
+    "#print('Workspace name: ' + ws.name, \n",
+    "#      'Azure region: ' + ws.location, \n",
+    "#      'Subscription id: ' + ws.subscription_id, \n",
+    "#      'Resource group: ' + ws.resource_group, sep = '\\n')\n",
+    "##'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##NOTE: service deployment always gets the model from the current working dir.\n",
+    "import os\n",
+    "\n",
+    "model_name = \"AdultCensus_runHistory.mml\" # \n",
+    "model_name_dbfs = os.path.join(\"/dbfs\", model_name)\n",
+    "\n",
+    "print(\"copy model from dbfs to local\")\n",
+    "model_local = \"file:\" + os.getcwd() + \"/\" + model_name\n",
+    "dbutils.fs.cp(model_name, model_local, True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Register the model\n",
+    "from azureml.core.model import Model\n",
+    "mymodel = Model.register(model_path = model_name, # this points to a local file\n",
+    "                       model_name = model_name, # this is the name the model is registered as, am using same name for both path and name.                 \n",
+    "                       description = \"ADB trained model by Parashar\",\n",
+    "                       workspace = ws)\n",
+    "\n",
+    "print(mymodel.name, mymodel.description, mymodel.version)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#%%writefile score_sparkml.py\n",
+    "score_sparkml = \"\"\"\n",
+    " \n",
+    "import json\n",
+    " \n",
+    "def init():\n",
+    "    # One-time initialization of PySpark and predictive model\n",
+    "    import pyspark\n",
+    "    from azureml.core.model import Model\n",
+    "    from pyspark.ml import PipelineModel\n",
+    " \n",
+    "    global trainedModel\n",
+    "    global spark\n",
+    " \n",
+    "    spark = pyspark.sql.SparkSession.builder.appName(\"ADB and AML notebook by Parashar\").getOrCreate()\n",
+    "    model_name = \"{model_name}\" #interpolated\n",
+    "    model_path = Model.get_model_path(model_name)\n",
+    "    trainedModel = PipelineModel.load(model_path)\n",
+    "    \n",
+    "def run(input_json):\n",
+    "    if isinstance(trainedModel, Exception):\n",
+    "        return json.dumps({{\"trainedModel\":str(trainedModel)}})\n",
+    "      \n",
+    "    try:\n",
+    "        sc = spark.sparkContext\n",
+    "        input_list = json.loads(input_json)\n",
+    "        input_rdd = sc.parallelize(input_list)\n",
+    "        input_df = spark.read.json(input_rdd)\n",
+    "    \n",
+    "        # Compute prediction\n",
+    "        prediction = trainedModel.transform(input_df)\n",
+    "        #result = prediction.first().prediction\n",
+    "        predictions = prediction.collect()\n",
+    " \n",
+    "        #Get each scored result\n",
+    "        preds = [str(x['prediction']) for x in predictions]\n",
+    "        result = \",\".join(preds)\n",
+    "        # you can return any data type as long as it is JSON-serializable\n",
+    "        return result.tolist()\n",
+    "    except Exception as e:\n",
+    "        result = str(e)\n",
+    "        return result\n",
+    "    \n",
+    "\"\"\".format(model_name=model_name)\n",
+    " \n",
+    "exec(score_sparkml)\n",
+    " \n",
+    "with open(\"score_sparkml.py\", \"w\") as file:\n",
+    "    file.write(score_sparkml)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.conda_dependencies import CondaDependencies \n",
+    "\n",
+    "myacienv = CondaDependencies.create(conda_packages=['scikit-learn','numpy','pandas']) #showing how to add libs as an eg. - not needed for this model.\n",
+    "\n",
+    "with open(\"mydeployenv.yml\",\"w\") as f:\n",
+    "    f.write(myacienv.serialize_to_string())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#deploy to ACI\n",
+    "from azureml.core.webservice import AciWebservice, Webservice\n",
+    "\n",
+    "myaci_config = AciWebservice.deploy_configuration(\n",
+    "    cpu_cores = 2, \n",
+    "    memory_gb = 2, \n",
+    "    tags = {'name':'Databricks Azure ML ACI'}, \n",
+    "    description = 'This is for ADB and AML example. Azure Databricks & Azure ML SDK demo with ACI by Parashar.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this will take 10-15 minutes to finish\n",
+    "\n",
+    "service_name = \"aciws\"\n",
+    "runtime = \"spark-py\" \n",
+    "driver_file = \"score_sparkml.py\"\n",
+    "my_conda_file = \"mydeployenv.yml\"\n",
+    "\n",
+    "# image creation\n",
+    "from azureml.core.image import ContainerImage\n",
+    "myimage_config = ContainerImage.image_configuration(execution_script = driver_file, \n",
+    "                                    runtime = runtime, \n",
+    "                                    conda_file = my_conda_file)\n",
+    "\n",
+    "# Webservice creation\n",
+    "myservice = Webservice.deploy_from_model(\n",
+    "  workspace=ws, \n",
+    "  name=service_name,\n",
+    "  deployment_config = myaci_config,\n",
+    "  models = [mymodel],\n",
+    "  image_config = myimage_config\n",
+    "    )\n",
+    "\n",
+    "myservice.wait_for_deployment(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "help(Webservice)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# List images by ws\n",
+    "\n",
+    "for i in ContainerImage.list(workspace = ws):\n",
+    "    print('{}(v.{} [{}]) stored at {} with build log {}'.format(i.name, i.version, i.creation_state, i.image_location, i.image_build_log_uri))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#for using the Web HTTP API \n",
+    "print(myservice.scoring_uri)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "#get the some sample data\n",
+    "test_data_path = \"AdultCensusIncomeTest\"\n",
+    "test = spark.read.parquet(test_data_path).limit(5)\n",
+    "\n",
+    "test_json = json.dumps(test.toJSON().collect())\n",
+    "\n",
+    "print(test_json)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#using data defined above predict if income is >50K (1) or <=50K (0)\n",
+    "myservice.run(input_data=test_json)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#comment to not delete the web service\n",
+    "#myservice.delete()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "pasha"
+   },
+   {
+    "name": "wamartin"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "name": "04.DeploytoACI",
+  "notebookId": 3836944406456376
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

how-to-use-azureml/azure-databricks/06.AutoML_Databricks_local.ipynb

@@ -0,0 +1,634 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AutoML : Classification with Local Compute on Azure DataBricks\n",
+    "\n",
+    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
+    "\n",
+    "In this notebook you will learn how to:\n",
+    "1. Create Azure Machine Learning Workspace object and initialize your notebook directory to easily reload this object from a configuration file.\n",
+    "2. Create an `Experiment` in an existing `Workspace`.\n",
+    "3. Configure AutoML using `AutoMLConfig`.\n",
+    "4. Train the model using AzureDataBricks.\n",
+    "5. Explore the results.\n",
+    "6. Test the best fitted model.\n",
+    "\n",
+    "Prerequisites:\n",
+    "Before running this notebook, please follow the readme for installing necessary libraries to your cluster."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Register Machine Learning Services Resource Provider\n",
+    "Microsoft.MachineLearningServices only needs to be registed once in the subscription. To register it:\n",
+    "Start the Azure portal.\n",
+    "Select your All services and then Subscription.\n",
+    "Select the subscription that you want to use.\n",
+    "Click on Resource providers\n",
+    "Click the Register link next to Microsoft.MachineLearningServices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Check the Azure ML Core SDK Version to Validate Your Installation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import azureml.core\n",
+    "\n",
+    "print(\"SDK Version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize an Azure ML Workspace\n",
+    "### What is an Azure ML Workspace and Why Do I Need One?\n",
+    "\n",
+    "An Azure ML workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an Azure ML workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, operationalization, and the monitoring of operationalized models.\n",
+    "\n",
+    "\n",
+    "### What do I Need?\n",
+    "\n",
+    "To create or access an Azure ML workspace, you will need to import the Azure ML library and specify following information:\n",
+    "* A name for your workspace. You can choose one.\n",
+    "* Your subscription id. Use the `id` value from the `az account show` command output above.\n",
+    "* The resource group name. The resource group organizes Azure resources and provides a default region for the resources in the group. The resource group will be created if it doesn't exist. Resource groups can be created and viewed in the [Azure portal](https://portal.azure.com)\n",
+    "* Supported regions include `eastus2`, `eastus`,`westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##PUBLISHONLY\n",
+    "#subscription_id = \"<Your SubscriptionId>\"\n",
+    "#resource_group = \"<Resource group - new or existing>\"\n",
+    "#workspace_name = \"<workspace to be created>\"\n",
+    "#workspace_region = \"<azureregion>\" #eg. eastus2, westcentralus, westeurope"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a Workspace\n",
+    "If you already have access to an Azure ML workspace you want to use, you can skip this cell.  Otherwise, this cell will create an Azure ML workspace for you in the specified subscription, provided you have the correct permissions for the given `subscription_id`.\n",
+    "\n",
+    "This will fail when:\n",
+    "1. The workspace already exists.\n",
+    "2. You do not have permission to create a workspace in the resource group.\n",
+    "3. You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription.\n",
+    "\n",
+    "If workspace creation fails for any reason other than already existing, please work with your IT administrator to provide you with the appropriate permissions or to provision the required resources.\n",
+    "\n",
+    "**Note:** Creation of a new workspace can take several minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##TESTONLY\n",
+    "# import auth creds from notebook parameters\n",
+    "tenant = dbutils.widgets.get('tenant_id')\n",
+    "username = dbutils.widgets.get('service_principal_id')\n",
+    "password = dbutils.widgets.get('service_principal_password')\n",
+    "\n",
+    "auth = azureml.core.authentication.ServicePrincipalAuthentication(tenant, username, password)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##TESTONLY\n",
+    "subscription_id  = dbutils.widgets.get('subscription_id')\n",
+    "resource_group = dbutils.widgets.get('resource_group')\n",
+    "workspace_name = dbutils.widgets.get('workspace_name')\n",
+    "workspace_region = dbutils.widgets.get('workspace_region')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import the Workspace class and check the Azure ML SDK version.\n",
+    "from azureml.core import Workspace\n",
+    "\n",
+    "ws = Workspace.create(name = workspace_name,\n",
+    "                      subscription_id = subscription_id,\n",
+    "                      resource_group = resource_group, \n",
+    "                      location = workspace_region,\n",
+    "                      auth = auth,\n",
+    "                      exist_ok=True)\n",
+    "ws.get_details()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##PUBLISHONLY\n",
+    "#from azureml.core import Workspace\n",
+    "#import azureml.core\n",
+    "#\n",
+    "## Check core SDK version number\n",
+    "#print(\"SDK version:\", azureml.core.VERSION)\n",
+    "#\n",
+    "##'''\n",
+    "#ws = Workspace.from_config()\n",
+    "#print('Workspace name: ' + ws.name, \n",
+    "#      'Azure region: ' + ws.location, \n",
+    "#      'Subscription id: ' + ws.subscription_id, \n",
+    "#      'Resource group: ' + ws.resource_group, sep = '\\n')\n",
+    "##'''"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configuring Your Local Environment\n",
+    "You can validate that you have access to the specified workspace and write a configuration file to the default configuration location, `./aml_config/config.json`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##TESTONLY\n",
+    "from azureml.core import Workspace\n",
+    "\n",
+    "ws = Workspace(workspace_name = workspace_name,\n",
+    "               subscription_id = subscription_id,\n",
+    "               resource_group = resource_group,\n",
+    "               auth = auth)\n",
+    "\n",
+    "# Persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
+    "ws.write_config()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##PUBLISHONLY\n",
+    "#from azureml.core import Workspace\n",
+    "#\n",
+    "#ws = Workspace(workspace_name = workspace_name,\n",
+    "#               subscription_id = subscription_id,\n",
+    "#               resource_group = resource_group)\n",
+    "#\n",
+    "## Persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
+    "#ws.write_config()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a Folder to Host Sample Projects\n",
+    "Finally, create a folder where all the sample projects will be hosted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "sample_projects_folder = './sample_projects'\n",
+    "\n",
+    "if not os.path.isdir(sample_projects_folder):\n",
+    "    os.mkdir(sample_projects_folder)\n",
+    "    \n",
+    "print('Sample projects will be created in {}.'.format(sample_projects_folder))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Experiment\n",
+    "\n",
+    "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "import time\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import imshow\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import azureml.core\n",
+    "from azureml.core.experiment import Experiment\n",
+    "from azureml.core.workspace import Workspace\n",
+    "from azureml.train.automl import AutoMLConfig\n",
+    "from azureml.train.automl.run import AutoMLRun"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##TESTONLY\n",
+    "ws = Workspace.from_config(auth = auth)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##PUBLISHONLY\n",
+    "#ws = Workspace.from_config(auth = auth)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Choose a name for the experiment and specify the project folder.\n",
+    "experiment_name = 'automl-local-classification'\n",
+    "project_folder = './sample_projects/automl-local-classification'\n",
+    "\n",
+    "experiment = Experiment(ws, experiment_name)\n",
+    "\n",
+    "output = {}\n",
+    "output['SDK version'] = azureml.core.VERSION\n",
+    "output['Subscription ID'] = ws.subscription_id\n",
+    "output['Workspace Name'] = ws.name\n",
+    "output['Resource Group'] = ws.resource_group\n",
+    "output['Location'] = ws.location\n",
+    "output['Project Directory'] = project_folder\n",
+    "output['Experiment Name'] = experiment.name\n",
+    "pd.set_option('display.max_colwidth', -1)\n",
+    "pd.DataFrame(data = output, index = ['']).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "set_diagnostics_collection(send_diagnostics = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Training Data Using DataPrep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import azureml.dataprep as dprep\n",
+    "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
+    "# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
+    "simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
+    "X_train = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1)  # Remove the header row.\n",
+    "\n",
+    "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
+    "# and convert column types manually.\n",
+    "# Here we read a comma delimited file and convert all columns to integers.\n",
+    "y_train = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Review the Data Preparation Result\n",
+    "You can peek the result of a Dataflow at any range using skip(i) and head(j). Doing so evaluates only j records for all the steps in the Dataflow, which makes it fast even against large datasets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train.skip(1).head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure AutoML\n",
+    "\n",
+    "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
+    "\n",
+    "|Property|Description|\n",
+    "|-|-|\n",
+    "|**task**|classification or regression|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
+    "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
+    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+    "|**n_cross_validations**|Number of cross validation splits.|\n",
+    "|**spark_context**|Spark Context object. for Databricks, use spark_context=sc|\n",
+    "|**max_cuncurrent_iterations**|Maximum number of iterations to execute in parallel. This should be less than the number of cores on the ADB..|\n",
+    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
+    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
+    "|**concurrent_iterations**|number of concurrent runs <= total cores in all worker nodes in your Databricks cluster|\n",
+    "|**exit_score**|Target score for experiment. It is associated with the metric. eg. exit_score=0.995 will exit experiment after that|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automl_config = AutoMLConfig(task = 'classification',\n",
+    "                             debug_log = 'automl_errors.log',\n",
+    "                             primary_metric = 'AUC_weighted',\n",
+    "                             iteration_timeout_minutes = 10,\n",
+    "                             iterations = 30,\n",
+    "                             n_cross_validations = 10,\n",
+    "                             max_concurrent_iterations = 8, #change it based on number of cores in worker nodes\n",
+    "                             verbosity = logging.INFO,\n",
+    "                             spark_context=sc, #databricks/spark related\n",
+    "                             X = X_train, \n",
+    "                             y = y_train,\n",
+    "                             enable_cache=False,\n",
+    "                             path = project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train the Models\n",
+    "\n",
+    "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
+    "In this example, we specify `show_output = True` to print currently running iterations to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_run = experiment.submit(automl_config, show_output = True) # for higher runs please use show_output=False and use the below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Portal URL for Monitoring Runs\n",
+    "\n",
+    "The following will provide a link to the web interface to explore individual run details and status. In the future we might support output displayed in the notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(local_run.get_portal_url())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following will show the child runs and waits for the parent run to complete."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Retrieve All Child Runs after the experiment is completed (in portal)\n",
+    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "children = list(local_run.get_children())\n",
+    "metricslist = {}\n",
+    "for run in children:\n",
+    "    properties = run.get_properties()\n",
+    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
+    "    metricslist[int(properties['iteration'])] = metrics\n",
+    "\n",
+    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+    "rundata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve the Best Model after the above run is complete \n",
+    "\n",
+    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_run, fitted_model = local_run.get_output()\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Best Model Based on Any Other Metric after the above run is complete based on the child run\n",
+    "Show the run and the model that has the smallest `log_loss` value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lookup_metric = \"log_loss\"\n",
+    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
+    "print(best_run)\n",
+    "print(fitted_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test the Best Fitted Model\n",
+    "\n",
+    "#### Load Test Data - you can split the dataset beforehand & pass Train dataset to AutoML and use Test dataset to evaluate the best model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "digits = datasets.load_digits()\n",
+    "X_test = digits.data[:10, :]\n",
+    "y_test = digits.target[:10]\n",
+    "images = digits.images[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Testing Our Best Fitted Model\n",
+    "We will try to predict digits and see how our model works. This is just an example to show you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Randomly select digits and test.\n",
+    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+    "    print(index)\n",
+    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+    "    label = y_test[index]\n",
+    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
+    "    fig = plt.figure(1, figsize = (3,3))\n",
+    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+    "    ax1.set_title(title)\n",
+    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+    "    display(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "savitam"
+   },
+   {
+    "name": "wamartin"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "name": "auto-ml-classification-local-adb",
+  "notebookId": 3836944406456411
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

how-to-use-azureml/deployment/accelerated-models/NOTICE.txt

@@ -1,217 +0,0 @@
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-NOTICES AND INFORMATION
-Do Not Translate or Localize
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-This Azure Machine Learning service example notebooks repository includes material from the projects listed below. 
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how-to-use-azureml/deployment/accelerated-models/README.md

@@ -1,104 +0,0 @@
-
-# Notebooks for Microsoft Azure Machine Learning Hardware Accelerated Models SDK
-
-Easily create and train a model using various deep neural networks (DNNs) as a featurizer for deployment to Azure or a Data Box Edge device for ultra-low latency inferencing using FPGA's. These models are currently available:
-
-* ResNet 50
-* ResNet 152
-* DenseNet-121
-* VGG-16
-* SSD-VGG  
-
-To learn more about the azureml-accel-model classes, see the section [Model Classes](#model-classes) below or the [Azure ML Accel Models SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel?view=azure-ml-py).
-
-### Step 1: Create an Azure ML workspace
-Follow [these instructions](https://docs.microsoft.com/en-us/azure/machine-learning/service/setup-create-workspace) to install the Azure ML SDK on your local machine, create an Azure ML workspace, and set up your notebook environment, which is required for the next step.
-
-### Step 2: Check your FPGA quota
-Use the Azure CLI to check whether you have quota.
-
-```shell
-az vm list-usage --location "eastus" -o table
-```
-
-The other locations are ``southeastasia``, ``westeurope``, and ``westus2``.
-
-Under the "Name" column, look for "Standard PBS Family vCPUs" and ensure you have at least 6 vCPUs under "CurrentValue."
-
-If you do not have quota, then submit a request form [here](https://aka.ms/accelerateAI).
-
-### Step 3: Install the Azure ML Accelerated Models SDK
-Once you have set up your environment, install the Azure ML Accel Models SDK. This package requires tensorflow >= 1.6,<2.0 to be installed. 
-
-If you already have tensorflow >= 1.6,<2.0 installed in your development environment, you can install the SDK package using: 
-
-```
-pip install azureml-accel-models
-```
-
-If you do not have tensorflow >= 1.6,<2.0 and are using a CPU-only development environment, our SDK with tensorflow can be installed using:
-
-```
-pip install azureml-accel-models[cpu]
-```
-
-If your machine supports GPU (for example, on an [Azure DSVM](https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview)), then you can leverage the tensorflow-gpu functionality using:
-
-```
-pip install azureml-accel-models[gpu]
-```
-
-### Step 4: Follow our notebooks
-
-We provide notebooks to walk through the following scenarios, linked below: 
-* [Quickstart](https://github.com/Azure/MachineLearningNotebooks/blob/33d6def8c30d3dd3a5bfbea50b9c727788185faf/how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.ipynb), deploy and inference a ResNet50 model trained on ImageNet
-* [Object Detection](https://github.com/Azure/MachineLearningNotebooks/blob/33d6def8c30d3dd3a5bfbea50b9c727788185faf/how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.ipynb), deploy and inference an SSD-VGG model that can do object detection
-* [Training models](https://github.com/Azure/MachineLearningNotebooks/blob/33d6def8c30d3dd3a5bfbea50b9c727788185faf/how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.ipynb), train one of our accelerated models on the Kaggle Cats and Dogs dataset to see how to improve accuracy on custom datasets
-
-**Note**: the above notebooks work only for tensorflow >= 1.6,<2.0.
-
-<a name="model-classes"></a>
-## Model Classes
-As stated above, we support 5 Accelerated Models. Here's more information on their input and output tensors.
-
-**Available models and output tensors**
-
-The available models and the corresponding default classifier output tensors are below. This is the value that you would use during inferencing if you used the default classifier.
-* Resnet50, QuantizedResnet50 
-``
-output_tensors = "classifier_1/resnet_v1_50/predictions/Softmax:0"
-``
-* Resnet152, QuantizedResnet152 
-``
-output_tensors = "classifier/resnet_v1_152/predictions/Softmax:0"
-``
-* Densenet121, QuantizedDensenet121
-``
-output_tensors = "classifier/densenet121/predictions/Softmax:0"
-``
-* Vgg16, QuantizedVgg16 
-``
-output_tensors = "classifier/vgg_16/fc8/squeezed:0"
-``
-* SsdVgg, QuantizedSsdVgg
-``
-output_tensors = ['ssd_300_vgg/block4_box/Reshape_1:0', 'ssd_300_vgg/block7_box/Reshape_1:0', 'ssd_300_vgg/block8_box/Reshape_1:0', 'ssd_300_vgg/block9_box/Reshape_1:0', 'ssd_300_vgg/block10_box/Reshape_1:0', 'ssd_300_vgg/block11_box/Reshape_1:0', 'ssd_300_vgg/block4_box/Reshape:0', 'ssd_300_vgg/block7_box/Reshape:0', 'ssd_300_vgg/block8_box/Reshape:0', 'ssd_300_vgg/block9_box/Reshape:0', 'ssd_300_vgg/block10_box/Reshape:0', 'ssd_300_vgg/block11_box/Reshape:0']
-``
-
-For more information, please reference the azureml.accel.models package in the [Azure ML Python SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel.models?view=azure-ml-py).
-
-**Input tensors**
-
-The input_tensors value defaults to "Placeholder:0" and is created in the [Image Preprocessing](#construct-model) step in the line: 
-``
-in_images = tf.placeholder(tf.string)
-``
-
-You can change the input_tensors name by doing this: 
-``
-in_images = tf.placeholder(tf.string, name="images")
-``
-
-
-## Resources
-*  [Read more about FPGAs](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-accelerate-with-fpgas)

how-to-use-azureml/deployment/deploy-multi-model/README.md

@@ -1,14 +0,0 @@
-# Model Deployment with Azure ML service
-You can use Azure Machine Learning to package, debug, validate and deploy inference containers to a variety of compute targets. This process is known as "MLOps" (ML operationalization).
-For more information please check out this article: https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where
-
-## Get Started
-To begin, you will need an ML workspace.
-For more information please check out this article: https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace
-
-## Deploy to the cloud
-You can deploy to the cloud using the Azure ML CLI or the Azure ML SDK.
-- CLI example: https://aka.ms/azmlcli
-- Notebook example: [model-register-and-deploy](./model-register-and-deploy.ipynb).
-
-![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-multi-model/README.png)

how-to-use-azureml/deployment/deploy-multi-model/multi-model-register-and-deploy.ipynb

@@ -1,395 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-    "\n",
-    "Licensed under the MIT License."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-multi-model/multi-model-register-and-deploy.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Deploy Multiple Models as Webservice\n",
-    "\n",
-    "This example shows how to deploy a Webservice with multiple models in step-by-step fashion:\n",
-    "\n",
-    " 1. Register Models\n",
-    " 2. Deploy Models as Webservice"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prerequisites\n",
-    "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Check core SDK version number\n",
-    "import azureml.core\n",
-    "\n",
-    "print(\"SDK version:\", azureml.core.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialize Workspace\n",
-    "\n",
-    "Initialize a workspace object from persisted configuration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "create workspace"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "from azureml.core import Workspace\n",
-    "\n",
-    "ws = Workspace.from_config()\n",
-    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Register Models"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this example, we will be using and registering two models. \n",
-    "\n",
-    "First we will train two simple models on the [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset) included with scikit-learn, serializing them to files in the current directory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import joblib\n",
-    "import sklearn\n",
-    "\n",
-    "from sklearn.datasets import load_diabetes\n",
-    "from sklearn.linear_model import BayesianRidge, Ridge\n",
-    "\n",
-    "x, y = load_diabetes(return_X_y=True)\n",
-    "\n",
-    "first_model = Ridge().fit(x, y)\n",
-    "second_model = BayesianRidge().fit(x, y)\n",
-    "\n",
-    "joblib.dump(first_model, \"first_model.pkl\")\n",
-    "joblib.dump(second_model, \"second_model.pkl\")\n",
-    "\n",
-    "print(\"Trained models using scikit-learn {}.\".format(sklearn.__version__))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have our trained models locally, we will register them as Models with the names `my_first_model` and `my_second_model` in the workspace."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "register model from file"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "from azureml.core.model import Model\n",
-    "\n",
-    "my_model_1 = Model.register(model_path=\"first_model.pkl\",\n",
-    "                            model_name=\"my_first_model\",\n",
-    "                            workspace=ws)\n",
-    "\n",
-    "my_model_2 = Model.register(model_path=\"second_model.pkl\",\n",
-    "                            model_name=\"my_second_model\",\n",
-    "                            workspace=ws)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Write the Entry Script\n",
-    "Write the script that will be used to predict on your models"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Model.get_model_path()\n",
-    "\n",
-    "To get the paths of your models, use `Model.get_model_path(model_name, version=None, _workspace=None)` method. This method will find the path to a model using the name of the model registered under the workspace.\n",
-    "\n",
-    "In this example, we do not use the optional arguments `version` and `_workspace`.\n",
-    "\n",
-    "#### Using environment variable AZUREML_MODEL_DIR\n",
-    "\n",
-    "In other [examples](../deploy-to-cloud/score.py) with a single model deployment, we use the environment variable `AZUREML_MODEL_DIR` and model file name to get the model path. \n",
-    "\n",
-    "For single model deployments, this environment variable is the path to the model folder (`./azureml-models/$MODEL_NAME/$VERSION`). When we deploy multiple models, the environment variable is set to the folder containing all models (./azureml-models).\n",
-    "\n",
-    "If you're using multiple models and you know the versions of the models you deploy, you can use this method to get the model path:\n",
-    "\n",
-    "```python\n",
-    "# Construct the model path using the registered model name, version, and model file name\n",
-    "model_1_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'my_first_model', '1', 'first_model.pkl')\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%writefile score.py\n",
-    "import joblib\n",
-    "import json\n",
-    "import numpy as np\n",
-    "\n",
-    "from azureml.core.model import Model\n",
-    "\n",
-    "def init():\n",
-    "    global model_1, model_2\n",
-    "    # Here \"my_first_model\" is the name of the model registered under the workspace.\n",
-    "    # This call will return the path to the .pkl file on the local disk.\n",
-    "    model_1_path = Model.get_model_path(model_name='my_first_model')\n",
-    "    model_2_path = Model.get_model_path(model_name='my_second_model')\n",
-    "    \n",
-    "    # Deserialize the model files back into scikit-learn models.\n",
-    "    model_1 = joblib.load(model_1_path)\n",
-    "    model_2 = joblib.load(model_2_path)\n",
-    "\n",
-    "# Note you can pass in multiple rows for scoring.\n",
-    "def run(raw_data):\n",
-    "    try:\n",
-    "        data = json.loads(raw_data)['data']\n",
-    "        data = np.array(data)\n",
-    "        \n",
-    "        # Call predict() on each model\n",
-    "        result_1 = model_1.predict(data)\n",
-    "        result_2 = model_2.predict(data)\n",
-    "\n",
-    "        # You can return any JSON-serializable value.\n",
-    "        return {\"prediction1\": result_1.tolist(), \"prediction2\": result_2.tolist()}\n",
-    "    except Exception as e:\n",
-    "        result = str(e)\n",
-    "        return result"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Environment"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can now create and/or use an Environment object when deploying a Webservice. The Environment can have been previously registered with your Workspace, or it will be registered with it as a part of the Webservice deployment. Please note that your environment must include azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service.\n",
-    "\n",
-    "More information can be found in our [using environments notebook](../training/using-environments/using-environments.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core import Environment\n",
-    "\n",
-    "env = Environment(\"deploytocloudenv\")\n",
-    "env.python.conda_dependencies.add_pip_package(\"joblib\")\n",
-    "env.python.conda_dependencies.add_pip_package(\"numpy\")\n",
-    "env.python.conda_dependencies.add_pip_package(\"scikit-learn=={}\".format(sklearn.__version__))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Inference Configuration\n",
-    "\n",
-    "There is now support for a source directory, you can upload an entire folder from your local machine as dependencies for the Webservice.\n",
-    "Note: in that case, environments's entry_script and file_path are relative paths to the source_directory path; myenv.docker.base_dockerfile is a string containing extra docker steps or contents of the docker file.\n",
-    "\n",
-    "Sample code for using a source directory:\n",
-    "\n",
-    "```python\n",
-    "from azureml.core.environment import Environment\n",
-    "from azureml.core.model import InferenceConfig\n",
-    "\n",
-    "myenv = Environment.from_conda_specification(name='myenv', file_path='env/myenv.yml')\n",
-    "\n",
-    "# explicitly set base_image to None when setting base_dockerfile\n",
-    "myenv.docker.base_image = None\n",
-    "# add extra docker commends to execute\n",
-    "myenv.docker.base_dockerfile = \"FROM ubuntu\\n RUN echo \\\"hello\\\"\"\n",
-    "\n",
-    "inference_config = InferenceConfig(source_directory=\"C:/abc\",\n",
-    "                                   entry_script=\"x/y/score.py\",\n",
-    "                                   environment=myenv)\n",
-    "```\n",
-    "\n",
-    " - file_path: input parameter to Environment constructor. Manages conda and python package dependencies.\n",
-    " - env.docker.base_dockerfile: any extra steps you want to inject into docker file\n",
-    " - source_directory: holds source path as string, this entire folder gets added in image so its really easy to access any files within this folder or subfolder\n",
-    " - entry_script: contains logic specific to initializing your model and running predictions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "create image"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "from azureml.core.model import InferenceConfig\n",
-    "\n",
-    "inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Deploy Model as Webservice on Azure Container Instance\n",
-    "\n",
-    "Note that the service creation can take few minutes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "azuremlexception-remarks-sample"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "from azureml.core.webservice import AciWebservice\n",
-    "\n",
-    "aci_service_name = \"aciservice-multimodel\"\n",
-    "\n",
-    "deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
-    "\n",
-    "service = Model.deploy(ws, aci_service_name, [my_model_1, my_model_2], inference_config, deployment_config, overwrite=True)\n",
-    "service.wait_for_deployment(True)\n",
-    "\n",
-    "print(service.state)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Test web service"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "\n",
-    "test_sample = json.dumps({'data': x[0:2].tolist()})\n",
-    "\n",
-    "prediction = service.run(test_sample)\n",
-    "\n",
-    "print(prediction)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Delete ACI to clean up"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "deploy service",
-     "aci"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "service.delete()"
-   ]
-  }
- ],
- "metadata": {
-  "authors": [
-   {
-    "name": "jenns"
-   }
-  ],
-  "kernelspec": {
-   "display_name": "Python 3.6",
-   "language": "python",
-   "name": "python36"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

how-to-use-azureml/deployment/deploy-multi-model/multi-model-register-and-deploy.yml

@@ -1,6 +0,0 @@
-name: multi-model-register-and-deploy
-dependencies:
-- pip:
-  - azureml-sdk
-  - numpy
-  - scikit-learn

how-to-use-azureml/deployment/deploy-to-cloud/README.md

@@ -1,12 +0,0 @@
-# Model Deployment with Azure ML service
-You can use Azure Machine Learning to package, debug, validate and deploy inference containers to a variety of compute targets. This process is known as "MLOps" (ML operationalization).
-For more information please check out this article: https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where
-
-## Get Started
-To begin, you will need an ML workspace.
-For more information please check out this article: https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace
-
-## Deploy to the cloud
-You can deploy to the cloud using the Azure ML CLI or the Azure ML SDK.
-- CLI example: https://aka.ms/azmlcli
-- Notebook example: [model-register-and-deploy](./model-register-and-deploy.ipynb).

how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb

@@ -1,593 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-    "\n",
-    "Licensed under the MIT License."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Register model and deploy as webservice in ACI\n",
-    "\n",
-    "Following this notebook, you will:\n",
-    "\n",
-    " - Learn how to register a model in your Azure Machine Learning Workspace.\n",
-    " - Deploy your model as a web service in an Azure Container Instance."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prerequisites\n",
-    "\n",
-    "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration notebook](../../../configuration.ipynb) to install the Azure Machine Learning Python SDK and create a workspace."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import azureml.core\n",
-    "\n",
-    "\n",
-    "# Check core SDK version number.\n",
-    "print('SDK version:', azureml.core.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialize workspace\n",
-    "\n",
-    "Create a [Workspace](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace%28class%29?view=azure-ml-py) object from your persisted configuration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "create workspace"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "from azureml.core import Workspace\n",
-    "\n",
-    "\n",
-    "ws = Workspace.from_config()\n",
-    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create trained model\n",
-    "\n",
-    "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import joblib\n",
-    "\n",
-    "from sklearn.datasets import load_diabetes\n",
-    "from sklearn.linear_model import Ridge\n",
-    "\n",
-    "\n",
-    "dataset_x, dataset_y = load_diabetes(return_X_y=True)\n",
-    "\n",
-    "model = Ridge().fit(dataset_x, dataset_y)\n",
-    "\n",
-    "joblib.dump(model, 'sklearn_regression_model.pkl')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Register input and output datasets\n",
-    "\n",
-    "Here, you will register the data used to create the model in your workspace."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "from azureml.core import Dataset\n",
-    "\n",
-    "\n",
-    "np.savetxt('features.csv', dataset_x, delimiter=',')\n",
-    "np.savetxt('labels.csv', dataset_y, delimiter=',')\n",
-    "\n",
-    "datastore = ws.get_default_datastore()\n",
-    "datastore.upload_files(files=['./features.csv', './labels.csv'],\n",
-    "                       target_path='sklearn_regression/',\n",
-    "                       overwrite=True)\n",
-    "\n",
-    "input_dataset = Dataset.Tabular.from_delimited_files(path=[(datastore, 'sklearn_regression/features.csv')])\n",
-    "output_dataset = Dataset.Tabular.from_delimited_files(path=[(datastore, 'sklearn_regression/labels.csv')])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Register model\n",
-    "\n",
-    "Register a file or folder as a model by calling [Model.register()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#register-workspace--model-path--model-name--tags-none--properties-none--description-none--datasets-none--model-framework-none--model-framework-version-none--child-paths-none-).\n",
-    "\n",
-    "In addition to the content of the model file itself, your registered model will also store model metadata -- model description, tags, and framework information -- that will be useful when managing and deploying models in your workspace. Using tags, for instance, you can categorize your models and apply filters when listing models in your workspace. Also, marking this model with the scikit-learn framework will simplify deploying it as a web service, as we'll see later."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "register model from file",
-     "sample-model-register"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "import sklearn\n",
-    "\n",
-    "from azureml.core import Model\n",
-    "from azureml.core.resource_configuration import ResourceConfiguration\n",
-    "\n",
-    "\n",
-    "model = Model.register(workspace=ws,\n",
-    "                       model_name='my-sklearn-model',                # Name of the registered model in your workspace.\n",
-    "                       model_path='./sklearn_regression_model.pkl',  # Local file to upload and register as a model.\n",
-    "                       model_framework=Model.Framework.SCIKITLEARN,  # Framework used to create the model.\n",
-    "                       model_framework_version=sklearn.__version__,  # Version of scikit-learn used to create the model.\n",
-    "                       sample_input_dataset=input_dataset,\n",
-    "                       sample_output_dataset=output_dataset,\n",
-    "                       resource_configuration=ResourceConfiguration(cpu=1, memory_in_gb=0.5),\n",
-    "                       description='Ridge regression model to predict diabetes progression.',\n",
-    "                       tags={'area': 'diabetes', 'type': 'regression'})\n",
-    "\n",
-    "print('Name:', model.name)\n",
-    "print('Version:', model.version)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Deploy model\n",
-    "\n",
-    "Deploy your model as a web service using [Model.deploy()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#deploy-workspace--name--models--inference-config--deployment-config-none--deployment-target-none-). Web services take one or more models, load them in an environment, and run them on one of several supported deployment targets. For more information on all your options when deploying models, see the [next steps](#Next-steps) section at the end of this notebook.\n",
-    "\n",
-    "For this example, we will deploy your scikit-learn model to an Azure Container Instance (ACI)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Use a default environment (for supported models)\n",
-    "\n",
-    "The Azure Machine Learning service provides a default environment for supported model frameworks, including scikit-learn, based on the metadata you provided when registering your model. This is the easiest way to deploy your model.\n",
-    "\n",
-    "Even when you deploy your model to ACI with a default environment you can still customize the deploy configuration (i.e. the number of cores and amount of memory made available for the deployment) using the [AciWebservice.deploy_configuration()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.webservice.aci.aciwebservice#deploy-configuration-cpu-cores-none--memory-gb-none--tags-none--properties-none--description-none--location-none--auth-enabled-none--ssl-enabled-none--enable-app-insights-none--ssl-cert-pem-file-none--ssl-key-pem-file-none--ssl-cname-none--dns-name-label-none--). Look at the \"Use a custom environment\" section of this notebook for more information on deploy configuration.\n",
-    "\n",
-    "**Note**: This step can take several minutes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "service_name = 'my-sklearn-service'\n",
-    "\n",
-    "service = Model.deploy(ws, service_name, [model], overwrite=True)\n",
-    "service.wait_for_deployment(show_output=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After your model is deployed, perform a call to the web service using [service.run()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice%28class%29?view=azure-ml-py#run-input-)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "\n",
-    "\n",
-    "input_payload = json.dumps({\n",
-    "    'data': dataset_x[0:2].tolist(),\n",
-    "    'method': 'predict'  # If you have a classification model, you can get probabilities by changing this to 'predict_proba'.\n",
-    "})\n",
-    "\n",
-    "output = service.run(input_payload)\n",
-    "\n",
-    "print(output)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When you are finished testing your service, clean up the deployment with [service.delete()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice%28class%29?view=azure-ml-py#delete--)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "service.delete()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Use a custom environment\n",
-    "\n",
-    "If you want more control over how your model is run, if it uses another framework, or if it has special runtime requirements, you can instead specify your own environment and scoring method. Custom environments can be used for any model you want to deploy.\n",
-    "\n",
-    "Specify the model's runtime environment by creating an [Environment](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.environment%28class%29?view=azure-ml-py) object and providing the [CondaDependencies](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.conda_dependencies.condadependencies?view=azure-ml-py) needed by your model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core import Environment\n",
-    "from azureml.core.conda_dependencies import CondaDependencies\n",
-    "\n",
-    "\n",
-    "environment = Environment('my-sklearn-environment')\n",
-    "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
-    "    'azureml-defaults',\n",
-    "    'inference-schema[numpy-support]',\n",
-    "    'joblib',\n",
-    "    'numpy',\n",
-    "    'scikit-learn=={}'.format(sklearn.__version__)\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When using a custom environment, you must also provide Python code for initializing and running your model. An example script is included with this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('score.py') as f:\n",
-    "    print(f.read())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Deploy your model in the custom environment by providing an [InferenceConfig](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.inferenceconfig?view=azure-ml-py) object to [Model.deploy()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#deploy-workspace--name--models--inference-config--deployment-config-none--deployment-target-none-). In this case we are also using the [AciWebservice.deploy_configuration()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.webservice.aci.aciwebservice#deploy-configuration-cpu-cores-none--memory-gb-none--tags-none--properties-none--description-none--location-none--auth-enabled-none--ssl-enabled-none--enable-app-insights-none--ssl-cert-pem-file-none--ssl-key-pem-file-none--ssl-cname-none--dns-name-label-none--) method to generate a custom deploy configuration.\n",
-    "\n",
-    "**Note**: This step can take several minutes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "azuremlexception-remarks-sample",
-     "sample-aciwebservice-deploy-config"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "from azureml.core.model import InferenceConfig\n",
-    "from azureml.core.webservice import AciWebservice\n",
-    "\n",
-    "\n",
-    "service_name = 'my-custom-env-service'\n",
-    "\n",
-    "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n",
-    "aci_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
-    "\n",
-    "service = Model.deploy(workspace=ws,\n",
-    "                       name=service_name,\n",
-    "                       models=[model],\n",
-    "                       inference_config=inference_config,\n",
-    "                       deployment_config=aci_config,\n",
-    "                       overwrite=True)\n",
-    "service.wait_for_deployment(show_output=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After your model is deployed, make a call to the web service using [service.run()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice%28class%29?view=azure-ml-py#run-input-)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "input_payload = json.dumps({\n",
-    "    'data': dataset_x[0:2].tolist()\n",
-    "})\n",
-    "\n",
-    "output = service.run(input_payload)\n",
-    "\n",
-    "print(output)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When you are finished testing your service, clean up the deployment with [service.delete()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice%28class%29?view=azure-ml-py#delete--)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "service.delete()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Model Profiling\n",
-    "\n",
-    "Profile your model to understand how much CPU and memory the service, created as a result of its deployment, will need. Profiling returns information such as CPU usage, memory usage, and response latency. It also provides a CPU and memory recommendation based on the resource usage. You can profile your model (or more precisely the service built based on your model) on any CPU and/or memory combination where 0.1 <= CPU <= 3.5 and 0.1GB <= memory <= 15GB. If you do not provide a CPU and/or memory requirement, we will test it on the default configuration of 3.5 CPU and 15GB memory.\n",
-    "\n",
-    "In order to profile your model you will need:\n",
-    "- a registered model\n",
-    "- an entry script\n",
-    "- an inference configuration\n",
-    "- a single column tabular dataset, where each row contains a string representing sample request data sent to the service.\n",
-    "\n",
-    "Please, note that profiling is a long running operation and can take up to 25 minutes depending on the size of the dataset.\n",
-    "\n",
-    "At this point we only support profiling of services that expect their request data to be a string, for example: string serialized json, text, string serialized image, etc. The content of each row of the dataset (string) will be put into the body of the HTTP request and sent to the service encapsulating the model for scoring.\n",
-    "\n",
-    "Below is an example of how you can construct an input dataset to profile a service which expects its incoming requests to contain serialized json. In this case we created a dataset based one hundred instances of the same request data. In real world scenarios however, we suggest that you use larger datasets with various inputs, especially if your model resource usage/behavior is input dependent."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You may want to register datasets using the register() method to your workspace so they can be shared with others, reused and referred to by name in your script.\n",
-    "You can try get the dataset first to see if it's already registered."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core import Datastore\n",
-    "from azureml.core.dataset import Dataset\n",
-    "from azureml.data import dataset_type_definitions\n",
-    "\n",
-    "dataset_name='diabetes_sample_request_data'\n",
-    "\n",
-    "dataset_registered = False\n",
-    "try:\n",
-    "    sample_request_data = Dataset.get_by_name(workspace = ws, name = dataset_name)\n",
-    "    dataset_registered = True\n",
-    "except:\n",
-    "    print(\"The dataset {} is not registered in workspace yet.\".format(dataset_name))\n",
-    "\n",
-    "if not dataset_registered:\n",
-    "    # create a string that can be utf-8 encoded and\n",
-    "    # put in the body of the request\n",
-    "    serialized_input_json = json.dumps({\n",
-    "        'data': [\n",
-    "            [ 0.03807591,  0.05068012,  0.06169621, 0.02187235, -0.0442235,\n",
-    "            -0.03482076, -0.04340085, -0.00259226, 0.01990842, -0.01764613]\n",
-    "        ]\n",
-    "    })\n",
-    "    dataset_content = []\n",
-    "    for i in range(100):\n",
-    "        dataset_content.append(serialized_input_json)\n",
-    "    dataset_content = '\\n'.join(dataset_content)\n",
-    "    file_name = \"{}.txt\".format(dataset_name)\n",
-    "    f = open(file_name, 'w')\n",
-    "    f.write(dataset_content)\n",
-    "    f.close()\n",
-    "\n",
-    "    # upload the txt file created above to the Datastore and create a dataset from it\n",
-    "    data_store = Datastore.get_default(ws)\n",
-    "    data_store.upload_files(['./' + file_name], target_path='sample_request_data')\n",
-    "    datastore_path = [(data_store, 'sample_request_data' +'/' + file_name)]\n",
-    "    sample_request_data = Dataset.Tabular.from_delimited_files(\n",
-    "        datastore_path,\n",
-    "        separator='\\n',\n",
-    "        infer_column_types=True,\n",
-    "        header=dataset_type_definitions.PromoteHeadersBehavior.NO_HEADERS)\n",
-    "    sample_request_data = sample_request_data.register(workspace=ws,\n",
-    "                                                    name=dataset_name,\n",
-    "                                                    create_new_version=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have an input dataset we are ready to go ahead with profiling. In this case we are testing the previously introduced sklearn regression model on 1 CPU and 0.5 GB memory. The memory usage and recommendation presented in the result is measured in Gigabytes. The CPU usage and recommendation is measured in CPU cores."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "\n",
-    "\n",
-    "environment = Environment('my-sklearn-environment')\n",
-    "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
-    "    'azureml-defaults',\n",
-    "    'inference-schema[numpy-support]',\n",
-    "    'joblib',\n",
-    "    'numpy',\n",
-    "    'scikit-learn=={}'.format(sklearn.__version__)\n",
-    "])\n",
-    "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n",
-    "# if cpu and memory_in_gb parameters are not provided\n",
-    "# the model will be profiled on default configuration of\n",
-    "# 3.5CPU and 15GB memory\n",
-    "profile = Model.profile(ws,\n",
-    "            'rgrsn-%s' % datetime.now().strftime('%m%d%Y-%H%M%S'),\n",
-    "            [model],\n",
-    "            inference_config,\n",
-    "            input_dataset=sample_request_data,\n",
-    "            cpu=1.0,\n",
-    "            memory_in_gb=0.5)\n",
-    "\n",
-    "# profiling is a long running operation and may take up to 25 min\n",
-    "profile.wait_for_completion(True)\n",
-    "details = profile.get_details()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Model packaging\n",
-    "\n",
-    "If you want to build a Docker image that encapsulates your model and its dependencies, you can use the model packaging option. The output image will be pushed to your workspace's ACR.\n",
-    "\n",
-    "You must include an Environment object in your inference configuration to use `Model.package()`.\n",
-    "\n",
-    "```python\n",
-    "package = Model.package(ws, [model], inference_config)\n",
-    "package.wait_for_creation(show_output=True)  # Or show_output=False to hide the Docker build logs.\n",
-    "package.pull()\n",
-    "```\n",
-    "\n",
-    "Instead of a fully-built image, you can also generate a Dockerfile and download all the assets needed to build an image on top of your Environment.\n",
-    "\n",
-    "```python\n",
-    "package = Model.package(ws, [model], inference_config, generate_dockerfile=True)\n",
-    "package.wait_for_creation(show_output=True)\n",
-    "package.save(\"./local_context_dir\")\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Next steps\n",
-    "\n",
-    " - To run a production-ready web service, see the [notebook on deployment to Azure Kubernetes Service](../production-deploy-to-aks/production-deploy-to-aks.ipynb).\n",
-    " - To run a local web service, see the [notebook on deployment to a local Docker container](../deploy-to-local/register-model-deploy-local.ipynb).\n",
-    " - For more information on datasets, see the [notebook on training with datasets](../../work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb).\n",
-    " - For more information on environments, see the [notebook on using environments](../../training/using-environments/using-environments.ipynb).\n",
-    " - For information on all the available deployment targets, see [&ldquo;How and where to deploy models&rdquo;](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where#choose-a-compute-target)."
-   ]
-  }
- ],
- "metadata": {
-  "authors": [
-   {
-    "name": "vaidyas"
-   }
-  ],
-  "category": "deployment",
-  "compute": [
-   "None"
-  ],
-  "datasets": [
-   "Diabetes"
-  ],
-  "deployment": [
-   "Azure Container Instance"
-  ],
-  "exclude_from_index": false,
-  "framework": [
-   "Scikit-learn"
-  ],
-  "friendly_name": "Register model and deploy as webservice",
-  "index_order": 3,
-  "kernelspec": {
-   "display_name": "Python 3.6",
-   "language": "python",
-   "name": "python36"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  },
-  "star_tag": [
-   "featured"
-  ],
-  "tags": [
-   "None"
-  ],
-  "task": "Deploy a model with Azure Machine Learning"
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.yml

@@ -1,6 +0,0 @@
-name: model-register-and-deploy
-dependencies:
-- pip:
-  - azureml-sdk
-  - numpy
-  - scikit-learn

how-to-use-azureml/deployment/deploy-to-cloud/score.py

@@ -1,38 +0,0 @@
-import joblib
-import numpy as np
-import os
-
-from inference_schema.schema_decorators import input_schema, output_schema
-from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType
-
-
-# The init() method is called once, when the web service starts up.
-#
-# Typically you would deserialize the model file, as shown here using joblib,
-# and store it in a global variable so your run() method can access it later.
-def init():
-    global model
-
-    # The AZUREML_MODEL_DIR environment variable indicates
-    # a directory containing the model file you registered.
-    model_filename = 'sklearn_regression_model.pkl'
-    model_path = os.path.join(os.environ['AZUREML_MODEL_DIR'], model_filename)
-
-    model = joblib.load(model_path)
-
-
-# The run() method is called each time a request is made to the scoring API.
-#
-# Shown here are the optional input_schema and output_schema decorators
-# from the inference-schema pip package. Using these decorators on your
-# run() method parses and validates the incoming payload against
-# the example input you provide here. This will also generate a Swagger
-# API document for your web service.
-@input_schema('data', NumpyParameterType(np.array([[0.1, 1.2, 2.3, 3.4, 4.5, 5.6, 6.7, 7.8, 8.9, 9.0]])))
-@output_schema(NumpyParameterType(np.array([4429.929236457418])))
-def run(data):
-    # Use the model object loaded by init().
-    result = model.predict(data)
-
-    # You can return any JSON-serializable object.
-    return result.tolist()

how-to-use-azureml/deployment/deploy-to-local/README.md

@@ -1,12 +0,0 @@
-# Model Deployment with Azure ML service
-You can use Azure Machine Learning to package, debug, validate and deploy inference containers to a variety of compute targets. This process is known as "MLOps" (ML operationalization).
-For more information please check out this article: https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where
-
-## Get Started
-To begin, you will need an ML workspace.
-For more information please check out this article: https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace
-
-## Deploy locally
-You can deploy a model locally for testing & debugging using the Azure ML CLI or the Azure ML SDK.
-- CLI example: https://aka.ms/azmlcli
-- Notebook example: [register-model-deploy-local](./register-model-deploy-local.ipynb).

how-to-use-azureml/deployment/deploy-to-local/helloworld.txt

@@ -1 +0,0 @@
-RUN echo "this is test"

how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb

@@ -1,495 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-    "\n",
-    "Licensed under the MIT License."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Register model and deploy locally with advanced usages\n",
-    "\n",
-    "This example shows how to deploy a web service in step-by-step fashion:\n",
-    "\n",
-    " 1. Register model\n",
-    " 2. Deploy the image as a web service in a local Docker container.\n",
-    " 3. Quickly test changes to your entry script by reloading the local service.\n",
-    " 4. Optionally, you can also make changes to model, conda or extra_docker_file_steps and update local service"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prerequisites\n",
-    "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Check core SDK version number\n",
-    "import azureml.core\n",
-    "\n",
-    "print(\"SDK version:\", azureml.core.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialize Workspace\n",
-    "\n",
-    "Initialize a workspace object from persisted configuration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "create workspace"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "from azureml.core import Workspace\n",
-    "\n",
-    "ws = Workspace.from_config()\n",
-    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create trained model\n",
-    "\n",
-    "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset). "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import joblib\n",
-    "\n",
-    "from sklearn.datasets import load_diabetes\n",
-    "from sklearn.linear_model import Ridge\n",
-    "\n",
-    "dataset_x, dataset_y = load_diabetes(return_X_y=True)\n",
-    "\n",
-    "sk_model = Ridge().fit(dataset_x, dataset_y)\n",
-    "\n",
-    "joblib.dump(sk_model, \"sklearn_regression_model.pkl\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Register Model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can add tags and descriptions to your models. we are using `sklearn_regression_model.pkl` file in the current directory as a model with the name `sklearn_regression_model` in the workspace.\n",
-    "\n",
-    "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model, framework, category, target customer etc. Note that tags must be alphanumeric."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "register model from file",
-     "sample-model-register"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "from azureml.core.model import Model\n",
-    "\n",
-    "model = Model.register(model_path=\"sklearn_regression_model.pkl\",\n",
-    "                       model_name=\"sklearn_regression_model\",\n",
-    "                       tags={'area': \"diabetes\", 'type': \"regression\"},\n",
-    "                       description=\"Ridge regression model to predict diabetes\",\n",
-    "                       workspace=ws)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Manage your dependencies in a folder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "source_directory = \"source_directory\"\n",
-    "\n",
-    "os.makedirs(source_directory, exist_ok=True)\n",
-    "os.makedirs(os.path.join(source_directory, \"x/y\"), exist_ok=True)\n",
-    "os.makedirs(os.path.join(source_directory, \"env\"), exist_ok=True)\n",
-    "os.makedirs(os.path.join(source_directory, \"dockerstep\"), exist_ok=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Show `score.py`. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%writefile source_directory/x/y/score.py\n",
-    "import joblib\n",
-    "import json\n",
-    "import numpy as np\n",
-    "import os\n",
-    "\n",
-    "from inference_schema.schema_decorators import input_schema, output_schema\n",
-    "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n",
-    "\n",
-    "def init():\n",
-    "    global model\n",
-    "    # AZUREML_MODEL_DIR is an environment variable created during deployment. Join this path with the filename of the model file.\n",
-    "    # It holds the path to the directory that contains the deployed model (./azureml-models/$MODEL_NAME/$VERSION)\n",
-    "    # If there are multiple models, this value is the path to the directory containing all deployed models (./azureml-models)\n",
-    "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
-    "    # Deserialize the model file back into a sklearn model.\n",
-    "    model = joblib.load(model_path)\n",
-    "\n",
-    "    global name\n",
-    "    # Note here, the entire source directory from inference config gets added into image.\n",
-    "    # Below is an example of how you can use any extra files in image.\n",
-    "    with open('./source_directory/extradata.json') as json_file:\n",
-    "        data = json.load(json_file)\n",
-    "        name = data[\"people\"][0][\"name\"]\n",
-    "\n",
-    "input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n",
-    "output_sample = np.array([3726.995])\n",
-    "\n",
-    "@input_schema('data', NumpyParameterType(input_sample))\n",
-    "@output_schema(NumpyParameterType(output_sample))\n",
-    "def run(data):\n",
-    "    try:\n",
-    "        result = model.predict(data)\n",
-    "        # You can return any JSON-serializable object.\n",
-    "        return \"Hello \" + name + \" here is your result = \" + str(result)\n",
-    "    except Exception as e:\n",
-    "        error = str(e)\n",
-    "        return error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%writefile source_directory/extradata.json\n",
-    "{\n",
-    "    \"people\": [\n",
-    "        {\n",
-    "            \"website\": \"microsoft.com\", \n",
-    "            \"from\": \"Seattle\", \n",
-    "            \"name\": \"Mrudula\"\n",
-    "        }\n",
-    "    ]\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Inference Configuration\n",
-    "\n",
-    " - file_path: input parameter to Environment constructor. Manages conda and python package dependencies.\n",
-    " - env.docker.base_dockerfile: any extra steps you want to inject into docker file\n",
-    " - source_directory: holds source path as string, this entire folder gets added in image so its really easy to access any files within this folder or subfolder\n",
-    " - entry_script: contains logic specific to initializing your model and running predictions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sklearn\n",
-    "\n",
-    "from azureml.core.environment import Environment\n",
-    "from azureml.core.model import InferenceConfig\n",
-    "\n",
-    "\n",
-    "myenv = Environment('myenv')\n",
-    "myenv.python.conda_dependencies.add_pip_package(\"inference-schema[numpy-support]\")\n",
-    "myenv.python.conda_dependencies.add_pip_package(\"joblib\")\n",
-    "myenv.python.conda_dependencies.add_pip_package(\"scikit-learn=={}\".format(sklearn.__version__))\n",
-    "\n",
-    "# explicitly set base_image to None when setting base_dockerfile\n",
-    "myenv.docker.base_image = None\n",
-    "myenv.docker.base_dockerfile = \"FROM mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04\\nRUN echo \\\"this is test\\\"\"\n",
-    "myenv.inferencing_stack_version = \"latest\"\n",
-    "\n",
-    "inference_config = InferenceConfig(source_directory=source_directory,\n",
-    "                                   entry_script=\"x/y/score.py\",\n",
-    "                                   environment=myenv)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Deploy Model as a Local Docker Web Service\n",
-    "\n",
-    "*Make sure you have Docker installed and running.*\n",
-    "\n",
-    "Note that the service creation can take few minutes.\n",
-    "\n",
-    "NOTE:\n",
-    "\n",
-    "The Docker image runs as a Linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running:\n",
-    "\n",
-    "    # PowerShell command to switch to Linux engine\n",
-    "    & 'C:\\Program Files\\Docker\\Docker\\DockerCli.exe' -SwitchLinuxEngine"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "deploy service",
-     "aci"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "from azureml.core.webservice import LocalWebservice\n",
-    "\n",
-    "# This is optional, if not provided Docker will choose a random unused port.\n",
-    "deployment_config = LocalWebservice.deploy_configuration(port=6789)\n",
-    "\n",
-    "local_service = Model.deploy(ws, \"test\", [model], inference_config, deployment_config)\n",
-    "\n",
-    "local_service.wait_for_deployment()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print('Local service port: {}'.format(local_service.port))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Check Status and Get Container Logs\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(local_service.get_logs())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Test Web Service"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Call the web service with some input data to get a prediction."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "\n",
-    "sample_input = json.dumps({\n",
-    "    'data': dataset_x[0:2].tolist()\n",
-    "})\n",
-    "\n",
-    "print(local_service.run(sample_input))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reload Service\n",
-    "\n",
-    "You can update your score.py file and then call `reload()` to quickly restart the service. This will only reload your execution script and dependency files, it will not rebuild the underlying Docker image. As a result, `reload()` is fast, but if you do need to rebuild the image -- to add a new Conda or pip package, for instance -- you will have to call `update()`, instead (see below)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%writefile source_directory/x/y/score.py\n",
-    "import joblib\n",
-    "import json\n",
-    "import numpy as np\n",
-    "import os\n",
-    "\n",
-    "from inference_schema.schema_decorators import input_schema, output_schema\n",
-    "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n",
-    "\n",
-    "def init():\n",
-    "    global model\n",
-    "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
-    "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
-    "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
-    "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
-    "    # Deserialize the model file back into a sklearn model.\n",
-    "    model = joblib.load(model_path)\n",
-    "\n",
-    "    global name, from_location\n",
-    "    # Note here, the entire source directory from inference config gets added into image.\n",
-    "    # Below is an example of how you can use any extra files in image.\n",
-    "    with open('source_directory/extradata.json') as json_file:  \n",
-    "        data = json.load(json_file)\n",
-    "        name = data[\"people\"][0][\"name\"]\n",
-    "        from_location = data[\"people\"][0][\"from\"]\n",
-    "\n",
-    "input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n",
-    "output_sample = np.array([3726.995])\n",
-    "\n",
-    "@input_schema('data', NumpyParameterType(input_sample))\n",
-    "@output_schema(NumpyParameterType(output_sample))\n",
-    "def run(data):\n",
-    "    try:\n",
-    "        result = model.predict(data)\n",
-    "        # You can return any JSON-serializable object.\n",
-    "        return \"Hello \" + name + \" from \" + from_location + \" here is your result = \" + str(result)\n",
-    "    except Exception as e:\n",
-    "        error = str(e)\n",
-    "        return error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "local_service.reload()\n",
-    "print(\"--------------------------------------------------------------\")\n",
-    "\n",
-    "# After calling reload(), run() will return the updated message.\n",
-    "local_service.run(sample_input)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Update Service\n",
-    "\n",
-    "If you want to change your model(s), Conda dependencies, or deployment configuration, call `update()` to rebuild the Docker image.\n",
-    "\n",
-    "```python\n",
-    "\n",
-    "local_service.update(models=[SomeOtherModelObject],\n",
-    "                     deployment_config=local_config,\n",
-    "                     inference_config=inference_config)\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Delete Service"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "local_service.delete()"
-   ]
-  }
- ],
- "metadata": {
-  "authors": [
-   {
-    "name": "keriehm"
-   }
-  ],
-  "kernelspec": {
-   "display_name": "Python 3.6",
-   "language": "python",
-   "name": "python36"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb

@@ -1,556 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-    "\n",
-    "Licensed under the MIT License."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Register model and deploy locally\n",
-    "\n",
-    "This example shows how to deploy a web service in step-by-step fashion:\n",
-    "\n",
-    " 1. Register model\n",
-    " 2. Deploy the image as a web service in a local Docker container.\n",
-    " 3. Quickly test changes to your entry script by reloading the local service.\n",
-    " 4. Optionally, you can also make changes to model, conda or extra_docker_file_steps and update local service"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prerequisites\n",
-    "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Check core SDK version number\n",
-    "import azureml.core\n",
-    "\n",
-    "print(\"SDK version:\", azureml.core.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialize Workspace\n",
-    "\n",
-    "Initialize a workspace object from persisted configuration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core import Workspace\n",
-    "\n",
-    "ws = Workspace.from_config()\n",
-    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create trained model\n",
-    "\n",
-    "For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset). "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import joblib\n",
-    "\n",
-    "from sklearn.datasets import load_diabetes\n",
-    "from sklearn.linear_model import Ridge\n",
-    "\n",
-    "dataset_x, dataset_y = load_diabetes(return_X_y=True)\n",
-    "\n",
-    "sk_model = Ridge().fit(dataset_x, dataset_y)\n",
-    "\n",
-    "joblib.dump(sk_model, \"sklearn_regression_model.pkl\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Register Model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we are registering the serialized file `sklearn_regression_model.pkl` in the current directory as a model with the name `sklearn_regression_model` in the workspace.\n",
-    "\n",
-    "You can add tags and descriptions to your models. Using tags, you can track useful information such as the name and version of the machine learning library used to train the model, framework, category, target customer etc. Note that tags must be alphanumeric."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "register model from file"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "from azureml.core.model import Model\n",
-    "\n",
-    "model = Model.register(model_path=\"sklearn_regression_model.pkl\",\n",
-    "                       model_name=\"sklearn_regression_model\",\n",
-    "                       tags={'area': \"diabetes\", 'type': \"regression\"},\n",
-    "                       description=\"Ridge regression model to predict diabetes\",\n",
-    "                       workspace=ws)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Environment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sklearn\n",
-    "\n",
-    "from azureml.core.environment import Environment\n",
-    "\n",
-    "environment = Environment(\"LocalDeploy\")\n",
-    "environment.python.conda_dependencies.add_pip_package(\"inference-schema[numpy-support]\")\n",
-    "environment.python.conda_dependencies.add_pip_package(\"joblib\")\n",
-    "environment.python.conda_dependencies.add_pip_package(\"scikit-learn=={}\".format(sklearn.__version__))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Provide the Scoring Script\n",
-    "\n",
-    "This Python script handles the model execution inside the service container. The `init()` method loads the model file, and `run(data)` is called for every input to the service."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%writefile score.py\n",
-    "import joblib\n",
-    "import json\n",
-    "import numpy as np\n",
-    "import os\n",
-    "\n",
-    "from inference_schema.schema_decorators import input_schema, output_schema\n",
-    "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n",
-    "\n",
-    "def init():\n",
-    "    global model\n",
-    "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
-    "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
-    "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
-    "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
-    "    # Deserialize the model file back into a sklearn model.\n",
-    "    model = joblib.load(model_path)\n",
-    "\n",
-    "input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n",
-    "output_sample = np.array([3726.995])\n",
-    "\n",
-    "@input_schema('data', NumpyParameterType(input_sample))\n",
-    "@output_schema(NumpyParameterType(output_sample))\n",
-    "def run(data):\n",
-    "    try:\n",
-    "        result = model.predict(data)\n",
-    "        # You can return any JSON-serializable object.\n",
-    "        return result.tolist()\n",
-    "    except Exception as e:\n",
-    "        error = str(e)\n",
-    "        return error"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Inference Configuration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.model import InferenceConfig\n",
-    "\n",
-    "inference_config = InferenceConfig(entry_script=\"score.py\",\n",
-    "                                   environment=environment)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Deploy Model as a Local Docker Web Service\n",
-    "\n",
-    "*Make sure you have Docker installed and running.*\n",
-    "\n",
-    "Note that the service creation can take few minutes.\n",
-    "\n",
-    "NOTE:\n",
-    "\n",
-    "The Docker image runs as a Linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running:\n",
-    "\n",
-    "    # PowerShell command to switch to Linux engine\n",
-    "    & 'C:\\Program Files\\Docker\\Docker\\DockerCli.exe' -SwitchLinuxEngine"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "sample-localwebservice-deploy"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "from azureml.core.webservice import LocalWebservice\n",
-    "\n",
-    "# This is optional, if not provided Docker will choose a random unused port.\n",
-    "deployment_config = LocalWebservice.deploy_configuration(port=6789)\n",
-    "\n",
-    "local_service = Model.deploy(ws, \"test\", [model], inference_config, deployment_config)\n",
-    "\n",
-    "local_service.wait_for_deployment()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print('Local service port: {}'.format(local_service.port))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Check Status and Get Container Logs\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(local_service.get_logs())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Test Web Service"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Call the web service with some input data to get a prediction."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "\n",
-    "sample_input = json.dumps({\n",
-    "    'data': dataset_x[0:2].tolist()\n",
-    "})\n",
-    "\n",
-    "local_service.run(sample_input)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reload Service\n",
-    "\n",
-    "You can update your score.py file and then call `reload()` to quickly restart the service. This will only reload your execution script and dependency files, it will not rebuild the underlying Docker image. As a result, `reload()` is fast, but if you do need to rebuild the image -- to add a new Conda or pip package, for instance -- you will have to call `update()`, instead (see below)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%writefile score.py\n",
-    "import joblib\n",
-    "import json\n",
-    "import numpy as np\n",
-    "import os\n",
-    "\n",
-    "from inference_schema.schema_decorators import input_schema, output_schema\n",
-    "from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType\n",
-    "\n",
-    "def init():\n",
-    "    global model\n",
-    "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
-    "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
-    "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
-    "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
-    "    # Deserialize the model file back into a sklearn model.\n",
-    "    model = joblib.load(model_path)\n",
-    "\n",
-    "input_sample = np.array([[10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]])\n",
-    "output_sample = np.array([3726.995])\n",
-    "\n",
-    "@input_schema('data', NumpyParameterType(input_sample))\n",
-    "@output_schema(NumpyParameterType(output_sample))\n",
-    "def run(data):\n",
-    "    try:\n",
-    "        result = model.predict(data)\n",
-    "        # You can return any JSON-serializable object.\n",
-    "        return 'Hello from the updated score.py: ' + str(result.tolist())\n",
-    "    except Exception as e:\n",
-    "        error = str(e)\n",
-    "        return error"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "local_service.reload()\n",
-    "print(\"--------------------------------------------------------------\")\n",
-    "\n",
-    "# After calling reload(), run() will return the updated message.\n",
-    "local_service.run(sample_input)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Update Service\n",
-    "\n",
-    "If you want to change your model(s), Conda dependencies or deployment configuration, call `update()` to rebuild the Docker image.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "local_service.update(models=[model],\n",
-    "                     inference_config=inference_config,\n",
-    "                     deployment_config=deployment_config)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Deploy model to AKS cluster based on the LocalWebservice's configuration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This is a one time setup for AKS Cluster. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\n",
-    "from azureml.core.compute import AksCompute, ComputeTarget\n",
-    "from azureml.core.compute_target import ComputeTargetException\n",
-    "\n",
-    "# Choose a name for your AKS cluster\n",
-    "aks_name = 'my-aks-9' \n",
-    "\n",
-    "# Verify the cluster does not exist already\n",
-    "try:\n",
-    "    aks_target = ComputeTarget(workspace=ws, name=aks_name)\n",
-    "    print('Found existing cluster, use it.')\n",
-    "except ComputeTargetException:\n",
-    "    # Use the default configuration (can also provide parameters to customize)\n",
-    "    prov_config = AksCompute.provisioning_configuration()\n",
-    "\n",
-    "    # Create the cluster\n",
-    "    aks_target = ComputeTarget.create(workspace = ws, \n",
-    "                                      name = aks_name, \n",
-    "                                      provisioning_configuration = prov_config)\n",
-    "\n",
-    "if aks_target.get_status() != \"Succeeded\":\n",
-    "    aks_target.wait_for_completion(show_output=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.webservice import AksWebservice\n",
-    "# Set the web service configuration (using default here)\n",
-    "aks_config = AksWebservice.deploy_configuration()\n",
-    "\n",
-    "# # Enable token auth and disable (key) auth on the webservice\n",
-    "# aks_config = AksWebservice.deploy_configuration(token_auth_enabled=True, auth_enabled=False)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "aks_service_name ='aks-service-1'\n",
-    "\n",
-    "aks_service = local_service.deploy_to_cloud(name=aks_service_name,\n",
-    "                                            deployment_config=aks_config,\n",
-    "                                            deployment_target=aks_target)\n",
-    "\n",
-    "aks_service.wait_for_deployment(show_output = True)\n",
-    "print(aks_service.state)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Test aks service\n",
-    "\n",
-    "sample_input = json.dumps({\n",
-    "    'data': dataset_x[0:2].tolist()\n",
-    "})\n",
-    "\n",
-    "aks_service.run(sample_input)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Delete the service if not needed.\n",
-    "aks_service.delete()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Delete Service"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "local_service.delete()"
-   ]
-  }
- ],
- "metadata": {
-  "authors": [
-   {
-    "name": "keriehm"
-   }
-  ],
-  "category": "tutorial",
-  "compute": [
-   "Local"
-  ],
-  "datasets": [
-   "None"
-  ],
-  "deployment": [
-   "Local"
-  ],
-  "exclude_from_index": false,
-  "framework": [
-   "None"
-  ],
-  "friendly_name": "Register a model and deploy locally",
-  "index_order": 1,
-  "kernelspec": {
-   "display_name": "Python 3.6",
-   "language": "python",
-   "name": "python36"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
-  },
-  "star_tag": [],
-  "tags": [
-   "None"
-  ],
-  "task": "Deployment"
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb

@@ -1,371 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-    "\n",
-    "Licensed under the MIT License."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Deploy models to Azure Kubernetes Service (AKS) using controlled roll out\n",
-    "This notebook will show you how to deploy mulitple AKS webservices with the same scoring endpoint and how to roll out your models in a controlled manner by configuring % of scoring traffic going to each webservice. If you are using a Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) to install the Azure Machine Learning Python SDK and create an Azure ML Workspace."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Check for latest version\n",
-    "import azureml.core\n",
-    "print(azureml.core.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialize workspace\n",
-    "Create a [Workspace](https://docs.microsoft.com/python/api/azureml-core/azureml.core.workspace%28class%29?view=azure-ml-py) object from your persisted configuration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.workspace import Workspace\n",
-    "\n",
-    "ws = Workspace.from_config()\n",
-    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Register the model\n",
-    "Register a file or folder as a model by calling [Model.register()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#register-workspace--model-path--model-name--tags-none--properties-none--description-none--datasets-none--model-framework-none--model-framework-version-none--child-paths-none-).\n",
-    "In addition to the content of the model file itself, your registered model will also store model metadata -- model description, tags, and framework information -- that will be useful when managing and deploying models in your workspace. Using tags, for instance, you can categorize your models and apply filters when listing models in your workspace."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core import Model\n",
-    "\n",
-    "model = Model.register(workspace=ws,\n",
-    "                       model_name='sklearn_regression_model.pkl',                # Name of the registered model in your workspace.\n",
-    "                       model_path='./sklearn_regression_model.pkl',  # Local file to upload and register as a model.\n",
-    "                       model_framework=Model.Framework.SCIKITLEARN,  # Framework used to create the model.\n",
-    "                       model_framework_version='0.19.1',             # Version of scikit-learn used to create the model.\n",
-    "                       description='Ridge regression model to predict diabetes progression.',\n",
-    "                       tags={'area': 'diabetes', 'type': 'regression'})\n",
-    "\n",
-    "print('Name:', model.name)\n",
-    "print('Version:', model.version)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Register an environment (for all models)\n",
-    "\n",
-    "If you control over how your model is run, or if it has special runtime requirements, you can specify your own environment and scoring method.\n",
-    "\n",
-    "Specify the model's runtime environment by creating an [Environment](https://docs.microsoft.com/python/api/azureml-core/azureml.core.environment%28class%29?view=azure-ml-py) object and providing the [CondaDependencies](https://docs.microsoft.com/python/api/azureml-core/azureml.core.conda_dependencies.condadependencies?view=azure-ml-py) needed by your model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core import Environment\n",
-    "from azureml.core.conda_dependencies import CondaDependencies\n",
-    "\n",
-    "environment=Environment('my-sklearn-environment')\n",
-    "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
-    "    'azureml-defaults',\n",
-    "    'inference-schema[numpy-support]',\n",
-    "    'numpy',\n",
-    "    'scikit-learn==0.19.1',\n",
-    "    'scipy'\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When using a custom environment, you must also provide Python code for initializing and running your model. An example script is included with this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('score.py') as f:\n",
-    "    print(f.read())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create the InferenceConfig\n",
-    "Create the inference configuration to reference your environment and entry script during deployment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.model import InferenceConfig\n",
-    "\n",
-    "inference_config = InferenceConfig(entry_script='score.py', \n",
-    "                                   source_directory='.',\n",
-    "                                   environment=environment)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Provision the AKS Cluster\n",
-    "If you already have an AKS cluster attached to this workspace, skip the step below and provide the name of the cluster.\n",
-    "\n",
-    "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.compute import AksCompute\n",
-    "from azureml.core.compute import ComputeTarget\n",
-    "# Use the default configuration (can also provide parameters to customize)\n",
-    "prov_config = AksCompute.provisioning_configuration()\n",
-    "\n",
-    "aks_name = 'my-aks' \n",
-    "# Create the cluster\n",
-    "aks_target = ComputeTarget.create(workspace = ws, \n",
-    "                                  name = aks_name, \n",
-    "                                  provisioning_configuration = prov_config) \n",
-    "aks_target.wait_for_completion(show_output=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create an Endpoint and add a version (AKS service)\n",
-    "This creates a new endpoint and adds a version behind it. By default the first version added is the default version. You can specify the traffic percentile a version takes behind an endpoint. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# deploying the model and create a new endpoint\n",
-    "from azureml.core.webservice import AksEndpoint\n",
-    "# from azureml.core.compute import ComputeTarget\n",
-    "\n",
-    "#select a created compute\n",
-    "compute = ComputeTarget(ws, 'my-aks')\n",
-    "namespace_name=\"endpointnamespace\"\n",
-    "# define the endpoint name\n",
-    "endpoint_name = \"myendpoint1\"\n",
-    "# define the service name\n",
-    "version_name= \"versiona\"\n",
-    "\n",
-    "endpoint_deployment_config = AksEndpoint.deploy_configuration(tags = {'modelVersion':'firstversion', 'department':'finance'}, \n",
-    "                                                              description = \"my first version\", namespace = namespace_name, \n",
-    "                                                              version_name = version_name, traffic_percentile = 40)\n",
-    "\n",
-    "endpoint = Model.deploy(ws, endpoint_name, [model], inference_config, endpoint_deployment_config, compute)\n",
-    "endpoint.wait_for_deployment(True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "endpoint.get_logs()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Add another version of the service to an existing endpoint\n",
-    "This adds another version behind an existing endpoint. You can specify the traffic percentile the new version takes. If no traffic_percentile is specified then it defaults to 0. All the unspecified traffic percentile (in this example 50) across all versions goes to default version."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Adding a new version to an existing Endpoint.\n",
-    "version_name_add=\"versionb\" \n",
-    "\n",
-    "endpoint.create_version(version_name = version_name_add, inference_config=inference_config, models=[model], tags = {'modelVersion':'secondversion', 'department':'finance'}, \n",
-    "                        description = \"my second version\", traffic_percentile = 10)\n",
-    "endpoint.wait_for_deployment(True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Update an existing version in an endpoint\n",
-    "There are two types of versions: control and treatment. An endpoint contains one or more treatment versions but only one control version. This categorization helps compare the different versions against the defined control version."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "endpoint.update_version(version_name=endpoint.versions[version_name_add].name, description=\"my second version update\", traffic_percentile=40, is_default=True, is_control_version_type=True)\n",
-    "endpoint.wait_for_deployment(True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Test the web service using run method\n",
-    "Test the web sevice by passing in data. Run() method retrieves API keys behind the scenes to make sure that call is authenticated."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Scoring on endpoint\n",
-    "import json\n",
-    "test_sample = json.dumps({'data': [\n",
-    "    [1,2,3,4,5,6,7,8,9,10], \n",
-    "    [10,9,8,7,6,5,4,3,2,1]\n",
-    "]})\n",
-    "\n",
-    "test_sample_encoded = bytes(test_sample, encoding='utf8')\n",
-    "prediction = endpoint.run(input_data=test_sample_encoded)\n",
-    "print(prediction)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Delete Resources"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# deleting a version in an endpoint\n",
-    "endpoint.delete_version(version_name=version_name)\n",
-    "endpoint.wait_for_deployment(True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# deleting an endpoint, this will delete all versions in the endpoint and the endpoint itself\n",
-    "endpoint.delete()"
-   ]
-  }
- ],
- "metadata": {
-  "authors": [
-   {
-    "name": "shipatel"
-   }
-  ],
-  "category": "deployment",
-  "compute": [
-   "None"
-  ],
-  "datasets": [
-   "Diabetes"
-  ],
-  "deployment": [
-   "Azure Kubernetes Service"
-  ],
-  "exclude_from_index": false,
-  "framework": [
-   "Scikit-learn"
-  ],
-  "friendly_name": "Deploy models to AKS using controlled roll out",
-  "index_order": 3,
-  "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": "3.6.0"
-  },
-  "star_tag": [
-   "featured"
-  ],
-  "tags": [
-   "None"
-  ],
-  "task": "Deploy a model with Azure Machine Learning"
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.yml

@@ -1,4 +0,0 @@
-name: deploy-aks-with-controlled-rollout
-dependencies:
-- pip:
-  - azureml-sdk

how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb

@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "# Enabling App Insights for Services in Production\n",
-    "With this notebook, you can learn how to enable App Insights for standard service monitoring, plus, we provide examples for doing custom logging within a scoring files in a model.\n",
+    "With this notebook, you can learn how to enable App Insights for standard service monitoring, plus, we provide examples for doing custom logging within a scoring files in a model. \n",
     "\n",
     "\n",
     "## What does Application Insights monitor?\n",
@@ -22,14 +22,7 @@
     "If you want to log custom traces, you will follow the standard deplyment process for AKS and you will:\n",
     "1. Update scoring file.\n",
     "2. Update aks configuration.\n",
-    "3. Deploy the model with this new configuration. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.png)"
+    "3. Build new image and deploy it. "
    ]
   },
   {
@@ -45,13 +38,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import azureml.core\n",
-    "import json\n",
-    "\n",
-    "from azureml.core import Workspace\n",
+    "from azureml.core import Workspace, Run\n",
     "from azureml.core.compute import AksCompute, ComputeTarget\n",
-    "from azureml.core.webservice import AksWebservice\n",
+    "from azureml.core.webservice import Webservice, AksWebservice\n",
+    "from azureml.core.image import Image\n",
+    "from azureml.core.model import Model\n",
     "\n",
+    "import azureml.core\n",
     "print(azureml.core.VERSION)"
    ]
   },
@@ -59,7 +52,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 2. Set up your configuration and create a workspace\n"
+    "## 2. Set up your configuration and create a workspace\n",
+    "Follow Notebook 00 instructions to do this.\n"
    ]
   },
   {
@@ -69,7 +63,7 @@
    "outputs": [],
    "source": [
     "ws = Workspace.from_config()\n",
-    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
+    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
    ]
   },
   {
@@ -86,13 +80,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from azureml.core import Model\n",
-    "\n",
-    "model = Model.register(model_path=\"sklearn_regression_model.pkl\", # This points to a local file.\n",
-    "                       model_name=\"sklearn_regression_model.pkl\", # This is the name the model is registered as.\n",
-    "                       tags={'area': \"diabetes\", 'type': \"regression\"},\n",
-    "                       description=\"Ridge regression model to predict diabetes\",\n",
-    "                       workspace=ws)\n",
+    "#Register the model\n",
+    "from azureml.core.model import Model\n",
+    "model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n",
+    "                       model_name = \"sklearn_regression_model.pkl\", # this is the name the model is registered as\n",
+    "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
+    "                       description = \"Ridge regression model to predict diabetes\",\n",
+    "                       workspace = ws)\n",
     "\n",
     "print(model.name, model.description, model.version)"
    ]
@@ -109,7 +103,8 @@
     "\n",
     "### b. In your run function add:\n",
     "```python\n",
-    "print (\"Prediction created\" + time.strftime(\"%H:%M:%S\"))```"
+    "print (\"saving input data\" + time.strftime(\"%H:%M:%S\"))\n",
+    "print (\"saving prediction data\" + time.strftime(\"%H:%M:%S\"))```"
    ]
   },
   {
@@ -119,36 +114,54 @@
    "outputs": [],
    "source": [
     "%%writefile score.py\n",
-    "import os\n",
     "import pickle\n",
     "import json\n",
-    "import numpy\n",
+    "import numpy \n",
     "from sklearn.externals import joblib\n",
     "from sklearn.linear_model import Ridge\n",
+    "from azureml.core.model import Model\n",
+    "from azureml.monitoring import ModelDataCollector\n",
     "import time\n",
     "\n",
     "def init():\n",
     "    global model\n",
     "    #Print statement for appinsights custom traces:\n",
     "    print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))\n",
-    "\n",
-    "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
-    "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
-    "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
-    "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
-    "\n",
+    "    \n",
+    "    # note here \"sklearn_regression_model.pkl\" is the name of the model registered under the workspace\n",
+    "    # this call should return the path to the model.pkl file on the local disk.\n",
+    "    model_path = Model.get_model_path(model_name = 'sklearn_regression_model.pkl')\n",
+    "    \n",
     "    # deserialize the model file back into a sklearn model\n",
     "    model = joblib.load(model_path)\n",
-    "\n",
-    "\n",
+    "    \n",
+    "    global inputs_dc, prediction_dc\n",
+    "    \n",
+    "    # this setup will help us save our inputs under the \"inputs\" path in our Azure Blob\n",
+    "    inputs_dc = ModelDataCollector(model_name=\"sklearn_regression_model\", identifier=\"inputs\", feature_names=[\"feat1\", \"feat2\"]) \n",
+    "    \n",
+    "    # this setup will help us save our ipredictions under the \"predictions\" path in our Azure Blob\n",
+    "    prediction_dc = ModelDataCollector(\"sklearn_regression_model\", identifier=\"predictions\", feature_names=[\"prediction1\", \"prediction2\"]) \n",
+    "  \n",
     "# note you can pass in multiple rows for scoring\n",
     "def run(raw_data):\n",
+    "    global inputs_dc, prediction_dc\n",
     "    try:\n",
     "        data = json.loads(raw_data)['data']\n",
     "        data = numpy.array(data)\n",
     "        result = model.predict(data)\n",
-    "        print (\"Prediction created\" + time.strftime(\"%H:%M:%S\"))\n",
-    "        # you can return any datatype as long as it is JSON-serializable\n",
+    "        \n",
+    "        #Print statement for appinsights custom traces:\n",
+    "        print (\"saving input data\" + time.strftime(\"%H:%M:%S\"))\n",
+    "        \n",
+    "        #this call is saving our input data into our blob\n",
+    "        inputs_dc.collect(data) \n",
+    "        #this call is saving our prediction data into our blob\n",
+    "        prediction_dc.collect(result)\n",
+    "        \n",
+    "        #Print statement for appinsights custom traces:\n",
+    "        print (\"saving prediction data\" + time.strftime(\"%H:%M:%S\"))\n",
+    "        # you can return any data type as long as it is JSON-serializable\n",
     "        return result.tolist()\n",
     "    except Exception as e:\n",
     "        error = str(e)\n",
@@ -160,8 +173,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 5. *Create myenv.yml file*\n",
-    "Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service."
+    "## 5. *Create myenv.yml file*"
    ]
   },
   {
@@ -170,10 +182,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "from azureml.core.conda_dependencies import CondaDependencies \n",
     "\n",
-    "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn==0.20.3'],\n",
-    "                                 pip_packages=['azureml-defaults'])\n",
+    "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
     "\n",
     "with open(\"myenv.yml\",\"w\") as f:\n",
     "    f.write(myenv.serialize_to_string())"
@@ -183,7 +194,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 6. Create Inference Configuration"
+    "## 6. Create your new Image"
    ]
   },
   {
@@ -192,68 +203,22 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from azureml.core.environment import Environment\n",
-    "from azureml.core.model import InferenceConfig\n",
+    "from azureml.core.image import ContainerImage\n",
     "\n",
-    "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
-    "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Deploy to ACI (Optional)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.webservice import AciWebservice\n",
+    "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
+    "                                                  runtime = \"python\",\n",
+    "                                                  conda_file = \"myenv.yml\",\n",
+    "                                                  description = \"Image with ridge regression model\",\n",
+    "                                                  tags = {'area': \"diabetes\", 'type': \"regression\"}\n",
+    "                                                 )\n",
     "\n",
-    "aci_deployment_config = AciWebservice.deploy_configuration(cpu_cores=1,\n",
-    "                                                           memory_gb=1,\n",
-    "                                                           tags={'area': \"diabetes\", 'type': \"regression\"},\n",
-    "                                                           description=\"Predict diabetes using regression model\",\n",
-    "                                                           enable_app_insights=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "aci_service_name = \"aci-service-appinsights\"\n",
+    "image = ContainerImage.create(name = \"myimage1\",\n",
+    "                              # this is the model object\n",
+    "                              models = [model],\n",
+    "                              image_config = image_config,\n",
+    "                              workspace = ws)\n",
     "\n",
-    "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aci_deployment_config, overwrite=True)\n",
-    "aci_service.wait_for_deployment(show_output=True)\n",
-    "\n",
-    "print(aci_service.state)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if aci_service.state == \"Healthy\":\n",
-    "    test_sample = json.dumps({\n",
-    "        \"data\": [\n",
-    "            [1,28,13,45,54,6,57,8,8,10],\n",
-    "            [101,9,8,37,6,45,4,3,2,41]\n",
-    "        ]\n",
-    "    })\n",
-    "\n",
-    "    prediction = aci_service.run(test_sample)\n",
-    "\n",
-    "    print(prediction)\n",
-    "else:\n",
-    "    raise ValueError(\"Service deployment isn't healthy, can't call the service. Error: \", aci_service.error)"
+    "image.wait_for_creation(show_output = True)"
    ]
   },
   {
@@ -267,9 +232,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Create AKS compute if you haven't done so.\n",
-    "\n",
-    "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
+    "### Create AKS compute if you haven't done so (Notebook 11)"
    ]
   },
   {
@@ -278,24 +241,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from azureml.core.compute import ComputeTarget, AksCompute\n",
-    "from azureml.core.compute_target import ComputeTargetException\n",
+    "# Use the default configuration (can also provide parameters to customize)\n",
+    "prov_config = AksCompute.provisioning_configuration()\n",
     "\n",
-    "aks_name = \"my-aks-insights\"\n",
-    "\n",
-    "creating_compute = False\n",
-    "try:\n",
-    "    aks_target = ComputeTarget(ws, aks_name)\n",
-    "    print(\"Using existing AKS compute target {}.\".format(aks_name))\n",
-    "except ComputeTargetException:\n",
-    "    print(\"Creating a new AKS compute target {}.\".format(aks_name))\n",
-    "\n",
-    "    # Use the default configuration (can also provide parameters to customize).\n",
-    "    prov_config = AksCompute.provisioning_configuration()\n",
-    "    aks_target = ComputeTarget.create(workspace=ws,\n",
-    "                                      name=aks_name,\n",
-    "                                      provisioning_configuration=prov_config)\n",
-    "    creating_compute = True"
+    "aks_name = 'my-aks-test2' \n",
+    "# Create the cluster\n",
+    "aks_target = ComputeTarget.create(workspace = ws, \n",
+    "                                  name = aks_name, \n",
+    "                                  provisioning_configuration = prov_config)"
    ]
   },
   {
@@ -305,16 +258,7 @@
    "outputs": [],
    "source": [
     "%%time\n",
-    "if creating_compute and aks_target.provisioning_state != \"Succeeded\":\n",
-    "    aks_target.wait_for_completion(show_output=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "aks_target.wait_for_completion(show_output = True)\n",
     "print(aks_target.provisioning_state)\n",
     "print(aks_target.provisioning_errors)"
    ]
@@ -330,13 +274,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```python\n",
+    "```python \n",
     "%%time\n",
     "resource_id = '/subscriptions/<subscriptionid>/resourcegroups/<resourcegroupname>/providers/Microsoft.ContainerService/managedClusters/<aksservername>'\n",
     "create_name= 'myaks4'\n",
     "attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
-    "aks_target = ComputeTarget.attach(workspace=ws,\n",
-    "                                  name=create_name,\n",
+    "aks_target = ComputeTarget.attach(workspace = ws, \n",
+    "                                  name = create_name, \n",
     "                                  attach_configuration=attach_config)\n",
     "## Wait for the operation to complete\n",
     "aks_target.wait_for_provisioning(True)```"
@@ -356,8 +300,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Set the web service configuration.\n",
-    "aks_deployment_config = AksWebservice.deploy_configuration(enable_app_insights=True)"
+    "#Set the web service configuration\n",
+    "aks_config = AksWebservice.deploy_configuration(enable_app_insights=True)"
    ]
   },
   {
@@ -373,19 +317,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "if aks_target.provisioning_state == \"Succeeded\":\n",
-    "    aks_service_name = \"aks-service-appinsights\"\n",
-    "    aks_service = Model.deploy(ws,\n",
-    "                               aks_service_name,\n",
-    "                               [model],\n",
-    "                               inference_config,\n",
-    "                               aks_deployment_config,\n",
-    "                               deployment_target=aks_target,\n",
-    "                               overwrite=True)\n",
-    "    aks_service.wait_for_deployment(show_output=True)\n",
-    "    print(aks_service.state)\n",
-    "else:\n",
-    "    raise ValueError(\"AKS cluster provisioning failed. Error: \", aks_target.provisioning_errors)"
+    "%%time\n",
+    "aks_service_name ='aks-w-dc3'\n",
+    "\n",
+    "aks_service = Webservice.deploy_from_image(workspace = ws, \n",
+    "                                           name = aks_service_name,\n",
+    "                                           image = image,\n",
+    "                                           deployment_config = aks_config,\n",
+    "                                           deployment_target = aks_target\n",
+    "                                           )\n",
+    "aks_service.wait_for_deployment(show_output = True)\n",
+    "print(aks_service.state)"
    ]
   },
   {
@@ -402,19 +344,16 @@
    "outputs": [],
    "source": [
     "%%time\n",
+    "import json\n",
     "\n",
-    "if aks_service.state == \"Healthy\":\n",
-    "    test_sample = json.dumps({\n",
-    "        \"data\": [\n",
-    "            [1,28,13,45,54,6,57,8,8,10],\n",
-    "            [101,9,8,37,6,45,4,3,2,41]\n",
-    "        ]\n",
-    "    })\n",
+    "test_sample = json.dumps({'data': [\n",
+    "    [1,28,13,45,54,6,57,8,8,10], \n",
+    "    [101,9,8,37,6,45,4,3,2,41]\n",
+    "]})\n",
+    "test_sample = bytes(test_sample,encoding='utf8')\n",
     "\n",
-    "    prediction = aks_service.run(input_data=test_sample)\n",
-    "    print(prediction)\n",
-    "else:\n",
-    "    raise ValueError(\"Service deployment isn't healthy, can't call the service. Error: \", aks_service.error)"
+    "prediction = aks_service.run(input_data=test_sample)\n",
+    "print(prediction)"
    ]
   },
   {
@@ -443,36 +382,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "aks_service.update(enable_app_insights=False)\n",
-    "aks_service.wait_for_deployment(show_output=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Clean up"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "aks_service.delete()\n",
-    "aci_service.delete()\n",
-    "model.delete()\n",
-    "if creating_compute:\n",
-    "    aks_target.delete()"
+    "aks_service.update(enable_app_insights=False)"
    ]
   }
  ],
  "metadata": {
   "authors": [
    {
-    "name": "gopalv"
+    "name": "marthalc"
    }
   ],
   "kernelspec": {
@@ -490,7 +407,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.6"
   }
  },
  "nbformat": 4,

how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.yml

@@ -1,4 +0,0 @@
-name: enable-app-insights-in-production-service
-dependencies:
-- pip:
-  - azureml-sdk

how-to-use-azureml/deployment/enable-data-collection-for-models-in-aks/enable-data-collection-for-models-in-aks.ipynb

@@ -0,0 +1,454 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Enabling Data Collection for Models in Production\n",
+    "With this notebook, you can learn how to collect input model data from your Azure Machine Learning service in an Azure Blob storage. Once enabled, this data collected gives you the opportunity:\n",
+    "\n",
+    "* Monitor data drifts as production data enters your model\n",
+    "* Make better decisions on when to retrain or optimize your model\n",
+    "* Retrain your model with the data collected\n",
+    "\n",
+    "## What data is collected?\n",
+    "* Model input data (voice, images, and video are not supported) from services deployed in Azure Kubernetes Cluster (AKS)\n",
+    "* Model predictions using production input data.\n",
+    "\n",
+    "**Note:** pre-aggregation or pre-calculations on this data are done by user and not included in this version of the product.\n",
+    "\n",
+    "## What is different compared to standard production deployment process?\n",
+    "1. Update scoring file.\n",
+    "2. Update yml file with new dependency.\n",
+    "3. Update aks configuration.\n",
+    "4. Build new image and deploy it. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Import your dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace, Run\n",
+    "from azureml.core.compute import AksCompute, ComputeTarget\n",
+    "from azureml.core.webservice import Webservice, AksWebservice\n",
+    "from azureml.core.image import Image\n",
+    "from azureml.core.model import Model\n",
+    "\n",
+    "import azureml.core\n",
+    "print(azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Set up your configuration and create a workspace\n",
+    "Follow Notebook 00 instructions to do this.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Register Model\n",
+    "Register an existing trained model, add descirption and tags."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Register the model\n",
+    "from azureml.core.model import Model\n",
+    "model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n",
+    "                       model_name = \"sklearn_regression_model.pkl\", # this is the name the model is registered as\n",
+    "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
+    "                       description = \"Ridge regression model to predict diabetes\",\n",
+    "                       workspace = ws)\n",
+    "\n",
+    "print(model.name, model.description, model.version)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. *Update your scoring file with Data Collection*\n",
+    "The file below, compared to the file used in notebook 11, has the following changes:\n",
+    "### a. Import the module\n",
+    "```python \n",
+    "from azureml.monitoring import ModelDataCollector```\n",
+    "### b. In your init function add:\n",
+    "```python \n",
+    "global inputs_dc, prediction_d\n",
+    "inputs_dc = ModelDataCollector(\"best_model\", identifier=\"inputs\", feature_names=[\"feat1\", \"feat2\", \"feat3\", \"feat4\", \"feat5\", \"Feat6\"])\n",
+    "prediction_dc = ModelDataCollector(\"best_model\", identifier=\"predictions\", feature_names=[\"prediction1\", \"prediction2\"])```\n",
+    "    \n",
+    "* Identifier: Identifier is later used for building the folder structure in your Blob, it can be used to divide \"raw\" data versus \"processed\".\n",
+    "* CorrelationId: is an optional parameter, you do not need to set it up if your model doesn't require it. Having a correlationId in place does help you for easier mapping with other data. (Examples include: LoanNumber, CustomerId, etc.)\n",
+    "* Feature Names: These need to be set up in the order of your features in order for them to have column names when the .csv is created.\n",
+    "\n",
+    "### c. In your run function add:\n",
+    "```python\n",
+    "inputs_dc.collect(data)\n",
+    "prediction_dc.collect(result)```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile score.py\n",
+    "import pickle\n",
+    "import json\n",
+    "import numpy \n",
+    "from sklearn.externals import joblib\n",
+    "from sklearn.linear_model import Ridge\n",
+    "from azureml.core.model import Model\n",
+    "from azureml.monitoring import ModelDataCollector\n",
+    "import time\n",
+    "\n",
+    "def init():\n",
+    "    global model\n",
+    "    print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))\n",
+    "    # note here \"sklearn_regression_model.pkl\" is the name of the model registered under the workspace\n",
+    "    # this call should return the path to the model.pkl file on the local disk.\n",
+    "    model_path = Model.get_model_path(model_name = 'sklearn_regression_model.pkl')\n",
+    "    # deserialize the model file back into a sklearn model\n",
+    "    model = joblib.load(model_path)\n",
+    "    global inputs_dc, prediction_dc\n",
+    "    # this setup will help us save our inputs under the \"inputs\" path in our Azure Blob\n",
+    "    inputs_dc = ModelDataCollector(model_name=\"sklearn_regression_model\", identifier=\"inputs\", feature_names=[\"feat1\", \"feat2\"]) \n",
+    "    # this setup will help us save our ipredictions under the \"predictions\" path in our Azure Blob\n",
+    "    prediction_dc = ModelDataCollector(\"sklearn_regression_model\", identifier=\"predictions\", feature_names=[\"prediction1\", \"prediction2\"]) \n",
+    "  \n",
+    "# note you can pass in multiple rows for scoring\n",
+    "def run(raw_data):\n",
+    "    global inputs_dc, prediction_dc\n",
+    "    try:\n",
+    "        data = json.loads(raw_data)['data']\n",
+    "        data = numpy.array(data)\n",
+    "        result = model.predict(data)\n",
+    "        print (\"saving input data\" + time.strftime(\"%H:%M:%S\"))\n",
+    "        inputs_dc.collect(data) #this call is saving our input data into our blob\n",
+    "        prediction_dc.collect(result)#this call is saving our prediction data into our blob\n",
+    "        print (\"saving prediction data\" + time.strftime(\"%H:%M:%S\"))\n",
+    "        # you can return any data type as long as it is JSON-serializable\n",
+    "        return result.tolist()\n",
+    "    except Exception as e:\n",
+    "        error = str(e)\n",
+    "        print (error + time.strftime(\"%H:%M:%S\"))\n",
+    "        return error"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. *Update your myenv.yml file with the required module*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.conda_dependencies import CondaDependencies \n",
+    "\n",
+    "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
+    "myenv.add_pip_package(\"azureml-monitoring\")\n",
+    "\n",
+    "with open(\"myenv.yml\",\"w\") as f:\n",
+    "    f.write(myenv.serialize_to_string())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Create your new Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.image import ContainerImage\n",
+    "\n",
+    "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
+    "                                                  runtime = \"python\",\n",
+    "                                                  conda_file = \"myenv.yml\",\n",
+    "                                                  description = \"Image with ridge regression model\",\n",
+    "                                                  tags = {'area': \"diabetes\", 'type': \"regression\"}\n",
+    "                                                 )\n",
+    "\n",
+    "image = ContainerImage.create(name = \"myimage1\",\n",
+    "                              # this is the model object\n",
+    "                              models = [model],\n",
+    "                              image_config = image_config,\n",
+    "                              workspace = ws)\n",
+    "\n",
+    "image.wait_for_creation(show_output = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(model.name, model.description, model.version)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Deploy to AKS service"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create AKS compute if you haven't done so (Notebook 11)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use the default configuration (can also provide parameters to customize)\n",
+    "prov_config = AksCompute.provisioning_configuration()\n",
+    "\n",
+    "aks_name = 'my-aks-test1' \n",
+    "# Create the cluster\n",
+    "aks_target = ComputeTarget.create(workspace = ws, \n",
+    "                                  name = aks_name, \n",
+    "                                  provisioning_configuration = prov_config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "aks_target.wait_for_completion(show_output = True)\n",
+    "print(aks_target.provisioning_state)\n",
+    "print(aks_target.provisioning_errors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you already have a cluster you can attach the service to it:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "scrolled": true
+   },
+   "source": [
+    "```python \n",
+    "    %%time\n",
+    "    resource_id = '/subscriptions/<subscriptionid>/resourcegroups/<resourcegroupname>/providers/Microsoft.ContainerService/managedClusters/<aksservername>'\n",
+    "    create_name= 'myaks4'\n",
+    "    attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
+    "    aks_target = ComputeTarget.attach(workspace = ws, \n",
+    "                                      name = create_name, \n",
+    "                                      attach_configuration=attach_config)\n",
+    "    ## Wait for the operation to complete\n",
+    "    aks_target.wait_for_provisioning(True)```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### a. *Activate Data Collection and App Insights through updating AKS Webservice configuration*\n",
+    "In order to enable Data Collection and App Insights in your service you will need to update your AKS configuration file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Set the web service configuration\n",
+    "aks_config = AksWebservice.deploy_configuration(collect_model_data=True, enable_app_insights=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### b. Deploy your service"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "aks_service_name ='aks-w-dc2'\n",
+    "\n",
+    "aks_service = Webservice.deploy_from_image(workspace = ws, \n",
+    "                                           name = aks_service_name,\n",
+    "                                           image = image,\n",
+    "                                           deployment_config = aks_config,\n",
+    "                                           deployment_target = aks_target\n",
+    "                                           )\n",
+    "aks_service.wait_for_deployment(show_output = True)\n",
+    "print(aks_service.state)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 8. Test your service and send some data\n",
+    "**Note**: It will take around 15 mins for your data to appear in your blob.\n",
+    "The data will appear in your Azure Blob following this format:\n",
+    "\n",
+    "/modeldata/subscriptionid/resourcegroupname/workspacename/webservicename/modelname/modelversion/identifier/year/month/day/data.csv "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "import json\n",
+    "\n",
+    "test_sample = json.dumps({'data': [\n",
+    "    [1,2,3,4,54,6,7,8,88,10], \n",
+    "    [10,9,8,37,36,45,4,33,2,1]\n",
+    "]})\n",
+    "test_sample = bytes(test_sample,encoding = 'utf8')\n",
+    "\n",
+    "prediction = aks_service.run(input_data = test_sample)\n",
+    "print(prediction)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 9. Validate you data and analyze it\n",
+    "You can look into your data following this path format in your Azure Blob (it takes up to 15 minutes for the data to appear):\n",
+    "\n",
+    "/modeldata/**subscriptionid>**/**resourcegroupname>**/**workspacename>**/**webservicename>**/**modelname>**/**modelversion>>**/**identifier>**/*year/month/day*/data.csv \n",
+    "\n",
+    "For doing further analysis you have multiple options:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### a. Create DataBricks cluter and connect it to your blob\n",
+    "https://docs.microsoft.com/en-us/azure/azure-databricks/quickstart-create-databricks-workspace-portal or in your databricks workspace you can look for the template \"Azure Blob Storage Import Example Notebook\".\n",
+    "\n",
+    "\n",
+    "Here is an example for setting up the file location to extract the relevant data:\n",
+    "\n",
+    "<code> file_location = \"wasbs://mycontainer@storageaccountname.blob.core.windows.net/unknown/unknown/unknown-bigdataset-unknown/my_iterate_parking_inputs/2018/&deg;/&deg;/data.csv\" \n",
+    "file_type = \"csv\"</code>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### b. Connect Blob to Power Bi (Small Data only)\n",
+    "1. Download and Open PowerBi Desktop\n",
+    "2. Select “Get Data” and click on “Azure Blob Storage” >> Connect\n",
+    "3. Add your storage account and enter your storage key.\n",
+    "4. Select the container where your Data Collection is stored and click on Edit. \n",
+    "5. In the query editor, click under “Name” column and add your Storage account Model path into the filter. Note: if you want to only look into files from a specific year or month, just expand the filter path. For example, just look into March data: /modeldata/subscriptionid>/resourcegroupname>/workspacename>/webservicename>/modelname>/modelversion>/identifier>/year>/3\n",
+    "6. Click on the double arrow aside the “Content” column to combine the files. \n",
+    "7. Click OK and the data will preload.\n",
+    "8. You can now click Close and Apply and start building your custom reports on your Model Input data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Disable Data Collection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aks_service.update(collect_model_data=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "marthalc"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

how-to-use-azureml/deployment/onnx/Dockerfile

@@ -1,2 +0,0 @@
-RUN apt-get update
-RUN apt-get install -y libgomp1

how-to-use-azureml/deployment/onnx/README.md

@@ -1,23 +1,14 @@
-# ONNX on Azure Machine Learning
+# ONNX on Azure Machine Learning
 
-These tutorials show how to create and deploy Open Neural Network eXchange ([ONNX](http://onnx.ai)) models in Azure Machine Learning environments using [ONNX Runtime](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-build-deploy-onnx) for inference. Once deployed as a web service, you can ping the model with your own set of images to be analyzed!
+These tutorials show how to create and deploy [ONNX](http://onnx.ai) models in Azure Machine Learning environments using [ONNX Runtime](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-build-deploy-onnx) for inference. Once deployed as a web service, you can ping the model with your own set of images to be analyzed!
 
 ## Tutorials
+- [Obtain ONNX model from ONNX Model Zoo and deploy with ONNX Runtime inference - Handwritten Digit Classification (MNIST)](https://github.com/Azure/MachineLearningNotebooks/blob/master/onnx/onnx-inference-mnist-deploy.ipynb)
+- [Obtain ONNX model from ONNX Model Zoo and deploy with ONNX Runtime inference - Facial Expression Recognition (Emotion FER+)](https://github.com/Azure/MachineLearningNotebooks/blob/master/onnx/onnx-inference-facial-emotion-recognition-deploy.ipynb)
+- [Obtain ONNX model from ONNX Model Zoo and deploy with ONNX Runtime inference - Image Recognition (ResNet50)](https://github.com/Azure/MachineLearningNotebooks/blob/master/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb)
+- [Convert ONNX model from CoreML and deploy - TinyYOLO](https://github.com/Azure/MachineLearningNotebooks/blob/master/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb)
+- [Train ONNX model in PyTorch and deploy - MNIST](https://github.com/Azure/MachineLearningNotebooks/blob/master/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb)
 
-0. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, [Configure your Azure Machine Learning Workspace](../../../configuration.ipynb)
-
-#### Obtain pretrained models from the [ONNX Model Zoo](https://github.com/onnx/models) and deploy with ONNX Runtime
-1. [MNIST - Handwritten Digit Classification with ONNX Runtime](onnx-inference-mnist-deploy.ipynb)
-2. [Emotion FER+ - Facial Expression Recognition with ONNX Runtime](onnx-inference-facial-expression-recognition-deploy.ipynb)
-
-#### Train model on Azure ML, convert to ONNX, and deploy with ONNX Runtime
-3. [MNIST - Train using PyTorch and deploy with ONNX Runtime](onnx-train-pytorch-aml-deploy-mnist.ipynb)
-
-#### Demo Notebooks from Microsoft Ignite 2018
-Note that the following notebooks do not have evaluation sections for the models since they were deployed as part of a live demo. You can find the respective pre-processing and post-processing code linked from the ONNX Model Zoo Github pages ([ResNet](https://github.com/onnx/models/tree/master/models/image_classification/resnet), [TinyYoloV2](https://github.com/onnx/models/tree/master/tiny_yolov2)), or experiment with the ONNX models by [running them in the browser](https://microsoft.github.io/onnxjs-demo/#/).
-
-4. [ResNet50 - Image Recognition with ONNX Runtime](onnx-modelzoo-aml-deploy-resnet50.ipynb)
-5. [TinyYoloV2 - Convert from CoreML and deploy with ONNX Runtime](onnx-convert-aml-deploy-tinyyolo.ipynb)
 
 ## Documentation
 - [ONNX Runtime Python API Documentation](http://aka.ms/onnxruntime-python)
@@ -25,15 +16,13 @@ Note that the following notebooks do not have evaluation sections for the models
 
 ## Related Articles
 - [Building and Deploying ONNX Runtime Models](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-build-deploy-onnx)
-- [Azure AI – Making AI Real for Business](https://aka.ms/aml-blog-overview)
+- [Azure AI – Making AI Real for Business](https://aka.ms/aml-blog-overview) 
 - [What’s new in Azure Machine Learning](https://aka.ms/aml-blog-whats-new)
 
+
 ## License
 Copyright (c) Microsoft Corporation. All rights reserved.  
 Licensed under the MIT License.
 
 ## Acknowledgements
 These tutorials were developed by Vinitra Swamy and Prasanth Pulavarthi of the Microsoft AI Frameworks team and adapted for presentation at Microsoft Ignite 2018.
-
-
- ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/README.png)

how-to-use-azureml/deployment/onnx/mnist.py

@@ -1,135 +0,0 @@
-# This is a modified version of https://github.com/pytorch/examples/blob/master/mnist/main.py which is
-# licensed under BSD 3-Clause (https://github.com/pytorch/examples/blob/master/LICENSE)
-
-from __future__ import print_function
-import argparse
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.optim as optim
-from torchvision import datasets, transforms
-import os
-
-
-class Net(nn.Module):
-    def __init__(self):
-        super(Net, self).__init__()
-        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
-        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
-        self.conv2_drop = nn.Dropout2d()
-        self.fc1 = nn.Linear(320, 50)
-        self.fc2 = nn.Linear(50, 10)
-
-    def forward(self, x):
-        x = F.relu(F.max_pool2d(self.conv1(x), 2))
-        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
-        x = x.view(-1, 320)
-        x = F.relu(self.fc1(x))
-        x = F.dropout(x, training=self.training)
-        x = self.fc2(x)
-        return F.log_softmax(x, dim=1)
-
-
-def train(args, model, device, train_loader, optimizer, epoch, output_dir):
-    model.train()
-    for batch_idx, (data, target) in enumerate(train_loader):
-        data, target = data.to(device), target.to(device)
-        optimizer.zero_grad()
-        output = model(data)
-        loss = F.nll_loss(output, target)
-        loss.backward()
-        optimizer.step()
-        if batch_idx % args.log_interval == 0:
-            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
-                epoch, batch_idx * len(data), len(train_loader.dataset),
-                100. * batch_idx / len(train_loader), loss.item()))
-
-
-def test(args, model, device, test_loader):
-    model.eval()
-    test_loss = 0
-    correct = 0
-    with torch.no_grad():
-        for data, target in test_loader:
-            data, target = data.to(device), target.to(device)
-            output = model(data)
-            test_loss += F.nll_loss(output, target, size_average=False, reduce=True).item()  # sum up batch loss
-            pred = output.max(1, keepdim=True)[1]  # get the index of the max log-probability
-            correct += pred.eq(target.view_as(pred)).sum().item()
-
-    test_loss /= len(test_loader.dataset)
-    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
-        test_loss, correct, len(test_loader.dataset),
-        100. * correct / len(test_loader.dataset)))
-
-
-def main():
-    # Training settings
-    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
-    parser.add_argument('--batch-size', type=int, default=64, metavar='N',
-                        help='input batch size for training (default: 64)')
-    parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
-                        help='input batch size for testing (default: 1000)')
-    parser.add_argument('--epochs', type=int, default=5, metavar='N',
-                        help='number of epochs to train (default: 5)')
-    parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
-                        help='learning rate (default: 0.01)')
-    parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
-                        help='SGD momentum (default: 0.5)')
-    parser.add_argument('--no-cuda', action='store_true', default=False,
-                        help='disables CUDA training')
-    parser.add_argument('--seed', type=int, default=1, metavar='S',
-                        help='random seed (default: 1)')
-    parser.add_argument('--log-interval', type=int, default=10, metavar='N',
-                        help='how many batches to wait before logging training status')
-    parser.add_argument('--output-dir', type=str, default='outputs')
-    args = parser.parse_args()
-    use_cuda = not args.no_cuda and torch.cuda.is_available()
-
-    torch.manual_seed(args.seed)
-
-    device = torch.device("cuda" if use_cuda else "cpu")
-
-    output_dir = args.output_dir
-    os.makedirs(output_dir, exist_ok=True)
-
-    kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
-    # Use Azure Open Datasets for MNIST dataset
-    datasets.MNIST.resources = [
-        ("https://azureopendatastorage.azurefd.net/mnist/train-images-idx3-ubyte.gz",
-         "f68b3c2dcbeaaa9fbdd348bbdeb94873"),
-        ("https://azureopendatastorage.azurefd.net/mnist/train-labels-idx1-ubyte.gz",
-         "d53e105ee54ea40749a09fcbcd1e9432"),
-        ("https://azureopendatastorage.azurefd.net/mnist/t10k-images-idx3-ubyte.gz",
-         "9fb629c4189551a2d022fa330f9573f3"),
-        ("https://azureopendatastorage.azurefd.net/mnist/t10k-labels-idx1-ubyte.gz",
-         "ec29112dd5afa0611ce80d1b7f02629c")
-    ]
-    train_loader = torch.utils.data.DataLoader(
-        datasets.MNIST('data', train=True, download=True,
-                       transform=transforms.Compose([transforms.ToTensor(),
-                                                    transforms.Normalize((0.1307,), (0.3081,))])
-                       ),
-        batch_size=args.batch_size, shuffle=True, **kwargs)
-    test_loader = torch.utils.data.DataLoader(
-        datasets.MNIST('data', train=False,
-                       transform=transforms.Compose([transforms.ToTensor(),
-                                                    transforms.Normalize((0.1307,), (0.3081,))])
-                       ),
-        batch_size=args.test_batch_size, shuffle=True, **kwargs)
-
-    model = Net().to(device)
-    optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
-
-    for epoch in range(1, args.epochs + 1):
-        train(args, model, device, train_loader, optimizer, epoch, output_dir)
-        test(args, model, device, test_loader)
-
-    # save model
-    dummy_input = torch.randn(1, 1, 28, 28, device=device)
-    model_path = os.path.join(output_dir, 'mnist.onnx')
-    torch.onnx.export(model, dummy_input, model_path)
-
-
-if __name__ == '__main__':
-    main()

how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb

@@ -9,13 +9,6 @@
     "Licensed under the MIT License."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.png)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -40,7 +33,7 @@
     "To make the best use of your time, make sure you have done the following:\n",
     "\n",
     "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
-    "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook to:\n",
+    "* Go through the [00.configuration.ipynb](../00.configuration.ipynb) notebook to:\n",
     "    * install the AML SDK\n",
     "    * create a workspace and its configuration file (config.json)"
    ]
@@ -78,7 +71,7 @@
    "source": [
     "## Convert model to ONNX\n",
     "\n",
-    "First we download the CoreML model. We use the CoreML model from [Matthijs Hollemans's tutorial](https://github.com/hollance/YOLO-CoreML-MPSNNGraph). This may take a few minutes."
+    "First we download the CoreML model. We use the CoreML model listed at https://coreml.store/tinyyolo. This may take a few minutes."
    ]
   },
   {
@@ -89,8 +82,8 @@
    "source": [
     "import urllib.request\n",
     "\n",
-    "coreml_model_url = \"https://github.com/hollance/YOLO-CoreML-MPSNNGraph/raw/master/TinyYOLO-CoreML/TinyYOLO-CoreML/TinyYOLO.mlmodel\"\n",
-    "urllib.request.urlretrieve(coreml_model_url, filename=\"TinyYOLO.mlmodel\")\n"
+    "onnx_model_url = \"https://s3-us-west-2.amazonaws.com/coreml-models/TinyYOLO.mlmodel\"\n",
+    "urllib.request.urlretrieve(onnx_model_url, filename=\"TinyYOLO.mlmodel\")\n"
    ]
   },
   {
@@ -115,11 +108,6 @@
     "# Convert from CoreML into ONNX\n",
     "onnx_model = onnxmltools.convert_coreml(coreml_model, 'TinyYOLOv2')\n",
     "\n",
-    "# Fix the preprocessor bias in the ImageScaler\n",
-    "for init in onnx_model.graph.initializer:\n",
-    "    if init.name == 'scalerPreprocessor_bias':\n",
-    "        init.dims[1] = 1\n",
-    "\n",
     "# Save ONNX model\n",
     "onnxmltools.utils.save_model(onnx_model, 'tinyyolov2.onnx')\n",
     "\n",
@@ -248,8 +236,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Setting up inference configuration\n",
-    "First we create a YAML file that specifies which dependencies we would like to see in our container. Please note that you must include azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service."
+    "### Create container image\n",
+    "First we create a YAML file that specifies which dependencies we would like to see in our container."
    ]
   },
   {
@@ -260,7 +248,7 @@
    "source": [
     "from azureml.core.conda_dependencies import CondaDependencies \n",
     "\n",
-    "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n",
+    "myenv = CondaDependencies.create(pip_packages=[\"numpy\",\"onnxruntime\",\"azureml-core\"])\n",
     "\n",
     "with open(\"myenv.yml\",\"w\") as f:\n",
     "    f.write(myenv.serialize_to_string())"
@@ -270,7 +258,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Then we create the inference configuration."
+    "Then we have Azure ML create the container. This step will likely take a few minutes."
    ]
   },
   {
@@ -279,19 +267,47 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from azureml.core.model import InferenceConfig\n",
-    "from azureml.core.environment import Environment\n",
+    "from azureml.core.image import ContainerImage\n",
+    "\n",
+    "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
+    "                                                  runtime = \"python\",\n",
+    "                                                  conda_file = \"myenv.yml\",\n",
+    "                                                  description = \"TinyYOLO ONNX Demo\",\n",
+    "                                                  tags = {\"demo\": \"onnx\"}\n",
+    "                                                 )\n",
     "\n",
     "\n",
-    "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
-    "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
+    "image = ContainerImage.create(name = \"onnxyolo\",\n",
+    "                              models = [model],\n",
+    "                              image_config = image_config,\n",
+    "                              workspace = ws)\n",
+    "\n",
+    "image.wait_for_creation(show_output = True)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Deploy the model"
+    "In case you need to debug your code, the next line of code accesses the log file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(image.image_build_log_uri)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We're all set! Let's get our model chugging.\n",
+    "\n",
+    "### Deploy the container image"
    ]
   },
   {
@@ -312,7 +328,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The following cell will take a few minutes to run as the model gets packaged up and deployed to ACI."
+    "The following cell will likely take a few minutes to run as well."
    ]
   },
   {
@@ -321,9 +337,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "aci_service_name = 'my-aci-service-tiny-yolo'\n",
+    "from azureml.core.webservice import Webservice\n",
+    "from random import randint\n",
+    "\n",
+    "aci_service_name = 'onnx-tinyyolo'+str(randint(0,100))\n",
     "print(\"Service\", aci_service_name)\n",
-    "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
+    "\n",
+    "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
+    "                                           image = image,\n",
+    "                                           name = aci_service_name,\n",
+    "                                           workspace = ws)\n",
+    "\n",
     "aci_service.wait_for_deployment(True)\n",
     "print(aci_service.state)"
    ]
@@ -378,32 +402,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "aci_service.delete()"
+    "#aci_service.delete()"
    ]
   }
  ],
  "metadata": {
   "authors": [
    {
-    "name": "viswamy"
+    "name": "onnx"
    }
   ],
-  "category": "deployment",
-  "compute": [
-   "local"
-  ],
-  "datasets": [
-   "PASCAL VOC"
-  ],
-  "deployment": [
-   "Azure Container Instance"
-  ],
-  "exclude_from_index": false,
-  "framework": [
-   "ONNX"
-  ],
-  "friendly_name": "Convert and deploy TinyYolo with ONNX Runtime",
-  "index_order": 5,
   "kernelspec": {
    "display_name": "Python 3.6",
    "language": "python",
@@ -419,15 +427,8 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  },
-  "star_tag": [
-   "featured"
-  ],
-  "tags": [
-   "ONNX Converter"
-  ],
-  "task": "Object Detection"
+   "version": "3.5.6"
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 2

how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.yml

@@ -1,8 +0,0 @@
-name: onnx-convert-aml-deploy-tinyyolo
-dependencies:
-- pip:
-  - azureml-sdk
-  - numpy
-  - git+https://github.com/apple/coremltools@v2.1
-  - onnx<1.7.0
-  - onnxmltools

how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb

@@ -8,13 +8,6 @@
     "Licensed under the MIT License."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.png)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -41,7 +34,7 @@
     "## Prerequisites\n",
     "\n",
     "### 1. Install Azure ML SDK and create a new workspace\n",
-    "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, please follow [Azure ML configuration notebook](../../../configuration.ipynb) to set up your environment.\n",
+    "Please follow [Azure ML configuration notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) to set up your environment.\n",
     "\n",
     "### 2. Install additional packages needed for this Notebook\n",
     "You need to install the popular plotting library `matplotlib`, the image manipulation library `opencv`, and the `onnx` library in the conda environment where Azure Maching Learning SDK is installed.\n",
@@ -54,7 +47,7 @@
     "\n",
     "### 3. Download sample data and pre-trained ONNX model from ONNX Model Zoo.\n",
     "\n",
-    "In the following lines of code, we download [the trained ONNX Emotion FER+ model and corresponding test data](https://github.com/onnx/models/tree/master/vision/body_analysis/emotion_ferplus) and place them in the same folder as this tutorial notebook. For more information about the FER+ dataset, please visit Microsoft Researcher Emad Barsoum's [FER+ source data repository](https://github.com/ebarsoum/FERPlus)."
+    "In the following lines of code, we download [the trained ONNX Emotion FER+ model and corresponding test data](https://github.com/onnx/models/tree/master/emotion_ferplus) and place them in the same folder as this tutorial notebook. For more information about the FER+ dataset, please visit Microsoft Researcher Emad Barsoum's [FER+ source data repository](https://github.com/ebarsoum/FERPlus)."
    ]
   },
   {
@@ -70,16 +63,16 @@
     "\n",
     "import urllib.request\n",
     "\n",
-    "onnx_model_url = \"https://github.com/onnx/models/blob/main/vision/body_analysis/emotion_ferplus/model/emotion-ferplus-7.tar.gz?raw=true\"\n",
+    "onnx_model_url = \"https://www.cntk.ai/OnnxModels/emotion_ferplus/opset_7/emotion_ferplus.tar.gz\"\n",
     "\n",
-    "urllib.request.urlretrieve(onnx_model_url, filename=\"emotion-ferplus-7.tar.gz\")\n",
+    "urllib.request.urlretrieve(onnx_model_url, filename=\"emotion_ferplus.tar.gz\")\n",
     "\n",
     "# the ! magic command tells our jupyter notebook kernel to run the following line of \n",
     "# code from the command line instead of the notebook kernel\n",
     "\n",
     "# We use tar and xvcf to unzip the files we just retrieved from the ONNX model zoo\n",
     "\n",
-    "!tar xvzf emotion-ferplus-7.tar.gz"
+    "!tar xvzf emotion_ferplus.tar.gz"
    ]
   },
   {
@@ -176,7 +169,7 @@
    "source": [
     "### ONNX FER+ Model Methodology\n",
     "\n",
-    "The image classification model we are using is pre-trained using Microsoft's deep learning cognitive toolkit, [CNTK](https://github.com/Microsoft/CNTK), from the [ONNX model zoo](http://github.com/onnx/models). The model zoo has many other models that can be deployed on cloud providers like AzureML without any additional training. To ensure that our cloud deployed model works, we use testing data from the well-known FER+ data set, provided as part of the [trained Emotion Recognition model](https://github.com/onnx/models/tree/master/vision/body_analysis/emotion_ferplus) in the ONNX model zoo.\n",
+    "The image classification model we are using is pre-trained using Microsoft's deep learning cognitive toolkit, [CNTK](https://github.com/Microsoft/CNTK), from the [ONNX model zoo](http://github.com/onnx/models). The model zoo has many other models that can be deployed on cloud providers like AzureML without any additional training. To ensure that our cloud deployed model works, we use testing data from the well-known FER+ data set, provided as part of the [trained Emotion Recognition model](https://github.com/onnx/models/tree/master/emotion_ferplus) in the ONNX model zoo.\n",
     "\n",
     "The original Facial Emotion Recognition (FER) Dataset was released in 2013 by Pierre-Luc Carrier and Aaron Courville as part of a [Kaggle Competition](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data), but some of the labels are not entirely appropriate for the expression. In the FER+ Dataset, each photo was evaluated by at least 10 croud sourced reviewers, creating a more accurate basis for ground truth. \n",
     "\n",
@@ -204,6 +197,7 @@
    "source": [
     "# for images and plots in this notebook\n",
     "import matplotlib.pyplot as plt  \n",
+    "from IPython.display import Image\n",
     "\n",
     "# display images inline\n",
     "%matplotlib inline"
@@ -256,11 +250,12 @@
     "import onnxruntime\n",
     "import sys\n",
     "import os\n",
+    "from azureml.core.model import Model\n",
     "import time\n",
     "\n",
     "def init():\n",
     "    global session, input_name, output_name\n",
-    "    model = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'model.onnx')\n",
+    "    model = Model.get_model_path(model_name = 'onnx_emotion')\n",
     "    session = onnxruntime.InferenceSession(model, None)\n",
     "    input_name = session.get_inputs()[0].name\n",
     "    output_name = session.get_outputs()[0].name \n",
@@ -319,8 +314,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Write Environment File\n",
-    "Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service."
+    "### Write Environment File"
    ]
   },
   {
@@ -331,8 +325,7 @@
    "source": [
     "from azureml.core.conda_dependencies import CondaDependencies \n",
     "\n",
-    "\n",
-    "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n",
+    "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\"])\n",
     "\n",
     "with open(\"myenv.yml\",\"w\") as f:\n",
     "    f.write(myenv.serialize_to_string())"
@@ -342,7 +335,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Setup inference configuration"
+    "### Create the Container Image\n",
+    "\n",
+    "This step will likely take a few minutes."
    ]
   },
   {
@@ -351,19 +346,47 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from azureml.core.model import InferenceConfig\n",
-    "from azureml.core.environment import Environment\n",
+    "from azureml.core.image import ContainerImage\n",
+    "\n",
+    "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
+    "                                                  runtime = \"python\",\n",
+    "                                                  conda_file = \"myenv.yml\",\n",
+    "                                                  description = \"Emotion ONNX Runtime container\",\n",
+    "                                                  tags = {\"demo\": \"onnx\"})\n",
     "\n",
     "\n",
-    "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
-    "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
+    "image = ContainerImage.create(name = \"onnximage\",\n",
+    "                              # this is the model object\n",
+    "                              models = [model],\n",
+    "                              image_config = image_config,\n",
+    "                              workspace = ws)\n",
+    "\n",
+    "image.wait_for_creation(show_output = True)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Deploy the model"
+    "In case you need to debug your code, the next line of code accesses the log file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(image.image_build_log_uri)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We're all done specifying what we want our virtual machine to do. Let's configure and deploy our container image.\n",
+    "\n",
+    "### Deploy the container image"
    ]
   },
   {
@@ -380,26 +403,33 @@
     "                                               description = 'ONNX for emotion recognition model')"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following cell will likely take a few minutes to run as well."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
+    "from azureml.core.webservice import Webservice\n",
+    "\n",
     "aci_service_name = 'onnx-demo-emotion'\n",
     "print(\"Service\", aci_service_name)\n",
-    "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
+    "\n",
+    "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
+    "                                           image = image,\n",
+    "                                           name = aci_service_name,\n",
+    "                                           workspace = ws)\n",
+    "\n",
     "aci_service.wait_for_deployment(True)\n",
     "print(aci_service.state)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following cell will likely take a few minutes to run as well."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -433,7 +463,7 @@
     "\n",
     "### Useful Helper Functions\n",
     "\n",
-    "We preprocess and postprocess our data (see score.py file) using the helper functions specified in the [ONNX FER+ Model page in the Model Zoo repository](https://github.com/onnx/models/tree/master/vision/body_analysis/emotion_ferplus)."
+    "We preprocess and postprocess our data (see score.py file) using the helper functions specified in the [ONNX FER+ Model page in the Model Zoo repository](https://github.com/onnx/models/tree/master/emotion_ferplus)."
    ]
   },
   {
@@ -451,8 +481,8 @@
     "    \n",
     "    emotion_keys = list(emotion_table.keys())\n",
     "    emotions = []\n",
-    "    for c in range(N):\n",
-    "        emotions.append(emotion_keys[classes[c]])\n",
+    "    for i in range(N):\n",
+    "        emotions.append(emotion_keys[classes[i]])\n",
     "    return emotions\n",
     "\n",
     "def softmax(x):\n",
@@ -504,9 +534,9 @@
     "# read in 3 testing images from .pb files\n",
     "test_data_size = 3\n",
     "\n",
-    "for num in np.arange(test_data_size):\n",
-    "    input_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(num), 'input_0.pb')\n",
-    "    output_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(num), 'output_0.pb')\n",
+    "for i in np.arange(test_data_size):\n",
+    "    input_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(i), 'input_0.pb')\n",
+    "    output_test_data = os.path.join(model_dir, 'test_data_set_{0}'.format(i), 'output_0.pb')\n",
     "    \n",
     "    # convert protobuf tensors to np arrays using the TensorProto reader from ONNX\n",
     "    tensor = onnx.TensorProto()\n",
@@ -570,7 +600,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.figure(figsize = (16, 6))\n",
+    "plt.figure(figsize = (16, 6), frameon=False)\n",
     "plt.subplot(1, 8, 1)\n",
     "\n",
     "plt.text(x = 0, y = -30, s = \"True Label: \", fontsize = 13, color = 'black')\n",
@@ -641,19 +671,19 @@
     "    \"\"\"Convert the input image into grayscale\"\"\"\n",
     "    return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])\n",
     "\n",
-    "def resize_img(img_to_resize):\n",
-    "    \"\"\"Resize image to FER+ model input dimensions\"\"\"\n",
-    "    r_img = cv2.resize(img_to_resize, dsize=(64, 64), interpolation=cv2.INTER_AREA)\n",
-    "    r_img.resize((1, 1, 64, 64))\n",
-    "    return r_img\n",
+    "def resize_img(img):\n",
+    "    \"\"\"Resize image to MNIST model input dimensions\"\"\"\n",
+    "    img = cv2.resize(img, dsize=(64, 64), interpolation=cv2.INTER_AREA)\n",
+    "    img.resize((1, 1, 64, 64))\n",
+    "    return img\n",
     "\n",
-    "def preprocess(img_to_preprocess):\n",
+    "def preprocess(img):\n",
     "    \"\"\"Resize input images and convert them to grayscale.\"\"\"\n",
-    "    if img_to_preprocess.shape == (64, 64):\n",
-    "        img_to_preprocess.resize((1, 1, 64, 64))\n",
-    "        return img_to_preprocess\n",
+    "    if img.shape == (64, 64):\n",
+    "        img.resize((1, 1, 64, 64))\n",
+    "        return img\n",
     "    \n",
-    "    grayscale = rgb2gray(img_to_preprocess)\n",
+    "    grayscale = rgb2gray(img)\n",
     "    processed_img = resize_img(grayscale)\n",
     "    return processed_img"
    ]
@@ -702,7 +732,7 @@
     "        r = json.loads(aci_service.run(input_data))\n",
     "        result = r['result'][0]\n",
     "        time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n",
-    "    except KeyError as e:\n",
+    "    except Exception as e:\n",
     "        print(str(e))\n",
     "\n",
     "    plt.figure(figsize = (16, 6))\n",
@@ -726,7 +756,7 @@
    "source": [
     "# remember to delete your service after you are done using it!\n",
     "\n",
-    "aci_service.delete()"
+    "# aci_service.delete()"
    ]
   },
   {
@@ -743,7 +773,7 @@
     "- ensured that your deep learning model is working perfectly (in the cloud) on test data, and checked it against some of your own!\n",
     "\n",
     "Next steps:\n",
-    "- If you have not already, check out another interesting ONNX/AML application that lets you set up a state-of-the-art [handwritten image classification model (MNIST)](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb) in the cloud! This tutorial deploys a pre-trained ONNX Computer Vision model for handwritten digit classification in an Azure ML virtual machine.\n",
+    "- If you have not already, check out another interesting ONNX/AML application that lets you set up a state-of-the-art [handwritten image classification model (MNIST)](https://github.com/Azure/MachineLearningNotebooks/tree/master/onnx/onnx-inference-mnist.ipynb) in the cloud! This tutorial deploys a pre-trained ONNX Computer Vision model for handwritten digit classification in an Azure ML virtual machine.\n",
     "- Keep an eye out for an updated version of this tutorial that uses ONNX Runtime GPU.\n",
     "- Contribute to our [open source ONNX repository on github](http://github.com/onnx/onnx) and/or add to our [ONNX model zoo](http://github.com/onnx/models)"
    ]
@@ -755,22 +785,6 @@
     "name": "viswamy"
    }
   ],
-  "category": "deployment",
-  "compute": [
-   "Local"
-  ],
-  "datasets": [
-   "Emotion FER"
-  ],
-  "deployment": [
-   "Azure Container Instance"
-  ],
-  "exclude_from_index": false,
-  "framework": [
-   "ONNX"
-  ],
-  "friendly_name": "Deploy Facial Expression Recognition (FER+) with ONNX Runtime",
-  "index_order": 2,
   "kernelspec": {
    "display_name": "Python 3.6",
    "language": "python",
@@ -786,14 +800,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.6"
   },
-  "msauthor": "vinitra.swamy",
-  "star_tag": [],
-  "tags": [
-   "ONNX Model Zoo"
-  ],
-  "task": "Facial Expression Recognition"
+  "msauthor": "vinitra.swamy"
  },
  "nbformat": 4,
  "nbformat_minor": 2

how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.yml

@@ -1,9 +0,0 @@
-name: onnx-inference-facial-expression-recognition-deploy
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-widgets
-  - matplotlib
-  - numpy
-  - onnx<1.7.0
-  - opencv-python-headless

how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb

@@ -8,13 +8,6 @@
     "Licensed under the MIT License."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.png)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -41,7 +34,7 @@
     "## Prerequisites\n",
     "\n",
     "### 1. Install Azure ML SDK and create a new workspace\n",
-    "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, please follow [Azure ML configuration notebook](../../../configuration.ipynb) to set up your environment.\n",
+    "Please follow [Azure ML configuration notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) to set up your environment.\n",
     "\n",
     "### 2. Install additional packages needed for this tutorial notebook\n",
     "You need to install the popular plotting library `matplotlib`, the image manipulation library `opencv`, and the `onnx` library in the conda environment where Azure Maching Learning SDK is installed. \n",
@@ -54,7 +47,7 @@
     "\n",
     "### 3. Download sample data and pre-trained ONNX model from ONNX Model Zoo.\n",
     "\n",
-    "In the following lines of code, we download [the trained ONNX MNIST model and corresponding test data](https://github.com/onnx/models/tree/master/vision/classification/mnist) and place them in the same folder as this tutorial notebook. For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/)."
+    "In the following lines of code, we download [the trained ONNX MNIST model and corresponding test data](https://github.com/onnx/models/tree/master/mnist) and place them in the same folder as this tutorial notebook. For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/)."
    ]
   },
   {
@@ -70,9 +63,9 @@
     "\n",
     "import urllib.request\n",
     "\n",
-    "onnx_model_url = \"https://github.com/onnx/models/blob/main/vision/classification/mnist/model/mnist-7.tar.gz?raw=true\"\n",
+    "onnx_model_url = \"https://www.cntk.ai/OnnxModels/mnist/opset_7/mnist.tar.gz\"\n",
     "\n",
-    "urllib.request.urlretrieve(onnx_model_url, filename=\"mnist-7.tar.gz\")"
+    "urllib.request.urlretrieve(onnx_model_url, filename=\"mnist.tar.gz\")"
    ]
   },
   {
@@ -86,7 +79,7 @@
     "\n",
     "# We use tar and xvcf to unzip the files we just retrieved from the ONNX model zoo\n",
     "\n",
-    "!tar xvzf mnist-7.tar.gz"
+    "!tar xvzf mnist.tar.gz"
    ]
   },
   {
@@ -187,7 +180,7 @@
    "source": [
     "### ONNX MNIST Model Methodology\n",
     "\n",
-    "The image classification model we are using is pre-trained using Microsoft's deep learning cognitive toolkit, [CNTK](https://github.com/Microsoft/CNTK), from the [ONNX model zoo](http://github.com/onnx/models). The model zoo has many other models that can be deployed on cloud providers like AzureML without any additional training. To ensure that our cloud deployed model works, we use testing data from the famous MNIST data set, provided as part of the [trained MNIST model](https://github.com/onnx/models/tree/master/vision/classification/mnist) in the ONNX model zoo.\n",
+    "The image classification model we are using is pre-trained using Microsoft's deep learning cognitive toolkit, [CNTK](https://github.com/Microsoft/CNTK), from the [ONNX model zoo](http://github.com/onnx/models). The model zoo has many other models that can be deployed on cloud providers like AzureML without any additional training. To ensure that our cloud deployed model works, we use testing data from the famous MNIST data set, provided as part of the [trained MNIST model](https://github.com/onnx/models/tree/master/mnist) in the ONNX model zoo.\n",
     "\n",
     "***Input: Handwritten Images from MNIST Dataset***\n",
     "\n",
@@ -251,49 +244,36 @@
     "import onnxruntime\n",
     "import sys\n",
     "import os\n",
+    "from azureml.core.model import Model\n",
     "import time\n",
     "\n",
     "\n",
     "def init():\n",
     "    global session, input_name, output_name\n",
-    "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
-    "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
-    "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
-    "    model = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'model.onnx')\n",
+    "    model = Model.get_model_path(model_name = 'mnist_1')\n",
     "    session = onnxruntime.InferenceSession(model, None)\n",
     "    input_name = session.get_inputs()[0].name\n",
     "    output_name = session.get_outputs()[0].name \n",
     "    \n",
-    "\n",
-    "def preprocess(input_data_json):\n",
-    "    # convert the JSON data into the tensor input\n",
-    "    return np.array(json.loads(input_data_json)['data']).astype('float32')\n",
-    "\n",
-    "def postprocess(result):\n",
-    "    # We use argmax to pick the highest confidence label\n",
-    "    return int(np.argmax(np.array(result).squeeze(), axis=0))\n",
-    "    \n",
     "def run(input_data):\n",
+    "    '''Purpose: evaluate test input in Azure Cloud using onnxruntime.\n",
+    "        We will call the run function later from our Jupyter Notebook \n",
+    "        so our azure service can evaluate our model input in the cloud. '''\n",
     "\n",
     "    try:\n",
     "        # load in our data, convert to readable format\n",
-    "        data = preprocess(input_data)\n",
-    "        \n",
-    "        # start timer\n",
+    "        data = np.array(json.loads(input_data)['data']).astype('float32')\n",
+    "\n",
     "        start = time.time()\n",
-    "        \n",
-    "        r = session.run([output_name], {input_name: data})\n",
-    "        \n",
-    "        #end timer\n",
+    "        r = session.run([output_name], {input_name: data})[0]\n",
     "        end = time.time()\n",
-    "        \n",
-    "        result = postprocess(r)\n",
-    "        result_dict = {\"result\": result,\n",
-    "                      \"time_in_sec\": end - start}\n",
+    "        result = choose_class(r[0])\n",
+    "        result_dict = {\"result\": [result],\n",
+    "                      \"time_in_sec\": [end - start]}\n",
     "    except Exception as e:\n",
     "        result_dict = {\"error\": str(e)}\n",
     "    \n",
-    "    return result_dict\n",
+    "    return json.dumps(result_dict)\n",
     "\n",
     "def choose_class(result_prob):\n",
     "    \"\"\"We use argmax to determine the right label to choose from our output\"\"\"\n",
@@ -306,7 +286,7 @@
    "source": [
     "### Write Environment File\n",
     "\n",
-    "This step creates a YAML environment file that specifies which dependencies we would like to see in our Linux Virtual Machine. Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service."
+    "This step creates a YAML environment file that specifies which dependencies we would like to see in our Linux Virtual Machine."
    ]
   },
   {
@@ -317,7 +297,7 @@
    "source": [
     "from azureml.core.conda_dependencies import CondaDependencies \n",
     "\n",
-    "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n",
+    "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\"])\n",
     "\n",
     "with open(\"myenv.yml\",\"w\") as f:\n",
     "    f.write(myenv.serialize_to_string())"
@@ -327,7 +307,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Create Inference Configuration"
+    "### Create the Container Image\n",
+    "This step will likely take a few minutes."
    ]
   },
   {
@@ -336,19 +317,47 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from azureml.core.model import InferenceConfig\n",
-    "from azureml.core.environment import Environment\n",
+    "from azureml.core.image import ContainerImage\n",
+    "\n",
+    "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
+    "                                                  runtime = \"python\",\n",
+    "                                                  conda_file = \"myenv.yml\",\n",
+    "                                                  description = \"MNIST ONNX Runtime container\",\n",
+    "                                                  tags = {\"demo\": \"onnx\"}) \n",
     "\n",
     "\n",
-    "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
-    "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
+    "image = ContainerImage.create(name = \"onnximage\",\n",
+    "                              # this is the model object\n",
+    "                              models = [model],\n",
+    "                              image_config = image_config,\n",
+    "                              workspace = ws)\n",
+    "\n",
+    "image.wait_for_creation(show_output = True)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Deploy the model"
+    "In case you need to debug your code, the next line of code accesses the log file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(image.image_build_log_uri)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We're all done specifying what we want our virtual machine to do. Let's configure and deploy our container image.\n",
+    "\n",
+    "### Deploy the container image"
    ]
   },
   {
@@ -369,7 +378,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The following cell will likely take a few minutes to run."
+    "The following cell will likely take a few minutes to run as well."
    ]
   },
   {
@@ -378,9 +387,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from azureml.core.webservice import Webservice\n",
+    "\n",
     "aci_service_name = 'onnx-demo-mnist'\n",
     "print(\"Service\", aci_service_name)\n",
-    "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
+    "\n",
+    "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
+    "                                           image = image,\n",
+    "                                           name = aci_service_name,\n",
+    "                                           workspace = ws)\n",
+    "\n",
     "aci_service.wait_for_deployment(True)\n",
     "print(aci_service.state)"
    ]
@@ -407,16 +423,7 @@
     "\n",
     "If you've made it this far, you've deployed a working VM with a handwritten digit classifier running in the cloud using Azure ML. Congratulations!\n",
     "\n",
-    "You can get the URL for the webservice with the code below. Let's now see how well our model deals with our test images."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(aci_service.scoring_uri)"
+    "Let's see how well our model deals with our test images."
    ]
   },
   {
@@ -521,7 +528,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.figure(figsize = (16, 6))\n",
+    "plt.figure(figsize = (16, 6), frameon=False)\n",
     "plt.subplot(1, 8, 1)\n",
     "\n",
     "plt.text(x = 0, y = -30, s = \"True Label: \", fontsize = 13, color = 'black')\n",
@@ -537,14 +544,14 @@
     "    input_data = json.dumps({'data': test_inputs[i].tolist()})\n",
     "    \n",
     "    # predict using the deployed model\n",
-    "    r = aci_service.run(input_data)\n",
+    "    r = json.loads(aci_service.run(input_data))\n",
     "    \n",
     "    if \"error\" in r:\n",
     "        print(r['error'])\n",
     "        break\n",
     "        \n",
-    "    result = r['result']\n",
-    "    time_ms = np.round(r['time_in_sec'] * 1000, 2)\n",
+    "    result = r['result'][0]\n",
+    "    time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n",
     "    \n",
     "    ground_truth = int(np.argmax(test_outputs[i]))\n",
     "    \n",
@@ -594,19 +601,19 @@
     "    \"\"\"Convert the input image into grayscale\"\"\"\n",
     "    return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])\n",
     "\n",
-    "def resize_img(img_to_resize):\n",
+    "def resize_img(img):\n",
     "    \"\"\"Resize image to MNIST model input dimensions\"\"\"\n",
-    "    r_img = cv2.resize(img_to_resize, dsize=(28, 28), interpolation=cv2.INTER_AREA)\n",
-    "    r_img.resize((1, 1, 28, 28))\n",
-    "    return r_img\n",
+    "    img = cv2.resize(img, dsize=(28, 28), interpolation=cv2.INTER_AREA)\n",
+    "    img.resize((1, 1, 28, 28))\n",
+    "    return img\n",
     "\n",
-    "def preprocess(img_to_preprocess):\n",
+    "def preprocess(img):\n",
     "    \"\"\"Resize input images and convert them to grayscale.\"\"\"\n",
-    "    if img_to_preprocess.shape == (28, 28):\n",
-    "        img_to_preprocess.resize((1, 1, 28, 28))\n",
-    "        return img_to_preprocess\n",
+    "    if img.shape == (28, 28):\n",
+    "        img.resize((1, 1, 28, 28))\n",
+    "        return img\n",
     "    \n",
-    "    grayscale = rgb2gray(img_to_preprocess)\n",
+    "    grayscale = rgb2gray(img)\n",
     "    processed_img = resize_img(grayscale)\n",
     "    return processed_img"
    ]
@@ -651,10 +658,10 @@
     "    input_data = json.dumps({'data': img.tolist()})\n",
     "\n",
     "    try:\n",
-    "        r = aci_service.run(input_data)\n",
-    "        result = r['result']\n",
-    "        time_ms = np.round(r['time_in_sec'] * 1000, 2)\n",
-    "    except KeyError as e:\n",
+    "        r = json.loads(aci_service.run(input_data))\n",
+    "        result = r['result'][0]\n",
+    "        time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n",
+    "    except Exception as e:\n",
     "        print(str(e))\n",
     "\n",
     "    plt.figure(figsize = (16, 6))\n",
@@ -684,7 +691,18 @@
     "\n",
     "A convolution layer is a set of filters. Each filter is defined by a weight (**W**) matrix, and  bias ($b$).\n",
     "\n",
-    "These filters are scanned across the image performing the dot product between the weights and corresponding input value ($x$). The bias value is added to the output of the dot product and the resulting sum is optionally mapped through an activation function."
+    "![](https://www.cntk.ai/jup/cntk103d_filterset_v2.png)\n",
+    "\n",
+    "These filters are scanned across the image performing the dot product between the weights and corresponding input value ($x$). The bias value is added to the output of the dot product and the resulting sum is optionally mapped through an activation function. This process is illustrated in the following animation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Image(url=\"https://www.cntk.ai/jup/cntk103d_conv2d_final.gif\", width= 200)"
    ]
   },
   {
@@ -696,6 +714,24 @@
     "The MNIST model from the ONNX Model Zoo uses maxpooling to update the weights in its convolutions, summarized by the graphic below. You can see the entire workflow of our pre-trained model in the following image, with our input images and our output probabilities of each of our 10 labels. If you're interested in exploring the logic behind creating a Deep Learning model further, please look at the [training tutorial for our ONNX MNIST Convolutional Neural Network](https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_103D_MNIST_ConvolutionalNeuralNetwork.ipynb). "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Max-Pooling for Convolutional Neural Nets\n",
+    "\n",
+    "![](http://www.cntk.ai/jup/c103d_max_pooling.gif)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Pre-Trained Model Architecture\n",
+    "\n",
+    "![](http://www.cntk.ai/jup/conv103d_mnist-conv-mp.png)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -704,7 +740,7 @@
    "source": [
     "# remember to delete your service after you are done using it!\n",
     "\n",
-    "aci_service.delete()"
+    "# aci_service.delete()"
    ]
   },
   {
@@ -721,7 +757,7 @@
     "- ensured that your deep learning model is working perfectly (in the cloud) on test data, and checked it against some of your own!\n",
     "\n",
     "Next steps:\n",
-    "- Check out another interesting application based on a Microsoft Research computer vision paper that lets you set up a [facial emotion recognition model](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb) in the cloud! This tutorial deploys a pre-trained ONNX Computer Vision model in an Azure ML virtual machine.\n",
+    "- Check out another interesting application based on a Microsoft Research computer vision paper that lets you set up a [facial emotion recognition model](https://github.com/Azure/MachineLearningNotebooks/tree/master/onnx/onnx-inference-emotion-recognition.ipynb) in the cloud! This tutorial deploys a pre-trained ONNX Computer Vision model in an Azure ML virtual machine.\n",
     "- Contribute to our [open source ONNX repository on github](http://github.com/onnx/onnx) and/or add to our [ONNX model zoo](http://github.com/onnx/models)"
    ]
   }
@@ -732,22 +768,6 @@
     "name": "viswamy"
    }
   ],
-  "category": "deployment",
-  "compute": [
-   "Local"
-  ],
-  "datasets": [
-   "MNIST"
-  ],
-  "deployment": [
-   "Azure Container Instance"
-  ],
-  "exclude_from_index": false,
-  "framework": [
-   "ONNX"
-  ],
-  "friendly_name": "Deploy MNIST digit recognition with ONNX Runtime",
-  "index_order": 1,
   "kernelspec": {
    "display_name": "Python 3.6",
    "language": "python",
@@ -763,14 +783,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.6"
   },
-  "msauthor": "vinitra.swamy",
-  "star_tag": [],
-  "tags": [
-   "ONNX Model Zoo"
-  ],
-  "task": "Image Classification"
+  "msauthor": "vinitra.swamy"
  },
  "nbformat": 4,
  "nbformat_minor": 2

how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.yml

@@ -1,9 +0,0 @@
-name: onnx-inference-mnist-deploy
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-widgets
-  - matplotlib
-  - numpy
-  - onnx<1.7.0
-  - opencv-python-headless

how-to-use-azureml/deployment/onnx/onnx-mnist-predict-input.json

@@ -1 +0,0 @@
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