Keras Sample

version 1.0.10

Dockerfiles/1.0.10/Dockerfile

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

Dockerfiles/1.0.2/Dockerfile

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

Dockerfiles/1.0.6/Dockerfile

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

Dockerfiles/1.0.8/Dockerfile

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

README.md

@@ -1,36 +1,37 @@
-# Azure Machine Learning service sample notebooks
-
----
+# Azure Machine Learning service example notebooks
 
 This repository contains example notebooks demonstrating the [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning-service/) Python SDK
 which allows you to build, train, deploy and manage machine learning solutions using Azure.  The AML SDK
 allows you the choice of using local or cloud compute resources, while managing
 and maintaining the complete data science workflow from the cloud.
 
-* Read [instructions on setting up notebooks](./NBSETUP.md) to run these notebooks.
+![Azure ML workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/service/media/overview-what-is-azure-ml/aml.png)
 
-* Find quickstarts, end-to-end tutorials, and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/).
+## Quick installation
+```sh
+pip install azureml-sdk
+```
+Read more detailed instructions on [how to set up your environment](./NBSETUP.md).
 
-## Getting Started
+## How to navigate and use the example notebooks?
+You should always run the [Configuration](./configuration.ipynb) notebook first when setting up a notebook library on a new machine or in a new environment. It configures your notebook library to connect to an Azure Machine Learning workspace, and sets up your workspace and compute to be used by many of the other examples. 
 
-These examples will provide you with an effective way to get started using AML.  Once you're familiar with
-some of the capabilities, explore the repository for specific topics.
+If you want to...
 
-- [Configuration](./configuration.ipynb) configures your notebook library to easily connect to an
-    Azure Machine Learning workspace, and sets up your workspace to be used by many of the other examples.  You should
-    always run this first when setting up a notebook library on a new machine or in a new environment
-- [Train in notebook](./how-to-use-azureml/training/train-within-notebook) shows how to create a model directly in a notebook while recording
-    metrics and deploy that model to a test service
-- [Train on remote](./how-to-use-azureml/training/train-on-remote-vm) takes the previous example and shows how to create the model on a cloud compute target
-- [Production deploy to AKS](./how-to-use-azureml/deployment/production-deploy-to-aks) shows how to create a production grade inferencing webservice
+ * ...try out and explore Azure ML, start with image classification tutorials [part 1 training](./tutorials/img-classification-part1-training.ipynb) and [part 2 deployment](./tutorials/img-classification-part2-deploy.ipynb).
+ * ...learn about experimentation and tracking run history, first [train within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then try [training on remote VM](./how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb) and [using logging APIs](./how-to-use-azureml/training/logging-api/logging-api.ipynb).
+ * ...train deep learning models at scale, first learn about [Machine Learning Compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and then try [distributed hyperparameter tuning](./how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) and [distributed training](./how-to-use-azureml/training-with-deep-learning/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb).
+ * ...deploy model as realtime scoring service, first learn the basics by [training within Notebook and deploying to Azure Container Instance](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then learn how to [register and manage models, and create Docker images](./how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb), and [production deploy models on Azure Kubernetes Cluster](./how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb).
+ * ...deploy models as batch scoring service, first [train a model within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), learn how to [register and manage models](./how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb), then [create Machine Learning Compute for scoring compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and [use Machine Learning Pipelines to deploy your model](./how-to-use-azureml/machine-learning-pipelines/pipeline-mpi-batch-prediction.ipynb).
+ * ...monitor your deployed models, learn about using [App Insights](./how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb) and [model data collection](./how-to-use-azureml/deployment/enable-data-collection-for-models-in-aks/enable-data-collection-for-models-in-aks.ipynb).
 
 ## Tutorials
 
 The [Tutorials](./tutorials) folder contains notebooks for the tutorials described in the [Azure Machine Learning documentation](https://aka.ms/aml-docs)
   
-## How to use AML
+## How to use Azure ML
 
-The [How to use AML](./how-to-use-azureml) folder contains specific examples demonstrating the features of the Azure Machine Learning SDK
+The [How to use Azure ML](./how-to-use-azureml) folder contains specific examples demonstrating the features of the Azure Machine Learning SDK
 
 - [Training](./how-to-use-azureml/training) - Examples of how to build models using Azure ML's logging and execution capabilities on local and remote compute targets.
 - [Training with Deep Learning](./how-to-use-azureml/training-with-deep-learning) - Examples demonstrating how to build deep learning models using estimators and parameter sweeps
@@ -38,3 +39,20 @@ The [How to use AML](./how-to-use-azureml) folder contains specific examples dem
 - [Machine Learning Pipelines](./how-to-use-azureml/machine-learning-pipelines) - Examples showing how to create and use reusable pipelines for training and batch scoring
 - [Deployment](./how-to-use-azureml/deployment) - Examples showing how to deploy and manage machine learning models and solutions
 - [Azure Databricks](./how-to-use-azureml/azure-databricks) - Examples showing how to use Azure ML with Azure Databricks
+
+---
+## Documentation
+
+ * Quickstarts, end-to-end tutorials, and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/).
+
+ * [Python SDK reference]( https://docs.microsoft.com/en-us/python/api/overview/azure/ml/intro?view=azure-ml-py)
+
+
+---
+
+## Projects using Azure Machine Learning
+
+Visit following repos to see projects contributed by Azure ML users:
+
+ - [Fine tune natural language processing models using Azure Machine Learning service](https://github.com/Microsoft/AzureML-BERT)
+ - [Fashion MNIST with Azure ML SDK](https://github.com/amynic/azureml-sdk-fashion)

configuration.ipynb

@@ -96,7 +96,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.0.6 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.0.10 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb

@@ -0,0 +1,409 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# NVIDIA RAPIDS in Azure Machine Learning"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The [RAPIDS](https://www.developer.nvidia.com/rapids) suite of software libraries from NVIDIA enables the execution of end-to-end data science and analytics pipelines entirely on GPUs. In many machine learning projects, a significant portion of the model training time is spent in setting up the data; this stage of the process is known as Extraction, Transformation and Loading, or ETL. By using the DataFrame API for ETL and GPU-capable ML algorithms in RAPIDS, data preparation and training models can be done in GPU-accelerated end-to-end pipelines without incurring serialization costs between the pipeline stages. This notebook demonstrates how to use NVIDIA RAPIDS to prepare data and train model in Azure.\n",
+        " \n",
+        "In this notebook, we will do the following:\n",
+        " \n",
+        "* Create an Azure Machine Learning Workspace\n",
+        "* Create an AMLCompute target\n",
+        "* Use a script to process our data and train a model\n",
+        "* Obtain the data required to run this sample\n",
+        "* Create an AML run configuration to launch a machine learning job\n",
+        "* Run the script to prepare data for training and train the model\n",
+        " \n",
+        "Prerequisites:\n",
+        "* An Azure subscription to create a Machine Learning Workspace\n",
+        "* Familiarity with the Azure ML SDK (refer to [notebook samples](https://github.com/Azure/MachineLearningNotebooks))\n",
+        "* A Jupyter notebook environment with Azure Machine Learning SDK installed. Refer to instructions to [setup the environment](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#local)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Verify if Azure ML SDK is installed"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import azureml.core\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from azureml.core import Workspace, Experiment\n",
+        "from azureml.core.compute import AmlCompute, ComputeTarget\n",
+        "from azureml.data.data_reference import DataReference\n",
+        "from azureml.core.runconfig import RunConfiguration\n",
+        "from azureml.core import ScriptRunConfig\n",
+        "from azureml.widgets import RunDetails"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create Azure ML Workspace"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The following step is optional if you already have a workspace. If you want to use an existing workspace, then\n",
+        "skip this workspace creation step and move on to the next step to load the workspace.\n",
+        " \n",
+        "<font color='red'>Important</font>: in the code cell below, be sure to set the correct values for the subscription_id, \n",
+        "resource_group, workspace_name, region before executing this code cell."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "subscription_id = os.environ.get(\"SUBSCRIPTION_ID\", \"<subscription_id>\")\n",
+        "resource_group = os.environ.get(\"RESOURCE_GROUP\", \"<resource_group>\")\n",
+        "workspace_name = os.environ.get(\"WORKSPACE_NAME\", \"<workspace_name>\")\n",
+        "workspace_region = os.environ.get(\"WORKSPACE_REGION\", \"<region>\")\n",
+        "\n",
+        "ws = Workspace.create(workspace_name, subscription_id=subscription_id, resource_group=resource_group, location=workspace_region)\n",
+        "\n",
+        "# write config to a local directory for future use\n",
+        "ws.write_config()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Load existing Workspace"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "# if a locally-saved configuration file for the workspace is not available, use the following to load workspace\n",
+        "# ws = Workspace(subscription_id=subscription_id, resource_group=resource_group, workspace_name=workspace_name)\n",
+        "print('Workspace name: ' + ws.name, \n",
+        "      'Azure region: ' + ws.location, \n",
+        "      'Subscription id: ' + ws.subscription_id, \n",
+        "      'Resource group: ' + ws.resource_group, sep = '\\n')\n",
+        "\n",
+        "scripts_folder = \"scripts_folder\"\n",
+        "\n",
+        "if not os.path.isdir(scripts_folder):\n",
+        "    os.mkdir(scripts_folder)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create AML Compute Target"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Because NVIDIA RAPIDS requires P40 or V100 GPUs, the user needs to specify compute targets from one of [NC_v3](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv3-series), [NC_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv2-series), [ND](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#nd-series) or [ND_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ndv2-series-preview) virtual machine types in Azure; these are the families of virtual machines in Azure that are provisioned with these GPUs.\n",
+        " \n",
+        "Pick one of the supported VM SKUs based on the number of GPUs you want to use for ETL and training in RAPIDS.\n",
+        " \n",
+        "The script in this notebook is implemented for single-machine scenarios. An example supporting multiple nodes will be published later."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "gpu_cluster_name = \"gpucluster\"\n",
+        "\n",
+        "if gpu_cluster_name in ws.compute_targets:\n",
+        "    gpu_cluster = ws.compute_targets[gpu_cluster_name]\n",
+        "    if gpu_cluster and type(gpu_cluster) is AmlCompute:\n",
+        "        print('found compute target. just use it. ' + gpu_cluster_name)\n",
+        "else:\n",
+        "    print(\"creating new cluster\")\n",
+        "    # vm_size parameter below could be modified to one of the RAPIDS-supported VM types\n",
+        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"Standard_NC6s_v2\", min_nodes=1, max_nodes = 1)\n",
+        "\n",
+        "    # create the cluster\n",
+        "    gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, provisioning_config)\n",
+        "    gpu_cluster.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Script to process data and train model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The _process&#95;data.py_ script used in the step below is a slightly modified implementation of [RAPIDS E2E example](https://github.com/rapidsai/notebooks/blob/master/mortgage/E2E.ipynb)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# copy process_data.py into the script folder\n",
+        "import shutil\n",
+        "shutil.copy('./process_data.py', os.path.join(scripts_folder, 'process_data.py'))\n",
+        "\n",
+        "with open(os.path.join(scripts_folder, './process_data.py'), 'r') as process_data_script:\n",
+        "    print(process_data_script.read())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Data required to run this sample"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This sample uses [Fannie Mae\u00e2\u20ac\u2122s Single-Family Loan Performance Data](http://www.fanniemae.com/portal/funding-the-market/data/loan-performance-data.html). Refer to the 'Available mortgage datasets' section in [instructions](https://rapidsai.github.io/demos/datasets/mortgage-data) to get sample data.\n",
+        "\n",
+        "Once you obtain access to the data, you will need to make this data available in an [Azure Machine Learning Datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data), for use in this sample."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<font color='red'>Important</font>: The following step assumes the data is uploaded to the Workspace's default data store under a folder named 'mortgagedata2000_01'. Note that uploading data to the Workspace's default data store is not necessary and the data can be referenced from any datastore, e.g., from Azure Blob or File service, once it is added as a datastore to the workspace. The path_on_datastore parameter needs to be updated, depending on where the data is available.  The directory where the data is available should have the following folder structure, as the process_data.py script expects this directory structure:\n",
+        "* _&lt;data directory>_/acq\n",
+        "* _&lt;data directory>_/perf\n",
+        "* _names.csv_\n",
+        "\n",
+        "The 'acq' and 'perf' refer to directories containing data files. The _&lt;data directory>_ is the path specified in _path&#95;on&#95;datastore_ parameter in the step below."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ds = ws.get_default_datastore()\n",
+        "\n",
+        "# download and uncompress data in a local directory before uploading to data store\n",
+        "# directory specified in src_dir parameter below should have the acq, perf directories with data and names.csv file\n",
+        "# ds.upload(src_dir='<local directory that has data>', target_path='mortgagedata2000_01', overwrite=True, show_progress=True)\n",
+        "\n",
+        "# data already uploaded to the datastore\n",
+        "data_ref = DataReference(data_reference_name='data', datastore=ds, path_on_datastore='mortgagedata2000_01')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create AML run configuration to launch a machine learning job"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "AML allows the option of using existing Docker images with prebuilt conda environments. The following step use an existing image from [Docker Hub](https://hub.docker.com/r/rapidsai/rapidsai/)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run_config = RunConfiguration()\n",
+        "run_config.framework = 'python'\n",
+        "run_config.environment.python.user_managed_dependencies = True\n",
+        "# use conda environment named 'rapids' available in the Docker image\n",
+        "# this conda environment does not include azureml-defaults package that is required for using AML functionality like metrics tracking, model management etc.\n",
+        "run_config.environment.python.interpreter_path = '/conda/envs/rapids/bin/python'\n",
+        "run_config.target = gpu_cluster_name\n",
+        "run_config.environment.docker.enabled = True\n",
+        "run_config.environment.docker.gpu_support = True\n",
+        "# if registry is not mentioned the image is pulled from Docker Hub\n",
+        "run_config.environment.docker.base_image = \"rapidsai/rapidsai:cuda9.2_ubuntu16.04_root\"\n",
+        "run_config.environment.spark.precache_packages = False\n",
+        "run_config.data_references={'data':data_ref.to_config()}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Wrapper function to submit Azure Machine Learning experiment"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# parameter cpu_predictor indicates if training should be done on CPU. If set to true, GPUs are used *only* for ETL and *not* for training\n",
+        "# parameter num_gpu indicates number of GPUs to use among the GPUs available in the VM for ETL and if cpu_predictor is false, for training as well \n",
+        "def run_rapids_experiment(cpu_training, gpu_count):\n",
+        "    # any value between 1-4 is allowed here depending the type of VMs available in gpu_cluster\n",
+        "    if gpu_count not in [1, 2, 3, 4]:\n",
+        "        raise Exception('Value specified for the number of GPUs to use {0} is invalid'.format(gpu_count))\n",
+        "\n",
+        "    # following data partition mapping is empirical (specific to GPUs used and current data partitioning scheme) and may need to be tweaked\n",
+        "    gpu_count_data_partition_mapping = {1: 2, 2: 4, 3: 5, 4: 7}\n",
+        "    part_count = gpu_count_data_partition_mapping[gpu_count]\n",
+        "\n",
+        "    end_year = 2000\n",
+        "    if gpu_count > 2:\n",
+        "        end_year = 2001 # use more data with more GPUs\n",
+        "\n",
+        "    src = ScriptRunConfig(source_directory=scripts_folder, \n",
+        "                          script='process_data.py', \n",
+        "                          arguments = ['--num_gpu', gpu_count, '--data_dir', str(data_ref),\n",
+        "                                      '--part_count', part_count, '--end_year', end_year,\n",
+        "                                      '--cpu_predictor', cpu_training\n",
+        "                                      ],\n",
+        "                          run_config=run_config\n",
+        "                         )\n",
+        "\n",
+        "    exp = Experiment(ws, 'rapidstest')\n",
+        "    run = exp.submit(config=src)\n",
+        "    RunDetails(run).show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Submit experiment (ETL & training on GPU)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "cpu_predictor = False\n",
+        "# the value for num_gpu should be less than or equal to the number of GPUs available in the VM\n",
+        "num_gpu = 1 \n",
+        "# train using CPU, use GPU for both ETL and training\n",
+        "run_rapids_experiment(cpu_predictor, num_gpu)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Submit experiment (ETL on GPU, training on CPU)\n",
+        "\n",
+        "To observe performance difference between GPU-accelerated RAPIDS based training with CPU-only training, set 'cpu_predictor' predictor to 'True' and rerun the experiment"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "cpu_predictor = True\n",
+        "# the value for num_gpu should be less than or equal to the number of GPUs available in the VM\n",
+        "num_gpu = 1\n",
+        "# train using CPU, use GPU for ETL\n",
+        "run_rapids_experiment(cpu_predictor, num_gpu)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Delete cluster"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# delete the cluster\n",
+        "# gpu_cluster.delete()"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "ksivas"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

contrib/RAPIDS/process_data.py

@@ -0,0 +1,500 @@
+# License Info: https://github.com/rapidsai/notebooks/blob/master/LICENSE
+import numpy as np
+import datetime
+import dask_xgboost as dxgb_gpu
+import dask
+import dask_cudf
+from dask.delayed import delayed
+from dask.distributed import Client, wait
+import xgboost as xgb
+import cudf
+from cudf.dataframe import DataFrame
+from collections import OrderedDict
+import gc
+from glob import glob
+import os
+import argparse
+
+parser = argparse.ArgumentParser("rapidssample")
+parser.add_argument("--data_dir", type=str, help="location of data")
+parser.add_argument("--num_gpu", type=int, help="Number of GPUs to use", default=1)
+parser.add_argument("--part_count", type=int, help="Number of data files to train against", default=2)
+parser.add_argument("--end_year", type=int, help="Year to end the data load", default=2000)
+parser.add_argument("--cpu_predictor", type=str, help="Flag to use CPU for prediction", default='False')
+parser.add_argument('-f', type=str, default='') # added for notebook execution scenarios
+args = parser.parse_args()
+data_dir = args.data_dir
+num_gpu = args.num_gpu
+part_count = args.part_count
+end_year = args.end_year
+cpu_predictor = args.cpu_predictor.lower() in ('yes', 'true', 't', 'y', '1')
+
+print('data_dir = {0}'.format(data_dir))
+print('num_gpu = {0}'.format(num_gpu))
+print('part_count = {0}'.format(part_count))
+part_count = part_count + 1 # adding one because the usage below is not inclusive
+print('end_year = {0}'.format(end_year))
+print('cpu_predictor = {0}'.format(cpu_predictor))
+
+import subprocess
+
+cmd = "hostname --all-ip-addresses"
+process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE)
+output, error = process.communicate()
+IPADDR = str(output.decode()).split()[0]
+print('IPADDR is {0}'.format(IPADDR))
+
+cmd = "/rapids/notebooks/utils/dask-setup.sh 0"
+process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE)
+output, error = process.communicate()
+
+cmd = "/rapids/notebooks/utils/dask-setup.sh rapids " + str(num_gpu) + " 8786 8787 8790 " + str(IPADDR) + " MASTER"
+process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE)
+output, error = process.communicate()
+
+print(output.decode())
+
+import dask
+from dask.delayed import delayed
+from dask.distributed import Client, wait
+
+_client = IPADDR + str(":8786")
+
+client = dask.distributed.Client(_client)
+
+def initialize_rmm_pool():
+    from librmm_cffi import librmm_config as rmm_cfg
+
+    rmm_cfg.use_pool_allocator = True
+    #rmm_cfg.initial_pool_size = 2<<30 # set to 2GiB. Default is 1/2 total GPU memory
+    import cudf
+    return cudf._gdf.rmm_initialize()
+
+def initialize_rmm_no_pool():
+    from librmm_cffi import librmm_config as rmm_cfg
+    
+    rmm_cfg.use_pool_allocator = False
+    import cudf
+    return cudf._gdf.rmm_initialize()
+
+def run_dask_task(func, **kwargs):
+    task = func(**kwargs)
+    return task
+
+def process_quarter_gpu(year=2000, quarter=1, perf_file=""):
+    ml_arrays = run_dask_task(delayed(run_gpu_workflow),
+                                          quarter=quarter,
+                                          year=year,
+                                          perf_file=perf_file)
+    return client.compute(ml_arrays,
+                          optimize_graph=False,
+                          fifo_timeout="0ms"
+                         )
+
+def null_workaround(df, **kwargs):
+    for column, data_type in df.dtypes.items():
+        if str(data_type) == "category":
+            df[column] = df[column].astype('int32').fillna(-1)
+        if str(data_type) in ['int8', 'int16', 'int32', 'int64', 'float32', 'float64']:
+            df[column] = df[column].fillna(-1)
+    return df
+
+def run_gpu_workflow(quarter=1, year=2000, perf_file="", **kwargs):
+    names = gpu_load_names()
+    acq_gdf = gpu_load_acquisition_csv(acquisition_path= acq_data_path + "/Acquisition_"
+                                      + str(year) + "Q" + str(quarter) + ".txt")
+    acq_gdf = acq_gdf.merge(names, how='left', on=['seller_name'])
+    acq_gdf.drop_column('seller_name')
+    acq_gdf['seller_name'] = acq_gdf['new']
+    acq_gdf.drop_column('new')
+    perf_df_tmp = gpu_load_performance_csv(perf_file)
+    gdf = perf_df_tmp
+    everdf = create_ever_features(gdf)
+    delinq_merge = create_delinq_features(gdf)
+    everdf = join_ever_delinq_features(everdf, delinq_merge)
+    del(delinq_merge)
+    joined_df = create_joined_df(gdf, everdf)
+    testdf = create_12_mon_features(joined_df)
+    joined_df = combine_joined_12_mon(joined_df, testdf)
+    del(testdf)
+    perf_df = final_performance_delinquency(gdf, joined_df)
+    del(gdf, joined_df)
+    final_gdf = join_perf_acq_gdfs(perf_df, acq_gdf)
+    del(perf_df)
+    del(acq_gdf)
+    final_gdf = last_mile_cleaning(final_gdf)
+    return final_gdf
+
+def gpu_load_performance_csv(performance_path, **kwargs):
+    """ Loads performance data
+
+    Returns
+    -------
+    GPU DataFrame
+    """
+    
+    cols = [
+        "loan_id", "monthly_reporting_period", "servicer", "interest_rate", "current_actual_upb",
+        "loan_age", "remaining_months_to_legal_maturity", "adj_remaining_months_to_maturity",
+        "maturity_date", "msa", "current_loan_delinquency_status", "mod_flag", "zero_balance_code",
+        "zero_balance_effective_date", "last_paid_installment_date", "foreclosed_after",
+        "disposition_date", "foreclosure_costs", "prop_preservation_and_repair_costs",
+        "asset_recovery_costs", "misc_holding_expenses", "holding_taxes", "net_sale_proceeds",
+        "credit_enhancement_proceeds", "repurchase_make_whole_proceeds", "other_foreclosure_proceeds",
+        "non_interest_bearing_upb", "principal_forgiveness_upb", "repurchase_make_whole_proceeds_flag",
+        "foreclosure_principal_write_off_amount", "servicing_activity_indicator"
+    ]
+    
+    dtypes = OrderedDict([
+        ("loan_id", "int64"),
+        ("monthly_reporting_period", "date"),
+        ("servicer", "category"),
+        ("interest_rate", "float64"),
+        ("current_actual_upb", "float64"),
+        ("loan_age", "float64"),
+        ("remaining_months_to_legal_maturity", "float64"),
+        ("adj_remaining_months_to_maturity", "float64"),
+        ("maturity_date", "date"),
+        ("msa", "float64"),
+        ("current_loan_delinquency_status", "int32"),
+        ("mod_flag", "category"),
+        ("zero_balance_code", "category"),
+        ("zero_balance_effective_date", "date"),
+        ("last_paid_installment_date", "date"),
+        ("foreclosed_after", "date"),
+        ("disposition_date", "date"),
+        ("foreclosure_costs", "float64"),
+        ("prop_preservation_and_repair_costs", "float64"),
+        ("asset_recovery_costs", "float64"),
+        ("misc_holding_expenses", "float64"),
+        ("holding_taxes", "float64"),
+        ("net_sale_proceeds", "float64"),
+        ("credit_enhancement_proceeds", "float64"),
+        ("repurchase_make_whole_proceeds", "float64"),
+        ("other_foreclosure_proceeds", "float64"),
+        ("non_interest_bearing_upb", "float64"),
+        ("principal_forgiveness_upb", "float64"),
+        ("repurchase_make_whole_proceeds_flag", "category"),
+        ("foreclosure_principal_write_off_amount", "float64"),
+        ("servicing_activity_indicator", "category")
+    ])
+
+    print(performance_path)
+    
+    return cudf.read_csv(performance_path, names=cols, delimiter='|', dtype=list(dtypes.values()), skiprows=1)
+
+def gpu_load_acquisition_csv(acquisition_path, **kwargs):
+    """ Loads acquisition data
+
+    Returns
+    -------
+    GPU DataFrame
+    """
+    
+    cols = [
+        'loan_id', 'orig_channel', 'seller_name', 'orig_interest_rate', 'orig_upb', 'orig_loan_term', 
+        'orig_date', 'first_pay_date', 'orig_ltv', 'orig_cltv', 'num_borrowers', 'dti', 'borrower_credit_score', 
+        'first_home_buyer', 'loan_purpose', 'property_type', 'num_units', 'occupancy_status', 'property_state',
+        'zip', 'mortgage_insurance_percent', 'product_type', 'coborrow_credit_score', 'mortgage_insurance_type', 
+        'relocation_mortgage_indicator'
+    ]
+    
+    dtypes = OrderedDict([
+        ("loan_id", "int64"),
+        ("orig_channel", "category"),
+        ("seller_name", "category"),
+        ("orig_interest_rate", "float64"),
+        ("orig_upb", "int64"),
+        ("orig_loan_term", "int64"),
+        ("orig_date", "date"),
+        ("first_pay_date", "date"),
+        ("orig_ltv", "float64"),
+        ("orig_cltv", "float64"),
+        ("num_borrowers", "float64"),
+        ("dti", "float64"),
+        ("borrower_credit_score", "float64"),
+        ("first_home_buyer", "category"),
+        ("loan_purpose", "category"),
+        ("property_type", "category"),
+        ("num_units", "int64"),
+        ("occupancy_status", "category"),
+        ("property_state", "category"),
+        ("zip", "int64"),
+        ("mortgage_insurance_percent", "float64"),
+        ("product_type", "category"),
+        ("coborrow_credit_score", "float64"),
+        ("mortgage_insurance_type", "float64"),
+        ("relocation_mortgage_indicator", "category")
+    ])
+    
+    print(acquisition_path)
+    
+    return cudf.read_csv(acquisition_path, names=cols, delimiter='|', dtype=list(dtypes.values()), skiprows=1)
+
+def gpu_load_names(**kwargs):
+    """ Loads names used for renaming the banks
+    
+    Returns
+    -------
+    GPU DataFrame
+    """
+
+    cols = [
+        'seller_name', 'new'
+    ]
+    
+    dtypes = OrderedDict([
+        ("seller_name", "category"),
+        ("new", "category"),
+    ])
+
+    return cudf.read_csv(col_names_path, names=cols, delimiter='|', dtype=list(dtypes.values()), skiprows=1)
+
+def create_ever_features(gdf, **kwargs):
+    everdf = gdf[['loan_id', 'current_loan_delinquency_status']]
+    everdf = everdf.groupby('loan_id', method='hash').max()
+    del(gdf)
+    everdf['ever_30'] = (everdf['max_current_loan_delinquency_status'] >= 1).astype('int8')
+    everdf['ever_90'] = (everdf['max_current_loan_delinquency_status'] >= 3).astype('int8')
+    everdf['ever_180'] = (everdf['max_current_loan_delinquency_status'] >= 6).astype('int8')
+    everdf.drop_column('max_current_loan_delinquency_status')
+    return everdf
+
+def create_delinq_features(gdf, **kwargs):
+    delinq_gdf = gdf[['loan_id', 'monthly_reporting_period', 'current_loan_delinquency_status']]
+    del(gdf)
+    delinq_30 = delinq_gdf.query('current_loan_delinquency_status >= 1')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min()
+    delinq_30['delinquency_30'] = delinq_30['min_monthly_reporting_period']
+    delinq_30.drop_column('min_monthly_reporting_period')
+    delinq_90 = delinq_gdf.query('current_loan_delinquency_status >= 3')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min()
+    delinq_90['delinquency_90'] = delinq_90['min_monthly_reporting_period']
+    delinq_90.drop_column('min_monthly_reporting_period')
+    delinq_180 = delinq_gdf.query('current_loan_delinquency_status >= 6')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min()
+    delinq_180['delinquency_180'] = delinq_180['min_monthly_reporting_period']
+    delinq_180.drop_column('min_monthly_reporting_period')
+    del(delinq_gdf)
+    delinq_merge = delinq_30.merge(delinq_90, how='left', on=['loan_id'], type='hash')
+    delinq_merge['delinquency_90'] = delinq_merge['delinquency_90'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]'))
+    delinq_merge = delinq_merge.merge(delinq_180, how='left', on=['loan_id'], type='hash')
+    delinq_merge['delinquency_180'] = delinq_merge['delinquency_180'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]'))
+    del(delinq_30)
+    del(delinq_90)
+    del(delinq_180)
+    return delinq_merge
+
+def join_ever_delinq_features(everdf_tmp, delinq_merge, **kwargs):
+    everdf = everdf_tmp.merge(delinq_merge, on=['loan_id'], how='left', type='hash')
+    del(everdf_tmp)
+    del(delinq_merge)
+    everdf['delinquency_30'] = everdf['delinquency_30'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]'))
+    everdf['delinquency_90'] = everdf['delinquency_90'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]'))
+    everdf['delinquency_180'] = everdf['delinquency_180'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]'))
+    return everdf
+
+def create_joined_df(gdf, everdf, **kwargs):
+    test = gdf[['loan_id', 'monthly_reporting_period', 'current_loan_delinquency_status', 'current_actual_upb']]
+    del(gdf)
+    test['timestamp'] = test['monthly_reporting_period']
+    test.drop_column('monthly_reporting_period')
+    test['timestamp_month'] = test['timestamp'].dt.month
+    test['timestamp_year'] = test['timestamp'].dt.year
+    test['delinquency_12'] = test['current_loan_delinquency_status']
+    test.drop_column('current_loan_delinquency_status')
+    test['upb_12'] = test['current_actual_upb']
+    test.drop_column('current_actual_upb')
+    test['upb_12'] = test['upb_12'].fillna(999999999)
+    test['delinquency_12'] = test['delinquency_12'].fillna(-1)
+    
+    joined_df = test.merge(everdf, how='left', on=['loan_id'], type='hash')
+    del(everdf)
+    del(test)
+    
+    joined_df['ever_30'] = joined_df['ever_30'].fillna(-1)
+    joined_df['ever_90'] = joined_df['ever_90'].fillna(-1)
+    joined_df['ever_180'] = joined_df['ever_180'].fillna(-1)
+    joined_df['delinquency_30'] = joined_df['delinquency_30'].fillna(-1)
+    joined_df['delinquency_90'] = joined_df['delinquency_90'].fillna(-1)
+    joined_df['delinquency_180'] = joined_df['delinquency_180'].fillna(-1)
+    
+    joined_df['timestamp_year'] = joined_df['timestamp_year'].astype('int32')
+    joined_df['timestamp_month'] = joined_df['timestamp_month'].astype('int32')
+    
+    return joined_df
+
+def create_12_mon_features(joined_df, **kwargs):
+    testdfs = []
+    n_months = 12
+    for y in range(1, n_months + 1):
+        tmpdf = joined_df[['loan_id', 'timestamp_year', 'timestamp_month', 'delinquency_12', 'upb_12']]
+        tmpdf['josh_months'] = tmpdf['timestamp_year'] * 12 + tmpdf['timestamp_month']
+        tmpdf['josh_mody_n'] = ((tmpdf['josh_months'].astype('float64') - 24000 - y) / 12).floor()
+        tmpdf = tmpdf.groupby(['loan_id', 'josh_mody_n'], method='hash').agg({'delinquency_12': 'max','upb_12': 'min'})
+        tmpdf['delinquency_12'] = (tmpdf['max_delinquency_12']>3).astype('int32')
+        tmpdf['delinquency_12'] +=(tmpdf['min_upb_12']==0).astype('int32')
+        tmpdf.drop_column('max_delinquency_12')
+        tmpdf['upb_12'] = tmpdf['min_upb_12']
+        tmpdf.drop_column('min_upb_12')
+        tmpdf['timestamp_year'] = (((tmpdf['josh_mody_n'] * n_months) + 24000 + (y - 1)) / 12).floor().astype('int16')
+        tmpdf['timestamp_month'] = np.int8(y)
+        tmpdf.drop_column('josh_mody_n')
+        testdfs.append(tmpdf)
+        del(tmpdf)
+    del(joined_df)
+
+    return cudf.concat(testdfs)
+
+def combine_joined_12_mon(joined_df, testdf, **kwargs):
+    joined_df.drop_column('delinquency_12')
+    joined_df.drop_column('upb_12')
+    joined_df['timestamp_year'] = joined_df['timestamp_year'].astype('int16')
+    joined_df['timestamp_month'] = joined_df['timestamp_month'].astype('int8')
+    return joined_df.merge(testdf, how='left', on=['loan_id', 'timestamp_year', 'timestamp_month'], type='hash')
+
+def final_performance_delinquency(gdf, joined_df, **kwargs):
+    merged = null_workaround(gdf)
+    joined_df = null_workaround(joined_df)
+    merged['timestamp_month'] = merged['monthly_reporting_period'].dt.month
+    merged['timestamp_month'] = merged['timestamp_month'].astype('int8')
+    merged['timestamp_year'] = merged['monthly_reporting_period'].dt.year
+    merged['timestamp_year'] = merged['timestamp_year'].astype('int16')
+    merged = merged.merge(joined_df, how='left', on=['loan_id', 'timestamp_year', 'timestamp_month'], type='hash')
+    merged.drop_column('timestamp_year')
+    merged.drop_column('timestamp_month')
+    return merged
+
+def join_perf_acq_gdfs(perf, acq, **kwargs):
+    perf = null_workaround(perf)
+    acq = null_workaround(acq)
+    return perf.merge(acq, how='left', on=['loan_id'], type='hash')
+
+def last_mile_cleaning(df, **kwargs):
+    drop_list = [
+        'loan_id', 'orig_date', 'first_pay_date', 'seller_name',
+        'monthly_reporting_period', 'last_paid_installment_date', 'maturity_date', 'ever_30', 'ever_90', 'ever_180',
+        'delinquency_30', 'delinquency_90', 'delinquency_180', 'upb_12',
+        'zero_balance_effective_date','foreclosed_after', 'disposition_date','timestamp'
+    ]
+    for column in drop_list:
+        df.drop_column(column)
+    for col, dtype in df.dtypes.iteritems():
+        if str(dtype)=='category':
+            df[col] = df[col].cat.codes
+        df[col] = df[col].astype('float32')
+    df['delinquency_12'] = df['delinquency_12'] > 0
+    df['delinquency_12'] = df['delinquency_12'].fillna(False).astype('int32')
+    for column in df.columns:
+        df[column] = df[column].fillna(-1)
+    return df.to_arrow(index=False)
+
+
+# to download data for this notebook, visit https://rapidsai.github.io/demos/datasets/mortgage-data and update the following paths accordingly
+acq_data_path = "{0}/acq".format(data_dir) #"/rapids/data/mortgage/acq"
+perf_data_path = "{0}/perf".format(data_dir) #"/rapids/data/mortgage/perf"
+col_names_path = "{0}/names.csv".format(data_dir) # "/rapids/data/mortgage/names.csv"
+start_year = 2000
+#end_year = 2000 # end_year is inclusive -- converted to parameter
+#part_count = 2 # the number of data files to train against -- converted to parameter
+
+client.run(initialize_rmm_pool)
+
+# NOTE: The ETL calculates additional features which are then dropped before creating the XGBoost DMatrix.
+# This can be optimized to avoid calculating the dropped features.
+print("Reading ...")
+t1 = datetime.datetime.now()
+gpu_dfs = []
+gpu_time = 0
+quarter = 1
+year = start_year
+count = 0
+while year <= end_year:
+    for file in glob(os.path.join(perf_data_path + "/Performance_" + str(year) + "Q" + str(quarter) + "*")):
+        if count < part_count:
+            gpu_dfs.append(process_quarter_gpu(year=year, quarter=quarter, perf_file=file))
+            count += 1
+            print('file: {0}'.format(file))
+            print('count: {0}'.format(count))
+    quarter += 1
+    if quarter == 5:
+        year += 1
+        quarter = 1
+        
+wait(gpu_dfs)
+t2 = datetime.datetime.now()
+print("Reading time ...")
+print(t2-t1)
+print('len(gpu_dfs) is {0}'.format(len(gpu_dfs)))
+
+client.run(cudf._gdf.rmm_finalize)
+client.run(initialize_rmm_no_pool)
+
+dxgb_gpu_params = {
+    'nround':            100,
+    'max_depth':         8,
+    'max_leaves':        2**8,
+    'alpha':             0.9,
+    'eta':               0.1,
+    'gamma':             0.1,
+    'learning_rate':     0.1,
+    'subsample':         1,
+    'reg_lambda':        1,
+    'scale_pos_weight':  2,
+    'min_child_weight':  30,
+    'tree_method':       'gpu_hist',
+    'n_gpus':            1, 
+    'distributed_dask':  True,
+    'loss':              'ls',
+    'objective':         'gpu:reg:linear',
+    'max_features':      'auto',
+    'criterion':         'friedman_mse',
+    'grow_policy':       'lossguide',
+    'verbose':           True
+}
+  
+if cpu_predictor:
+    print('Training using CPUs')
+    dxgb_gpu_params['predictor'] = 'cpu_predictor'
+    dxgb_gpu_params['tree_method'] = 'hist'
+    dxgb_gpu_params['objective'] = 'reg:linear'
+    
+else:
+    print('Training using GPUs')
+
+print('Training parameters are {0}'.format(dxgb_gpu_params))
+
+gpu_dfs = [delayed(DataFrame.from_arrow)(gpu_df) for gpu_df in gpu_dfs[:part_count]]
+    
+gpu_dfs = [gpu_df for gpu_df in gpu_dfs]
+
+wait(gpu_dfs)
+tmp_map = [(gpu_df, list(client.who_has(gpu_df).values())[0]) for gpu_df in gpu_dfs]
+new_map = {}
+for key, value in tmp_map:
+    if value not in new_map:
+        new_map[value] = [key]
+    else:
+        new_map[value].append(key)
+
+del(tmp_map)
+gpu_dfs = []
+for list_delayed in new_map.values():
+    gpu_dfs.append(delayed(cudf.concat)(list_delayed))
+
+del(new_map)
+gpu_dfs = [(gpu_df[['delinquency_12']], gpu_df[delayed(list)(gpu_df.columns.difference(['delinquency_12']))]) for gpu_df in gpu_dfs]
+gpu_dfs = [(gpu_df[0].persist(), gpu_df[1].persist()) for gpu_df in gpu_dfs]
+gpu_dfs = [dask.delayed(xgb.DMatrix)(gpu_df[1], gpu_df[0]) for gpu_df in gpu_dfs]
+gpu_dfs = [gpu_df.persist() for gpu_df in gpu_dfs]
+
+gc.collect()
+labels = None
+
+print('str(gpu_dfs) is {0}'.format(str(gpu_dfs)))
+
+wait(gpu_dfs)
+t1 = datetime.datetime.now()
+bst = dxgb_gpu.train(client, dxgb_gpu_params, gpu_dfs, labels, num_boost_round=dxgb_gpu_params['nround'])
+t2 = datetime.datetime.now()
+print("Training time ...")
+print(t2-t1)
+print('str(bst) is {0}'.format(str(bst)))
+print('Exiting script')

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

@@ -35,7 +35,7 @@ Below are the three execution environments supported by AutoML.
 **NOTE**: You should at least have contributor access to your Azure subcription to run the notebook.
 - Please remove the previous SDK version if there is any and install the latest SDK by installing **azureml-sdk[automl_databricks]** as a PyPi library in Azure Databricks workspace.
 - You can find the detail Readme instructions at [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks).
-- Download the sample notebook AutoML_Databricks_local_06.ipynb from [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks) and import into the Azure databricks workspace.
+- Download the sample notebook automl-databricks-local-01.ipynb from [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks) and import into the Azure databricks workspace.
 - Attach the notebook to the cluster.
 
 <a name="localconda"></a>
@@ -175,10 +175,6 @@ bash automl_setup_linux.sh
 - [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-whitelisting.ipynb](classification-with-whitelisting/auto-ml-classification-with-whitelisting.ipynb)
     - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
     - Simple example of using Auto ML for classification with whitelisting tensorflow models.

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

@@ -1,6 +1,7 @@
 @echo off
 set conda_env_name=%1
 set automl_env_file=%2
+set options=%3
 set PIP_NO_WARN_SCRIPT_LOCATION=0
 
 IF "%conda_env_name%"=="" SET conda_env_name="azure_automl"
@@ -23,15 +24,21 @@ if errorlevel 1 goto ErrorExit
 
 call python -m ipykernel install --user --name %conda_env_name% --display-name "Python (%conda_env_name%)"
 
+REM azureml.widgets is now installed as part of the pip install under the conda env.
+REM Removing the old user install so that the notebooks will use the latest widget.
+call jupyter nbextension uninstall --user --py azureml.widgets
+
 echo.
 echo.
 echo ***************************************
 echo * AutoML setup completed successfully *
 echo ***************************************
-echo.
-echo Starting jupyter notebook - please run the configuration notebook 
-echo.
-jupyter notebook --log-level=50 --notebook-dir='..\..'
+IF NOT "%options%"=="nolaunch" (
+  echo.
+  echo Starting jupyter notebook - please run the configuration notebook 
+  echo.
+  jupyter notebook --log-level=50 --notebook-dir='..\..'
+)
 
 goto End
 

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

@@ -2,6 +2,7 @@
 
 CONDA_ENV_NAME=$1
 AUTOML_ENV_FILE=$2
+OPTIONS=$3
 PIP_NO_WARN_SCRIPT_LOCATION=0
 
 if [ "$CONDA_ENV_NAME" == "" ]
@@ -22,20 +23,25 @@ fi
 if source activate $CONDA_ENV_NAME 2> /dev/null
 then
    echo "Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment" $CONDA_ENV_NAME
-   pip install --upgrade azureml-sdk[automl,notebooks,explain]
+   pip install --upgrade azureml-sdk[automl,notebooks,explain] &&
+   jupyter nbextension uninstall --user --py azureml.widgets
 else
    conda env create -f $AUTOML_ENV_FILE -n $CONDA_ENV_NAME &&
    source activate $CONDA_ENV_NAME &&
    python -m ipykernel install --user --name $CONDA_ENV_NAME --display-name "Python ($CONDA_ENV_NAME)" &&
+   jupyter nbextension uninstall --user --py azureml.widgets &&
    echo "" &&
    echo "" &&
    echo "***************************************" &&
    echo "* AutoML setup completed successfully *" &&
    echo "***************************************" &&
+   if [ "$OPTIONS" != "nolaunch" ]
+   then
       echo "" &&
       echo "Starting jupyter notebook - please run the configuration notebook" &&
       echo "" &&
       jupyter notebook --log-level=50 --notebook-dir '../..'
+   fi
 fi
 
 if [ $? -gt 0 ]

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

@@ -2,6 +2,7 @@
 
 CONDA_ENV_NAME=$1
 AUTOML_ENV_FILE=$2
+OPTIONS=$3
 PIP_NO_WARN_SCRIPT_LOCATION=0
 
 if [ "$CONDA_ENV_NAME" == "" ]
@@ -22,22 +23,27 @@ fi
 if source activate $CONDA_ENV_NAME 2> /dev/null
 then
    echo "Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment" $CONDA_ENV_NAME
-   pip install --upgrade azureml-sdk[automl,notebooks,explain]
+   pip install --upgrade azureml-sdk[automl,notebooks,explain] &&
+   jupyter nbextension uninstall --user --py azureml.widgets
 else
    conda env create -f $AUTOML_ENV_FILE -n $CONDA_ENV_NAME &&
    source activate $CONDA_ENV_NAME &&
    conda install lightgbm -c conda-forge -y &&
    python -m ipykernel install --user --name $CONDA_ENV_NAME --display-name "Python ($CONDA_ENV_NAME)" &&
-   pip install numpy==1.15.3
+   jupyter nbextension uninstall --user --py azureml.widgets &&
+   pip install numpy==1.15.3 &&
    echo "" &&
    echo "" &&
    echo "***************************************" &&
    echo "* AutoML setup completed successfully *" &&
    echo "***************************************" &&
+   if [ "$OPTIONS" != "nolaunch" ]
+   then
       echo "" &&
       echo "Starting jupyter notebook - please run the configuration notebook" &&
       echo "" &&
       jupyter notebook --log-level=50 --notebook-dir '../..'
+   fi
 fi
 
 if [ $? -gt 0 ]

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

@@ -62,11 +62,8 @@
       "source": [
         "import json\n",
         "import logging\n",
-    "import os\n",
-    "import random\n",
         "\n",
         "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
         "import numpy as np\n",
         "import pandas as pd\n",
         "from sklearn import datasets\n",
@@ -102,7 +99,8 @@
         "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data=output, index=['']).T"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
       ]
     },
     {
@@ -228,7 +226,8 @@
         "description = 'AutoML Model'\n",
         "tags = None\n",
         "model = local_run.register_model(description = description, tags = tags)\n",
-    "local_run.model_id # This will be written to the script file later in the notebook."
+        "\n",
+        "print(local_run.model_id) # This will be written to the script file later in the notebook."
       ]
     },
     {

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

@@ -61,11 +61,8 @@
       "outputs": [],
       "source": [
         "import logging\n",
-    "import os\n",
-    "import random\n",
         "\n",
         "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
         "import numpy as np\n",
         "import pandas as pd\n",
         "from sklearn import datasets\n",
@@ -73,8 +70,7 @@
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun"
+        "from azureml.train.automl import AutoMLConfig"
       ]
     },
     {
@@ -100,7 +96,8 @@
         "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data = output, index = ['']).T"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
       ]
     },
     {
@@ -135,8 +132,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "from sklearn import datasets\n",
-    "\n",
         "digits = datasets.load_digits()\n",
         "\n",
         "# Exclude the first 100 rows from training so that they can be used for test.\n",

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

@@ -60,11 +60,8 @@
       "outputs": [],
       "source": [
         "import logging\n",
-    "import os\n",
-    "import random\n",
         "\n",
         "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
         "import numpy as np\n",
         "import pandas as pd\n",
         "from sklearn import datasets\n",
@@ -72,8 +69,7 @@
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun"
+        "from azureml.train.automl import AutoMLConfig"
       ]
     },
     {
@@ -99,7 +95,8 @@
         "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data = output, index = ['']).T"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
       ]
     },
     {
@@ -134,8 +131,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "from sklearn import datasets\n",
-    "\n",
         "digits = datasets.load_digits()\n",
         "\n",
         "# Exclude the first 100 rows from training so that they can be used for test.\n",

how-to-use-azureml/automated-machine-learning/configuration.ipynb

@@ -1,154 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Automated Machine Learning Configuration\n",
-        "\n",
-        "In this example you will create an Azure Machine Learning `Workspace` object and initialize your notebook directory to easily reload this object from a configuration file.  Typically you will only need to run this once per notebook directory, and all other notebooks in this directory or any sub-directories will automatically use the settings you indicate here.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Check the Azure ML Core SDK Version to Validate Your Installation"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.core\n",
-        "\n",
-        "print(\"SDK Version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize an Azure ML Workspace\n",
-        "### What is an Azure ML Workspace and Why Do I Need One?\n",
-        "\n",
-        "An Azure ML workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an Azure ML workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, operationalization, and the monitoring of operationalized models.\n",
-        "\n",
-        "\n",
-        "### What do I Need?\n",
-        "\n",
-        "To create or access an Azure ML workspace, you will need to import the Azure ML library and specify following information:\n",
-        "* A name for your workspace. You can choose one.\n",
-        "* Your subscription id. Use the `id` value from the `az account show` command output above.\n",
-        "* The resource group name. The resource group organizes Azure resources and provides a default region for the resources in the group. The resource group will be created if it doesn't exist. Resource groups can be created and viewed in the [Azure portal](https://portal.azure.com)\n",
-        "* Supported regions include `eastus2`, `eastus`,`westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "subscription_id = \"<subscription_id>\"\n",
-        "resource_group = \"myrg\"\n",
-        "workspace_name = \"myws\"\n",
-        "workspace_region = \"eastus2\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Creating a Workspace\n",
-        "If you already have access to an Azure ML workspace you want to use, you can skip this cell.  Otherwise, this cell will create an Azure ML workspace for you in the specified subscription, provided you have the correct permissions for the given `subscription_id`.\n",
-        "\n",
-        "This will fail when:\n",
-        "1. The workspace already exists.\n",
-        "2. You do not have permission to create a workspace in the resource group.\n",
-        "3. You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription.\n",
-        "\n",
-        "If workspace creation fails for any reason other than already existing, please work with your IT administrator to provide you with the appropriate permissions or to provision the required resources.\n",
-        "\n",
-        "**Note:** Creation of a new workspace can take several minutes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Import the Workspace class and check the Azure ML SDK version.\n",
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.create(name = workspace_name,\n",
-        "                      subscription_id = subscription_id,\n",
-        "                      resource_group = resource_group, \n",
-        "                      location = workspace_region)\n",
-        "ws.get_details()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Configuring Your Local Environment\n",
-        "You can validate that you have access to the specified workspace and write a configuration file to the default configuration location, `./aml_config/config.json`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace(workspace_name = workspace_name,\n",
-        "               subscription_id = subscription_id,\n",
-        "               resource_group = resource_group)\n",
-        "\n",
-        "# Persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
-        "ws.write_config()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "savitam"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

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

@@ -80,7 +80,6 @@
       "outputs": [],
       "source": [
         "import logging\n",
-    "import os\n",
         "import time\n",
         "\n",
         "import pandas as pd\n",
@@ -117,7 +116,8 @@
         "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data = output, index = ['']).T"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
       ]
     },
     {
@@ -323,7 +323,6 @@
         "    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"
       ]
@@ -427,8 +426,6 @@
       "source": [
         "#Randomly select digits and test\n",
         "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
-    "import random\n",
         "import numpy as np\n",
         "\n",
         "for index in np.random.choice(len(y_test), 2, replace = False):\n",
@@ -482,7 +479,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "digits_complete.to_pandas_dataframe().shape\n",
+        "print(digits_complete.to_pandas_dataframe().shape)\n",
         "labels_column = 'Column64'\n",
         "dflow_X = digits_complete.drop_columns(columns = [labels_column])\n",
         "dflow_y = digits_complete.keep_columns(columns = [labels_column])"

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

@@ -80,7 +80,6 @@
       "outputs": [],
       "source": [
         "import logging\n",
-    "import os\n",
         "\n",
         "import pandas as pd\n",
         "\n",
@@ -115,7 +114,8 @@
         "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data = output, index = ['']).T"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
       ]
     },
     {
@@ -274,7 +274,6 @@
         "    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"
       ]
@@ -378,8 +377,6 @@
       "source": [
         "#Randomly select digits and test\n",
         "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
-    "import random\n",
         "import numpy as np\n",
         "\n",
         "for index in np.random.choice(len(y_test), 2, replace = False):\n",
@@ -433,7 +430,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "digits_complete.to_pandas_dataframe().shape\n",
+        "print(digits_complete.to_pandas_dataframe().shape)\n",
         "labels_column = 'Column64'\n",
         "dflow_X = digits_complete.drop_columns(columns = [labels_column])\n",
         "dflow_y = digits_complete.keep_columns(columns = [labels_column])"

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

@@ -53,22 +53,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "import logging\n",
-    "import os\n",
-    "import random\n",
-    "import re\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
-    "import numpy as np\n",
         "import pandas as pd\n",
-    "from sklearn import datasets\n",
+        "import json\n",
         "\n",
-    "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
-    "from azureml.core.run import Run\n",
         "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
         "from azureml.train.automl.run import AutoMLRun"
       ]
     },
@@ -152,7 +141,7 @@
         "for run in automl_runs:\n",
         "    properties = run.get_properties()\n",
         "    tags = run.get_tags()\n",
-    "    amlsettings = eval(properties['RawAMLSettingsString'])\n",
+        "    amlsettings = json.loads(properties['AMLSettingsJsonString'])\n",
         "    if 'iterations' in tags:\n",
         "        iterations = tags['iterations']\n",
         "    else:\n",
@@ -196,7 +185,7 @@
         "properties = ml_run.get_properties()\n",
         "tags = ml_run.get_tags()\n",
         "status = ml_run.get_details()\n",
-    "amlsettings = eval(properties['RawAMLSettingsString'])\n",
+        "amlsettings = json.loads(properties['AMLSettingsJsonString'])\n",
         "if 'iterations' in tags:\n",
         "    iterations = tags['iterations']\n",
         "else:\n",
@@ -297,7 +286,7 @@
         "description = 'AutoML Model'\n",
         "tags = None\n",
         "ml_run.register_model(description = description, tags = tags)\n",
-    "ml_run.model_id # Use this id to deploy the model as a web service in Azure."
+        "print(ml_run.model_id) # Use this id to deploy the model as a web service in Azure."
       ]
     },
     {

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

@@ -44,9 +44,31 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Setup\n",
-    "\n",
-    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
+        "## Setup\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To use the *forecasting* task in AutoML, you need to have the **azuremlftk** package installed in your environment. The following cell tests whether this package is installed locally and, if not, gives you instructions for installing it.  "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "try:\n",
+        "    import ftk\n",
+        "    print('Using FTK version ' + ftk.__version__)\n",
+        "except ImportError:\n",
+        "    print(\"Unable to import forecasting package. This notebook does not work without this package.\\n\"\n",
+        "          + \"Please open a command prompt and run `pip install azuremlftk` to install the package. \\n\"\n",
+        "          + \"Make sure you install the package into AutoML's Python environment.\\n\\n\"\n",
+        "          + \"For instance, if AutoML is installed in a conda environment called `python36`, run:\\n\"\n",
+        "          + \"> activate python36\\n> pip install azuremlftk\")"
       ]
     },
     {
@@ -58,7 +80,6 @@
         "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",
@@ -68,12 +89,17 @@
         "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": "markdown",
+      "metadata": {},
+      "source": [
+        "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -98,7 +124,8 @@
         "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"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
       ]
     },
     {
@@ -289,61 +316,6 @@
         "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": {},
@@ -357,7 +329,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "y_test,y_pred =  _check_calc_input(y_test,y_pred)"
+        "if len(y_test) != len(y_pred):\n",
+        "    raise ValueError(\n",
+        "        'the true values and prediction values do not have equal length.')\n",
+        "elif len(y_test) == 0:\n",
+        "    raise ValueError(\n",
+        "        'y_true and y_pred are empty.')\n",
+        "\n",
+        "# if there is any non-numeric element in the y_true or y_pred,\n",
+        "# the ValueError exception will be thrown.\n",
+        "y_test_f = np.array(y_test).astype(float)\n",
+        "y_pred_f = np.array(y_pred).astype(float)\n",
+        "\n",
+        "# remove entries both in y_true and y_pred where at least\n",
+        "# one element in y_true or y_pred is missing\n",
+        "y_test = y_test_f[~(np.isnan(y_test_f) | np.isnan(y_pred_f))]\n",
+        "y_pred = y_pred_f[~(np.isnan(y_test_f) | np.isnan(y_pred_f))]"
       ]
     },
     {
@@ -410,7 +397,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+      "version": "3.6.8"
     }
   },
   "nbformat": 4,

how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb

@@ -45,9 +45,31 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Setup\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. "
+        "## Setup"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To use the *forecasting* task in AutoML, you need to have the **azuremlftk** package installed in your environment. The following cell tests whether this package is installed locally and, if not, gives you instructions for installing it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "try:\n",
+        "    import ftk\n",
+        "    print('Using FTK version ' + ftk.__version__)\n",
+        "except ImportError:\n",
+        "    print(\"Unable to import forecasting package. This notebook does not work without this package.\\n\"\n",
+        "          + \"Please open a command prompt and run `pip install azuremlftk` to install the package. \\n\"\n",
+        "          + \"Make sure you install the package into AutoML's Python environment.\\n\\n\"\n",
+        "          + \"For instance, if AutoML is installed in a conda environment called `python36`, run:\\n\"\n",
+        "          + \"> activate python36\\n> pip install azuremlftk\")"
       ]
     },
     {
@@ -59,7 +81,6 @@
         "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",
@@ -69,10 +90,16 @@
         "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": "markdown",
+      "metadata": {},
+      "source": [
+        "As part of the setup you have already created a <b>Workspace</b>. To run AutoML, you also need to create an <b>Experiment</b>. An Experiment is a named object in a Workspace which represents a predictive task, the output of which is a trained model and a set of evaluation metrics for the model. "
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -97,7 +124,8 @@
         "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"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
       ]
     },
     {
@@ -405,7 +433,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+      "version": "3.6.8"
     }
   },
   "nbformat": 4,

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

@@ -63,11 +63,8 @@
       "outputs": [],
       "source": [
         "import logging\n",
-    "import os\n",
-    "import random\n",
         "\n",
         "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
         "import numpy as np\n",
         "import pandas as pd\n",
         "from sklearn import datasets\n",
@@ -75,8 +72,7 @@
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun"
+        "from azureml.train.automl import AutoMLConfig"
       ]
     },
     {
@@ -102,7 +98,8 @@
         "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data=output, index=['']).T"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
       ]
     },
     {
@@ -135,8 +132,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "from scipy import sparse\n",
-    "\n",
         "digits = datasets.load_digits()\n",
         "X_train = digits.data[10:,:]\n",
         "y_train = digits.target[10:]\n",

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

@@ -57,15 +57,12 @@
       "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"
+        "from azureml.train.automl import AutoMLConfig"
       ]
     },
     {
@@ -92,7 +89,8 @@
         "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data = output, index = ['']).T"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
       ]
     },
     {

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

@@ -58,20 +58,15 @@
       "outputs": [],
       "source": [
         "import logging\n",
-    "import os\n",
-    "import random\n",
         "\n",
         "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
         "import numpy as np\n",
         "import pandas as pd\n",
-    "from sklearn import datasets\n",
         "\n",
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun"
+        "from azureml.train.automl import AutoMLConfig"
       ]
     },
     {
@@ -97,7 +92,8 @@
         "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data = output, index = ['']).T"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
       ]
     },
     {
@@ -354,9 +350,6 @@
       "outputs": [],
       "source": [
         "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "from sklearn import datasets\n",
         "from sklearn.metrics import mean_squared_error, r2_score\n",
         "\n",
         "# Set up a multi-plot chart.\n",
@@ -375,8 +368,8 @@
         "a0.set_ylabel('Residual Values', fontsize = 12)\n",
         "\n",
         "# Plot a histogram.\n",
-    "a0.hist(y_residual_train, orientation = 'horizontal', color = 'b', bins = 10, histtype = 'step');\n",
-    "a0.hist(y_residual_train, orientation = 'horizontal', color = 'b', alpha = 0.2, bins = 10);\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",

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

@@ -66,21 +66,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "import logging\n",
         "import os\n",
-    "import random\n",
         "\n",
-    "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
         "import numpy as np\n",
         "import pandas as pd\n",
-    "from sklearn import datasets\n",
         "\n",
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun"
+        "from azureml.train.automl import AutoMLConfig"
       ]
     },
     {
@@ -106,7 +100,8 @@
         "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data=output, index=['']).T"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
       ]
     },
     {

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

@@ -67,10 +67,8 @@
       "source": [
         "import logging\n",
         "import os\n",
-    "import random\n",
         "\n",
         "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
         "import numpy as np\n",
         "import pandas as pd\n",
         "from sklearn import datasets\n",
@@ -78,8 +76,7 @@
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun"
+        "from azureml.train.automl import AutoMLConfig"
       ]
     },
     {
@@ -105,7 +102,8 @@
         "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data = output, index = ['']).T"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
       ]
     },
     {
@@ -170,7 +168,7 @@
         "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
         "    compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
         "    \n",
-    "     # For a more detailed view of current AmlCompute status, use the 'status' property."
+        "     # For a more detailed view of current AmlCompute status, use get_status()."
       ]
     },
     {

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

@@ -59,21 +59,16 @@
       "source": [
         "import logging\n",
         "import os\n",
-    "import random\n",
         "import time\n",
         "\n",
-    "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
         "import numpy as np\n",
         "import pandas as pd\n",
-    "from sklearn import datasets\n",
         "\n",
         "import azureml.core\n",
         "from azureml.core.compute import DsvmCompute\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun"
+        "from azureml.train.automl import AutoMLConfig"
       ]
     },
     {
@@ -100,7 +95,8 @@
         "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data=output, index=['']).T"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
       ]
     },
     {
@@ -169,7 +165,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "mkdir data"
+        "if not os.path.isdir('data'):\n",
+        "    os.mkdir('data')    "
       ]
     },
     {
@@ -218,7 +215,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "from azureml.core import Workspace, Datastore\n",
         "#blob_datastore = Datastore(ws, blob_datastore_name)\n",
         "ds = ws.get_default_datastore()\n",
         "print(ds.datastore_type, ds.account_name, ds.container_name)"

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

@@ -67,11 +67,9 @@
       "source": [
         "import logging\n",
         "import os\n",
-    "import random\n",
         "import time\n",
         "\n",
         "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
         "import numpy as np\n",
         "import pandas as pd\n",
         "from sklearn import datasets\n",
@@ -79,8 +77,7 @@
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun"
+        "from azureml.train.automl import AutoMLConfig"
       ]
     },
     {
@@ -106,7 +103,8 @@
         "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data = output, index = ['']).T"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
       ]
     },
     {
@@ -152,7 +150,7 @@
         "    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"
+        "    time.sleep(90) # Wait for ssh to be accessible"
       ]
     },
     {

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

@@ -51,11 +51,8 @@
       "outputs": [],
       "source": [
         "import logging\n",
-    "import os\n",
-    "import random\n",
         "\n",
         "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
         "import numpy as np\n",
         "import pandas as pd\n",
         "from sklearn import datasets\n",
@@ -63,8 +60,7 @@
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun"
+        "from azureml.train.automl import AutoMLConfig"
       ]
     },
     {
@@ -93,7 +89,8 @@
         "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data = output, index = ['']).T"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
       ]
     },
     {

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

@@ -61,20 +61,13 @@
       "outputs": [],
       "source": [
         "import logging\n",
-    "import os\n",
-    "import random\n",
         "\n",
-    "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
-    "import numpy as np\n",
         "import pandas as pd\n",
-    "from sklearn import datasets\n",
         "\n",
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun"
+        "from azureml.train.automl import AutoMLConfig"
       ]
     },
     {
@@ -101,7 +94,8 @@
         "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data=output, index=['']).T"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
       ]
     },
     {

how-to-use-azureml/azure-databricks/README.md

@@ -1,70 +1,26 @@
 Azure Databricks is a managed Spark offering on Azure and customers already use it for advanced analytics. It provides a collaborative Notebook based environment with CPU or GPU based compute cluster. 
 
-In this section, you will see sample notebooks on how to use Azure Machine Learning SDK with Azure Databricks. You can train a model using Spark MLlib and then deploy the model to ACI/AKS from within Azure Databricks. You can also use Automated ML capability (**public preview**) of Azure ML SDK with Azure Databricks. 
+In this section, you will find sample notebooks on how to use Azure Machine Learning SDK with Azure Databricks. You can train a model using Spark MLlib and then deploy the model to ACI/AKS from within Azure Databricks. You can also use Automated ML capability (**public preview**) of Azure ML SDK with Azure Databricks. 
 
 - Customers who use Azure Databricks for advanced analytics can now use the same cluster to run experiments with or without automated machine learning. 
 - You can keep the data within the same cluster. 
 - You can leverage the local worker nodes with autoscale and auto termination capabilities. 
 - You can use multiple cores of your Azure Databricks cluster to perform simultenous training. 
 - You can further tune the model generated by automated machine learning if you chose to. 
-- Every run (including the best run) is available as a pipeline. 
+- Every run (including the best run) is available as a pipeline, which you can tune further if needed. 
 - The model trained using Azure Databricks can be registered in Azure ML SDK workspace and then deployed to Azure managed compute (ACI or AKS) using the Azure Machine learning SDK.
 
-**Create Azure Databricks Cluster:**
+Please follow our [Azure doc](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#azure-databricks) to install the sdk in your Azure Databricks cluster before trying any of the sample notebooks.
 
-Select New Cluster and fill in following detail:
- - Cluster name: _yourclustername_
- - Databricks Runtime: Any 4.x runtime.
- - Python version: **3**
- - Workers: 2 or higher.  
+**Single file** - 
+The following archive contains all the sample notebooks. You can the run notebooks after importing [DBC](Databricks_AMLSDK_1-4_6.dbc) in your Databricks workspace instead of downloading individually.
 
-These settings are only for using Automated Machine Learning on Databricks.
- - Max. number of **concurrent iterations** in Automated ML settings is **<=** to the number of **worker nodes** in your Databricks cluster.
- - Worker node VM types: **Memory optimized VM** preferred. 
- - Uncheck _Enable Autoscaling_
+Notebooks 1-4 have to be run sequentially & are related to Income prediction experiment based on this [dataset](https://archive.ics.uci.edu/ml/datasets/adult) and demonstrate how to data prep, train and operationalize a Spark ML model with Azure ML Python SDK from within Azure Databricks. 
 
-
-It will take few minutes to create the cluster. Please ensure that the cluster state is running before proceeding further.
-
-**Install Azure ML SDK without Automated ML capability on your Azure Databricks cluster**
-
-- Select Import library
-
-- Source: Upload Python Egg or PyPI
-
-- PyPi Name: **azureml-sdk[databricks]**
-
-**Install Azure ML with Automated ML SDK on your Azure Databricks cluster**
-
-- Select Import library
-
-- Source: Upload Python Egg or PyPI
-
-- PyPi Name: **azureml-sdk[automl_databricks]**
-
-**For installation with or without Automated ML**
-
-- Click Install Library
-
-- Do not select _Attach automatically to all clusters_. In case you have selected earlier then you can go to your Home folder and deselect it.
-
-- Select the check box _Attach_ next to your cluster name
-
-(More details on how to attach and detach libs are here - [https://docs.databricks.com/user-guide/libraries.html#attach-a-library-to-a-cluster](https://docs.databricks.com/user-guide/libraries.html#attach-a-library-to-a-cluster) )
-
-- Ensure that there are no errors until Status changes to _Attached_. It may take a couple of minutes.
-
-**Note** - If you have the old build the please deselect it from cluster’s installed libs > move to trash. Install the new build and restart the cluster. And if still there is an issue then detach and reattach your cluster.
-
-iPython Notebooks 1-4 have to be run sequentially after making changes based on your subscription. The corresponding DBC archive contains all the notebooks and can be imported into your Databricks workspace. You can the run notebooks after importing [databricks_amlsdk](Databricks_AMLSDK_1-4_6.dbc) instead of downloading individually.
-
-Notebooks 1-4 are related to Income prediction experiment based on this [dataset](https://archive.ics.uci.edu/ml/datasets/adult) and demonstrate how to data prep, train and operationalize a Spark ML model with Azure ML Python SDK from within Azure Databricks. Notebook 6 is an Automated ML sample notebook.
-
-For details on SDK concepts, please refer to [notebooks](https://github.com/Azure/MachineLearningNotebooks).
+Notebook 6 is an Automated ML sample notebook for Classification.
 
 Learn more about [how to use Azure Databricks as a development environment](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment#azure-databricks) for Azure Machine Learning service.
 
-You can also use Azure Databricks as a compute target for [training models with an Azure Machine Learning pipeline](https://docs.microsoft.com/machine-learning/service/how-to-set-up-training-targets#databricks).
-
+For more on SDK concepts, please refer to [notebooks](https://github.com/Azure/MachineLearningNotebooks).
 
 **Please let us know your feedback.**

how-to-use-azureml/azure-databricks/amlsdk/build-model-run-history-03.ipynb

@@ -54,21 +54,6 @@
         "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,
@@ -91,15 +76,14 @@
       "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')"
+        "# 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')"
       ]
     },
     {
@@ -372,9 +356,9 @@
       }
     ],
     "kernelspec": {
-   "display_name": "Python 3",
+      "display_name": "Python 3.6",
       "language": "python",
-   "name": "python3"
+      "name": "python36"
     },
     "language_info": {
       "codemirror_mode": {

how-to-use-azureml/azure-databricks/amlsdk/deploy-to-aci-04.ipynb

@@ -39,21 +39,6 @@
         "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,
@@ -77,20 +62,19 @@
       "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",
-    "##'''"
+        "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",
+        "#'''"
       ]
     },
     {
@@ -330,9 +314,9 @@
       }
     ],
     "kernelspec": {
-   "display_name": "Python 3",
+      "display_name": "Python 3.6",
       "language": "python",
-   "name": "python3"
+      "name": "python36"
     },
     "language_info": {
       "codemirror_mode": {

how-to-use-azureml/azure-databricks/amlsdk/ingest-data-02.ipynb

@@ -158,9 +158,9 @@
       }
     ],
     "kernelspec": {
-   "display_name": "Python 3",
+      "display_name": "Python 3.6",
       "language": "python",
-   "name": "python3"
+      "name": "python36"
     },
     "language_info": {
       "codemirror_mode": {

how-to-use-azureml/azure-databricks/amlsdk/installation-and-configuration-01.ipynb

@@ -73,35 +73,6 @@
       "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",
@@ -111,29 +82,9 @@
         "                      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,
@@ -150,31 +101,14 @@
       "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",
+        "               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": "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>)"
+        "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>)"
       ]
     },
     {
@@ -192,11 +126,10 @@
       "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()\n",
         "#ws = Workspace.from_config(<full path>)\n",
         "print('Workspace name: ' + ws.name, \n",
         "      'Azure region: ' + ws.location, \n",
@@ -204,24 +137,6 @@
         "      '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,
@@ -240,9 +155,9 @@
       }
     ],
     "kernelspec": {
-   "display_name": "Python 3",
+      "display_name": "Python 3.6",
       "language": "python",
-   "name": "python3"
+      "name": "python36"
     },
     "language_info": {
       "codemirror_mode": {

how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb

@@ -13,45 +13,31 @@
       "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",
+        "# Automated ML on Azure Databricks\n",
         "\n",
-    "**install azureml-sdk with Automated ML**\n",
-    "* Source: Upload Python Egg or PyPi\n",
-    "* PyPi Name: `azureml-sdk[automl_databricks]`\n",
-    "* Select Install Library"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# AutoML : Classification with Local Compute on Azure DataBricks\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",
+        "In this example we use the scikit-learn's <a href=\"http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset\" target=\"_blank\">digit dataset</a> to showcase how you can use AutoML for a simple classification problem.\n",
         "\n",
         "In this notebook you will learn how to:\n",
         "1. Create Azure Machine Learning Workspace object and initialize your notebook directory to easily reload this object from a configuration file.\n",
         "2. Create an `Experiment` in an existing `Workspace`.\n",
-    "3. Configure AutoML using `AutoMLConfig`.\n",
-    "4. Train the model using AzureDataBricks.\n",
+        "3. Configure Automated ML using `AutoMLConfig`.\n",
+        "4. Train the model using Azure Databricks.\n",
         "5. Explore the results.\n",
         "6. Test the best fitted model.\n",
         "\n",
-    "Prerequisites:\n",
-    "Before running this notebook, please follow the readme for installing necessary libraries to your cluster."
+        "Before running this notebook, please follow the <a href=\"https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks\" target=\"_blank\">readme for using Automated ML on Azure Databricks</a> for installing necessary libraries to your cluster."
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## 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"
+        "We support installing AML SDK with Automated ML as library from GUI. When attaching a library follow <a href=\"https://docs.databricks.com/user-guide/libraries.html\" target=\"_blank\">this link</a> and add the below string as your PyPi package. You can select the option to attach the library to all clusters or just one cluster.\n",
+        "\n",
+        "**azureml-sdk with automated ml**\n",
+        "* Source: Upload Python Egg or PyPi\n",
+        "* PyPi Name: `azureml-sdk[automl_databricks]`\n",
+        "* Select Install Library"
       ]
     },
     {
@@ -97,11 +83,10 @@
       "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"
+        "subscription_id = \"<Your SubscriptionId>\" #you should be owner or contributor\n",
+        "resource_group = \"<Resource group - new or existing>\" #you should be owner or contributor\n",
+        "workspace_name = \"<workspace to be created>\" #your workspace name\n",
+        "workspace_region = \"<azureregion>\" #your region"
       ]
     },
     {
@@ -121,34 +106,6 @@
         "**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,
@@ -161,8 +118,7 @@
         "ws = Workspace.create(name = workspace_name,\n",
         "                      subscription_id = subscription_id,\n",
         "                      resource_group = resource_group, \n",
-    "                      location = workspace_region,\n",
-    "                      auth = auth,\n",
+        "                      location = workspace_region,                      \n",
         "                      exist_ok=True)\n",
         "ws.get_details()"
       ]
@@ -172,22 +128,7 @@
       "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",
-    "##'''"
-   ]
+      "source": []
     },
     {
       "cell_type": "markdown",
@@ -203,35 +144,16 @@
       "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",
+        "               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": "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": {},
@@ -262,7 +184,7 @@
       "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."
+        "As part of the setup you have already created an Azure ML `Workspace` object. For Automated ML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
       ]
     },
     {
@@ -288,26 +210,6 @@
         "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,
@@ -364,6 +266,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "#Automated ML requires a dataflow, which is different from dataframe.\n",
+        "#If your data is in a dataframe, please use read_pandas_dataframe to convert a dataframe to dataflow before usind dprep.\n",
+        "\n",
         "import azureml.dataprep as dprep\n",
         "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
         "# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
@@ -435,7 +340,6 @@
         "                             spark_context=sc, #databricks/spark related\n",
         "                             X = X_train, \n",
         "                             y = y_train,\n",
-    "                             enable_cache=False,\n",
         "                             path = project_folder)"
       ]
     },
@@ -480,7 +384,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "print(local_run.get_portal_url())"
+        "displayHTML(\"<a href={} target='_blank'>Your experiment in Azure Portal: {}</a>\".format(local_run.get_portal_url(), local_run.id))"
       ]
     },
     {
@@ -608,7 +512,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "When deploying an automated ML trained model, please specify _pip_packages=['azureml-sdk[automl]']_ in your CondaDependencies."
+        "When deploying an automated ML trained model, please specify _pippackages=['azureml-sdk[automl]']_ in your CondaDependencies.\n",
+        "\n",
+        "Please refer to only the **Deploy** section in this notebook - <a href=\"https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification-with-deployment\" target=\"_blank\">Deployment of Automated ML trained model</a>"
       ]
     },
     {
@@ -629,9 +535,9 @@
       }
     ],
     "kernelspec": {
-   "display_name": "Python 3",
+      "display_name": "Python 3.6",
       "language": "python",
-   "name": "python3"
+      "name": "python36"
     },
     "language_info": {
       "codemirror_mode": {
@@ -646,8 +552,8 @@
       "version": "3.7.0"
     },
     "name": "auto-ml-classification-local-adb",
-  "notebookId": 3836944406456411
+    "notebookId": 817220787969977
   },
   "nbformat": 4,
- "nbformat_minor": 1
+  "nbformat_minor": 0
 }

how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb

@@ -0,0 +1,704 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package. You can select the option to attach the library to all clusters or just one cluster.\n",
+        "\n",
+        "**install azureml-sdk with Automated ML**\n",
+        "* Source: Upload Python Egg or PyPi\n",
+        "* PyPi Name: `azureml-sdk[automl_databricks]`\n",
+        "* Select Install Library"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# AutoML : Classification with Local Compute on Azure DataBricks with deployment to ACI\n",
+        "\n",
+        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
+        "\n",
+        "In this notebook you will learn how to:\n",
+        "1. Create Azure Machine Learning Workspace object and initialize your notebook directory to easily reload this object from a configuration file.\n",
+        "2. Create an `Experiment` in an existing `Workspace`.\n",
+        "3. Configure AutoML using `AutoMLConfig`.\n",
+        "4. Train the model using AzureDataBricks.\n",
+        "5. Explore the results.\n",
+        "6. Register the model.\n",
+        "7. Deploy the model.\n",
+        "8. Test the best fitted model.\n",
+        "\n",
+        "Prerequisites:\n",
+        "Before running this notebook, please follow the readme for installing necessary libraries to your cluster."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Register Machine Learning Services Resource Provider\n",
+        "Microsoft.MachineLearningServices only needs to be registed once in the subscription. To register it:\n",
+        "Start the Azure portal.\n",
+        "Select your All services and then Subscription.\n",
+        "Select the subscription that you want to use.\n",
+        "Click on Resource providers\n",
+        "Click the Register link next to Microsoft.MachineLearningServices"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Check the Azure ML Core SDK Version to Validate Your Installation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK Version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize an Azure ML Workspace\n",
+        "### What is an Azure ML Workspace and Why Do I Need One?\n",
+        "\n",
+        "An Azure ML workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an Azure ML workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, operationalization, and the monitoring of operationalized models.\n",
+        "\n",
+        "\n",
+        "### What do I Need?\n",
+        "\n",
+        "To create or access an Azure ML workspace, you will need to import the Azure ML library and specify following information:\n",
+        "* A name for your workspace. You can choose one.\n",
+        "* Your subscription id. Use the `id` value from the `az account show` command output above.\n",
+        "* The resource group name. The resource group organizes Azure resources and provides a default region for the resources in the group. The resource group will be created if it doesn't exist. Resource groups can be created and viewed in the [Azure portal](https://portal.azure.com)\n",
+        "* Supported regions include `eastus2`, `eastus`,`westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "subscription_id = \"<Your SubscriptionId>\"\n",
+        "resource_group = \"<Resource group - new or existing>\"\n",
+        "workspace_name = \"<workspace to be created>\"\n",
+        "workspace_region = \"<azureregion>\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Creating a Workspace\n",
+        "If you already have access to an Azure ML workspace you want to use, you can skip this cell.  Otherwise, this cell will create an Azure ML workspace for you in the specified subscription, provided you have the correct permissions for the given `subscription_id`.\n",
+        "\n",
+        "This will fail when:\n",
+        "1. The workspace already exists.\n",
+        "2. You do not have permission to create a workspace in the resource group.\n",
+        "3. You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription.\n",
+        "\n",
+        "If workspace creation fails for any reason other than already existing, please work with your IT administrator to provide you with the appropriate permissions or to provision the required resources.\n",
+        "\n",
+        "**Note:** Creation of a new workspace can take several minutes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Import the Workspace class and check the Azure ML SDK version.\n",
+        "from azureml.core import Workspace\n",
+        "\n",
+        "ws = Workspace.create(name = workspace_name,\n",
+        "                      subscription_id = subscription_id,\n",
+        "                      resource_group = resource_group, \n",
+        "                      location = workspace_region,\n",
+        "                      exist_ok=True)\n",
+        "ws.get_details()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Configuring Your Local Environment\n",
+        "You can validate that you have access to the specified workspace and write a configuration file to the default configuration location, `./aml_config/config.json`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace\n",
+        "\n",
+        "ws = Workspace(workspace_name = workspace_name,\n",
+        "               subscription_id = subscription_id,\n",
+        "               resource_group = resource_group)\n",
+        "\n",
+        "# Persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
+        "ws.write_config()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create a Folder to Host Sample Projects\n",
+        "Finally, create a folder where all the sample projects will be hosted."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "sample_projects_folder = './sample_projects'\n",
+        "\n",
+        "if not os.path.isdir(sample_projects_folder):\n",
+        "    os.mkdir(sample_projects_folder)\n",
+        "    \n",
+        "print('Sample projects will be created in {}.'.format(sample_projects_folder))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create an Experiment\n",
+        "\n",
+        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import logging\n",
+        "import os\n",
+        "import random\n",
+        "import time\n",
+        "\n",
+        "from matplotlib import pyplot as plt\n",
+        "from matplotlib.pyplot import imshow\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "import azureml.core\n",
+        "from azureml.core.experiment import Experiment\n",
+        "from azureml.core.workspace import Workspace\n",
+        "from azureml.train.automl import AutoMLConfig\n",
+        "from azureml.train.automl.run import AutoMLRun"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Choose a name for the experiment and specify the project folder.\n",
+        "experiment_name = 'automl-local-classification'\n",
+        "project_folder = './sample_projects/automl-local-classification'\n",
+        "\n",
+        "experiment = Experiment(ws, experiment_name)\n",
+        "\n",
+        "output = {}\n",
+        "output['SDK version'] = azureml.core.VERSION\n",
+        "output['Subscription ID'] = ws.subscription_id\n",
+        "output['Workspace Name'] = ws.name\n",
+        "output['Resource Group'] = ws.resource_group\n",
+        "output['Location'] = ws.location\n",
+        "output['Project Directory'] = project_folder\n",
+        "output['Experiment Name'] = experiment.name\n",
+        "pd.set_option('display.max_colwidth', -1)\n",
+        "pd.DataFrame(data = output, index = ['']).T"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Diagnostics\n",
+        "\n",
+        "Opt-in diagnostics for better experience, quality, and security of future releases."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.telemetry import set_diagnostics_collection\n",
+        "set_diagnostics_collection(send_diagnostics = True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load Training Data Using DataPrep"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import azureml.dataprep as dprep\n",
+        "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
+        "# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
+        "simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
+        "X_train = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1)  # Remove the header row.\n",
+        "\n",
+        "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
+        "# and convert column types manually.\n",
+        "# Here we read a comma delimited file and convert all columns to integers.\n",
+        "y_train = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Review the Data Preparation Result\n",
+        "You can peek the result of a Dataflow at any range using skip(i) and head(j). Doing so evaluates only j records for all the steps in the Dataflow, which makes it fast even against large datasets."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_train.skip(1).head(5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Configure AutoML\n",
+        "\n",
+        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
+        "\n",
+        "|Property|Description|\n",
+        "|-|-|\n",
+        "|**task**|classification or regression|\n",
+        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
+        "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
+        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+        "|**n_cross_validations**|Number of cross validation splits.|\n",
+        "|**spark_context**|Spark Context object. for Databricks, use spark_context=sc|\n",
+        "|**max_concurrent_iterations**|Maximum number of iterations to execute in parallel. This should be <= number of worker nodes in your Azure Databricks cluster.|\n",
+        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+        "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
+        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
+        "|**preprocess**|set this to True to enable pre-processing of data eg. string to numeric using one-hot encoding|\n",
+        "|**exit_score**|Target score for experiment. It is associated with the metric. eg. exit_score=0.995 will exit experiment after that|"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "automl_config = AutoMLConfig(task = 'classification',\n",
+        "                             debug_log = 'automl_errors.log',\n",
+        "                             primary_metric = 'AUC_weighted',\n",
+        "                             iteration_timeout_minutes = 10,\n",
+        "                             iterations = 5,\n",
+        "                             n_cross_validations = 2,\n",
+        "                             max_concurrent_iterations = 4, #change it based on number of worker nodes\n",
+        "                             verbosity = logging.INFO,\n",
+        "                             spark_context=sc, #databricks/spark related\n",
+        "                             X = X_train, \n",
+        "                             y = y_train,\n",
+        "                             enable_cache=False,\n",
+        "                             path = project_folder)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train the Models\n",
+        "\n",
+        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
+        "In this example, we specify `show_output = True` to print currently running iterations to the console."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "local_run = experiment.submit(automl_config, show_output = True) # for higher runs please use show_output=False and use the below"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Explore the Results"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Portal URL for Monitoring Runs\n",
+        "\n",
+        "The following will provide a link to the web interface to explore individual run details and status. In the future we might support output displayed in the notebook."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "displayHTML(\"<a href={} target='_blank'>Azure Portal: {}</a>\".format(local_run.get_portal_url(), local_run.id))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The following will show the child runs and waits for the parent run to complete."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Retrieve All Child Runs after the experiment is completed (in portal)\n",
+        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "children = list(local_run.get_children())\n",
+        "metricslist = {}\n",
+        "for run in children:\n",
+        "    properties = run.get_properties()\n",
+        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
+        "    metricslist[int(properties['iteration'])] = metrics\n",
+        "\n",
+        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+        "rundata"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Retrieve the Best Model after the above run is complete \n",
+        "\n",
+        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "best_run, fitted_model = local_run.get_output()\n",
+        "print(best_run)\n",
+        "print(fitted_model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Best Model Based on Any Other Metric after the above run is complete based on the child run\n",
+        "Show the run and the model that has the smallest `log_loss` value:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "lookup_metric = \"log_loss\"\n",
+        "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
+        "print(best_run)\n",
+        "print(fitted_model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Register the Fitted Model for Deployment\n",
+        "If neither metric nor iteration are specified in the register_model call, the iteration with the best primary metric is registered."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "description = 'AutoML Model'\n",
+        "tags = None\n",
+        "model = local_run.register_model(description = description, tags = tags)\n",
+        "local_run.model_id # This will be written to the scoring script file later in the notebook."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create Scoring Script\n",
+        "Replace model_id with name of model from output of above register cell"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%writefile score.py\n",
+        "import pickle\n",
+        "import json\n",
+        "import numpy\n",
+        "import azureml.train.automl\n",
+        "from sklearn.externals import joblib\n",
+        "from azureml.core.model import Model\n",
+        "\n",
+        "\n",
+        "def init():\n",
+        "    global model\n",
+        "    model_path = Model.get_model_path(model_name = '<<modelid>>') # this name is model.id of model that we want to deploy\n",
+        "    # deserialize the model file back into a sklearn model\n",
+        "    model = joblib.load(model_path)\n",
+        "\n",
+        "def run(rawdata):\n",
+        "    try:\n",
+        "        data = json.loads(rawdata)['data']\n",
+        "        data = numpy.array(data)\n",
+        "        result = model.predict(data)\n",
+        "    except Exception as e:\n",
+        "        result = str(e)\n",
+        "        return json.dumps({\"error\": result})\n",
+        "    return json.dumps({\"result\":result.tolist()})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Create a YAML File for the Environment"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "\n",
+        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-sdk[automl]'])\n",
+        "\n",
+        "conda_env_file_name = 'mydeployenv.yml'\n",
+        "myenv.save_to_file('.', conda_env_file_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Create ACI config"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#deploy to ACI\n",
+        "from azureml.core.webservice import AciWebservice, Webservice\n",
+        "\n",
+        "myaci_config = AciWebservice.deploy_configuration(\n",
+        "    cpu_cores = 2, \n",
+        "    memory_gb = 2, \n",
+        "    tags = {'name':'Databricks Azure ML ACI'}, \n",
+        "    description = 'This is for ADB and AutoML example.')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Deploy the Image as a Web Service on Azure Container Instance\n",
+        "Replace servicename with any meaningful name of service"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "\n",
+        "# this will take 10-15 minutes to finish\n",
+        "\n",
+        "service_name = \"<<servicename>>\"\n",
+        "runtime = \"spark-py\" \n",
+        "driver_file = \"score.py\"\n",
+        "my_conda_file = \"mydeployenv.yml\"\n",
+        "\n",
+        "# image creation\n",
+        "from azureml.core.image import ContainerImage\n",
+        "myimage_config = ContainerImage.image_configuration(execution_script = driver_file, \n",
+        "                                    runtime = runtime, \n",
+        "                                    conda_file = 'mydeployenv.yml')\n",
+        "\n",
+        "# Webservice creation\n",
+        "myservice = Webservice.deploy_from_model(\n",
+        "  workspace=ws, \n",
+        "  name=service_name,\n",
+        "  deployment_config = myaci_config,\n",
+        "  models = [model],\n",
+        "  image_config = myimage_config\n",
+        "    )\n",
+        "\n",
+        "myservice.wait_for_deployment(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#for using the Web HTTP API \n",
+        "print(myservice.scoring_uri)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Test the Best Fitted Model\n",
+        "\n",
+        "#### Load Test Data - you can split the dataset beforehand & pass Train dataset to AutoML and use Test dataset to evaluate the best model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn import datasets\n",
+        "digits = datasets.load_digits()\n",
+        "X_test = digits.data[:10, :]\n",
+        "y_test = digits.target[:10]\n",
+        "images = digits.images[:10]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Testing Our Best Fitted Model\n",
+        "We will try to predict digits and see how our model works. This is just an example to show you."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Randomly select digits and test.\n",
+        "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+        "    print(index)\n",
+        "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+        "    label = y_test[index]\n",
+        "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
+        "    fig = plt.figure(1, figsize = (3,3))\n",
+        "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+        "    ax1.set_title(title)\n",
+        "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+        "    display(fig)"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "savitam"
+      },
+      {
+        "name": "wamartin"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.0"
+    },
+    "name": "auto-ml-classification-local-adb",
+    "notebookId": 3888835968049288
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

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

@@ -38,13 +38,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "from azureml.core import Workspace, Run\n",
+        "from azureml.core import Workspace\n",
         "from azureml.core.compute import AksCompute, ComputeTarget\n",
-    "from azureml.core.webservice import Webservice, AksWebservice\n",
-    "from azureml.core.image import Image\n",
-    "from azureml.core.model import Model\n",
-    "\n",
+        "from azureml.core.webservice import AksWebservice\n",
         "import azureml.core\n",
+        "import json\n",
         "print(azureml.core.VERSION)"
       ]
     },
@@ -247,7 +245,6 @@
       "outputs": [],
       "source": [
         "%%time\n",
-    "import json\n",
         "\n",
         "test_sample = json.dumps({'data': [\n",
         "    [1,28,13,45,54,6,57,8,8,10], \n",
@@ -401,7 +398,6 @@
       "outputs": [],
       "source": [
         "%%time\n",
-    "import json\n",
         "\n",
         "test_sample = json.dumps({'data': [\n",
         "    [1,28,13,45,54,6,57,8,8,10], \n",
@@ -469,13 +465,13 @@
   "metadata": {
     "authors": [
       {
-    "name": "marthalc"
+        "name": "jocier"
       }
     ],
     "kernelspec": {
-   "display_name": "Python [default]",
+      "display_name": "Python 3.6",
       "language": "python",
-   "name": "python3"
+      "name": "python36"
     },
     "language_info": {
       "codemirror_mode": {
@@ -487,7 +483,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+      "version": "3.6.3"
     }
   },
   "nbformat": 4,

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

@@ -37,12 +37,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "from azureml.core import Workspace, Run\n",
+        "from azureml.core import Workspace\n",
         "from azureml.core.compute import AksCompute, ComputeTarget\n",
         "from azureml.core.webservice import Webservice, AksWebservice\n",
-    "from azureml.core.image import Image\n",
-    "from azureml.core.model import Model\n",
-    "\n",
         "import azureml.core\n",
         "print(azureml.core.VERSION)"
       ]
@@ -51,8 +48,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## 2. Set up your configuration and create a workspace\n",
-    "Follow Notebook 00 instructions to do this.\n"
+        "## 2. Set up your configuration and create a workspace"
       ]
     },
     {
@@ -277,9 +273,7 @@
     },
     {
       "cell_type": "markdown",
-   "metadata": {
-    "scrolled": true
-   },
+      "metadata": {},
       "source": [
         "```python \n",
         "    %%time\n",
@@ -403,11 +397,11 @@
       "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",
+        "2. Select \u201cGet Data\u201d and click on \u201cAzure Blob Storage\u201d >> Connect\n",
         "3. Add your storage account and enter your storage key.\n",
         "4. Select the container where your Data Collection is stored and click on Edit. \n",
-    "5. In the query editor, click under “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",
+        "5. In the query editor, click under \u201cName\u201d column and add your Storage account Model path into the filter. Note: if you want to only look into files from a specific year or month, just expand the filter path. For example, just look into March data: /modeldata/subscriptionid>/resourcegroupname>/workspacename>/webservicename>/modelname>/modelversion>/identifier>/year>/3\n",
+        "6. Click on the double arrow aside the \u201cContent\u201d column to combine the files. \n",
         "7. Click OK and the data will preload.\n",
         "8. You can now click Close and Apply and start building your custom reports on your Model Input data."
       ]
@@ -451,13 +445,13 @@
   "metadata": {
     "authors": [
       {
-    "name": "marthalc"
+        "name": "jocier"
       }
     ],
     "kernelspec": {
-   "display_name": "Python [default]",
+      "display_name": "Python 3.6",
       "language": "python",
-   "name": "python3"
+      "name": "python36"
     },
     "language_info": {
       "codemirror_mode": {
@@ -469,7 +463,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+      "version": "3.6.3"
     }
   },
   "nbformat": 4,

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

@@ -4,7 +4,7 @@ These tutorials show how to create and deploy Open Neural Network eXchange ([ONN
 
 ## Tutorials
 
-0. [Configure your Azure Machine Learning Workspace](https://github.com/Azure/MachineLearningNotebooks/blob/master/configuration.ipynb)
+0. [Configure your Azure Machine Learning Workspace](../../../configuration.ipynb)
 
 #### Obtain models from the [ONNX Model Zoo](https://github.com/onnx/models) and deploy with ONNX Runtime Inference
 1. [Handwritten Digit Classification (MNIST)](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb)

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

@@ -33,7 +33,7 @@
         "To make the best use of your time, make sure you have done the following:\n",
         "\n",
         "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
-    "* Go through the [00.configuration.ipynb](../00.configuration.ipynb) notebook to:\n",
+        "* Go through the [configuration](../../../configuration.ipynb) notebook to:\n",
         "    * install the AML SDK\n",
         "    * create a workspace and its configuration file (config.json)"
       ]
@@ -71,7 +71,7 @@
       "source": [
         "## Convert model to ONNX\n",
         "\n",
-    "First we download the CoreML model. We use the CoreML model listed at https://coreml.store/tinyyolo. This may take a few minutes."
+        "First we download the CoreML model. We use the CoreML model from [Matthijs Hollemans's tutorial](https://github.com/hollance/YOLO-CoreML-MPSNNGraph). This may take a few minutes."
       ]
     },
     {
@@ -82,8 +82,8 @@
       "source": [
         "import urllib.request\n",
         "\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"
+        "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"
       ]
     },
     {
@@ -409,7 +409,7 @@
   "metadata": {
     "authors": [
       {
-    "name": "onnx"
+        "name": "viswamy"
       }
     ],
     "kernelspec": {
@@ -427,7 +427,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-   "version": "3.5.6"
+      "version": "3.6.5"
     }
   },
   "nbformat": 4,

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

@@ -34,7 +34,7 @@
         "## Prerequisites\n",
         "\n",
         "### 1. Install Azure ML SDK and create a new workspace\n",
-    "Please follow [Azure ML configuration notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) to set up your environment.\n",
+        "Please follow [Azure ML configuration notebook](../../../configuration.ipynb) to set up your environment.\n",
         "\n",
         "### 2. Install additional packages needed for this 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",
@@ -197,7 +197,6 @@
       "source": [
         "# for images and plots in this notebook\n",
         "import matplotlib.pyplot as plt  \n",
-    "from IPython.display import Image\n",
         "\n",
         "# display images inline\n",
         "%matplotlib inline"
@@ -481,8 +480,8 @@
         "    \n",
         "    emotion_keys = list(emotion_table.keys())\n",
         "    emotions = []\n",
-    "    for i in range(N):\n",
-    "        emotions.append(emotion_keys[classes[i]])\n",
+        "    for c in range(N):\n",
+        "        emotions.append(emotion_keys[classes[c]])\n",
         "    return emotions\n",
         "\n",
         "def softmax(x):\n",
@@ -534,9 +533,9 @@
         "# read in 3 testing images from .pb files\n",
         "test_data_size = 3\n",
         "\n",
-    "for i in np.arange(test_data_size):\n",
-    "    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",
+        "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",
         "    \n",
         "    # convert protobuf tensors to np arrays using the TensorProto reader from ONNX\n",
         "    tensor = onnx.TensorProto()\n",
@@ -671,19 +670,19 @@
         "    \"\"\"Convert the input image into grayscale\"\"\"\n",
         "    return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])\n",
         "\n",
-    "def resize_img(img):\n",
-    "    \"\"\"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",
+        "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",
         "\n",
-    "def preprocess(img):\n",
+        "def preprocess(img_to_preprocess):\n",
         "    \"\"\"Resize input images and convert them to grayscale.\"\"\"\n",
-    "    if img.shape == (64, 64):\n",
-    "        img.resize((1, 1, 64, 64))\n",
-    "        return img\n",
+        "    if img_to_preprocess.shape == (64, 64):\n",
+        "        img_to_preprocess.resize((1, 1, 64, 64))\n",
+        "        return img_to_preprocess\n",
         "    \n",
-    "    grayscale = rgb2gray(img)\n",
+        "    grayscale = rgb2gray(img_to_preprocess)\n",
         "    processed_img = resize_img(grayscale)\n",
         "    return processed_img"
       ]
@@ -732,7 +731,7 @@
         "        r = json.loads(aci_service.run(input_data))\n",
         "        result = r['result'][0]\n",
         "        time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n",
-    "    except Exception as e:\n",
+        "    except KeyError as e:\n",
         "        print(str(e))\n",
         "\n",
         "    plt.figure(figsize = (16, 6))\n",
@@ -800,7 +799,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+      "version": "3.6.5"
     },
     "msauthor": "vinitra.swamy"
   },

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

@@ -34,7 +34,7 @@
         "## Prerequisites\n",
         "\n",
         "### 1. Install Azure ML SDK and create a new workspace\n",
-    "Please follow [Azure ML configuration notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) to set up your environment.\n",
+        "Please follow [Azure ML configuration notebook](../../../configuration.ipynb) to set up your environment.\n",
         "\n",
         "### 2. Install additional packages needed for this tutorial notebook\n",
         "You need to install the popular plotting library `matplotlib`, the image manipulation library `opencv`, and the `onnx` library in the conda environment where Azure Maching Learning SDK is installed. \n",
@@ -621,19 +621,19 @@
         "    \"\"\"Convert the input image into grayscale\"\"\"\n",
         "    return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])\n",
         "\n",
-    "def resize_img(img):\n",
+        "def resize_img(img_to_resize):\n",
         "    \"\"\"Resize image to MNIST model input dimensions\"\"\"\n",
-    "    img = cv2.resize(img, dsize=(28, 28), interpolation=cv2.INTER_AREA)\n",
-    "    img.resize((1, 1, 28, 28))\n",
-    "    return img\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",
         "\n",
-    "def preprocess(img):\n",
+        "def preprocess(img_to_preprocess):\n",
         "    \"\"\"Resize input images and convert them to grayscale.\"\"\"\n",
-    "    if img.shape == (28, 28):\n",
-    "        img.resize((1, 1, 28, 28))\n",
-    "        return img\n",
+        "    if img_to_preprocess.shape == (28, 28):\n",
+        "        img_to_preprocess.resize((1, 1, 28, 28))\n",
+        "        return img_to_preprocess\n",
         "    \n",
-    "    grayscale = rgb2gray(img)\n",
+        "    grayscale = rgb2gray(img_to_preprocess)\n",
         "    processed_img = resize_img(grayscale)\n",
         "    return processed_img"
       ]
@@ -681,7 +681,7 @@
         "        r = aci_service.run(input_data)\n",
         "        result = r['result']\n",
         "        time_ms = np.round(r['time_in_sec'] * 1000, 2)\n",
-    "    except Exception as e:\n",
+        "    except KeyError as e:\n",
         "        print(str(e))\n",
         "\n",
         "    plt.figure(figsize = (16, 6))\n",

how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb

@@ -33,7 +33,7 @@
         "To make the best use of your time, make sure you have done the following:\n",
         "\n",
         "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
-    "* Go through the [00.configuration.ipynb](../00.configuration.ipynb) notebook to:\n",
+        "* Go through the [configuration notebook](../../../configuration.ipynb) to:\n",
         "    * install the AML SDK\n",
         "    * create a workspace and its configuration file (config.json)"
       ]
@@ -393,7 +393,7 @@
   "metadata": {
     "authors": [
       {
-    "name": "onnx"
+        "name": "viswamy"
       }
     ],
     "kernelspec": {
@@ -411,7 +411,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-   "version": "3.5.6"
+      "version": "3.6.5"
     }
   },
   "nbformat": 4,

how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb

@@ -13,7 +13,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## 10. Register Model, Create Image and Deploy Service\n",
+        "## Register Model, Create Image and Deploy Service\n",
         "\n",
         "This example shows how to deploy a web service in step-by-step fashion:\n",
         "\n",
@@ -24,9 +24,9 @@
         " 5. Deploy the image as web service\n",
         " \n",
         "**IMPORTANT**:\n",
-    " * This notebook requires you to first complete \"01.SDK-101-Train-and-Deploy-to-ACI.ipynb\" Notebook\n",
+        " * This notebook requires you to first complete [train-within-notebook](../../training/train-within-notebook/train-within-notebook.ipynb) example\n",
         " \n",
-    "The 101 Notebook taught you how to deploy a web service directly from model in one step. This Notebook shows a more advanced approach that gives you more control over model versions and Docker image versions.  "
+        "The train-within-notebook example taught you how to deploy a web service directly from model in one step. This Notebook shows a more advanced approach that gives you more control over model versions and Docker image versions.  "
       ]
     },
     {
@@ -34,7 +34,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-    "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
+        "Make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
       ]
     },
     {
@@ -241,7 +241,8 @@
         "                                 description = \"Image with ridge regression model\")\n",
         "\n",
         "image = Image.create(name = \"myimage1\",\n",
-    "                     # this is the model object \n",
+        "                     # this is the model object. note you can pass in 0-n models via this list-type parameter\n",
+        "                     # in case you need to reference multiple models, or none at all, in your scoring script.\n",
         "                     models = [model],\n",
         "                     image_config = image_config, \n",
         "                     workspace = ws)"

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb

@@ -35,16 +35,12 @@
       "source": [
         "import os\n",
         "import azureml.core\n",
-    "from azureml.core.compute import ComputeTarget, DatabricksCompute, DataFactoryCompute\n",
+        "from azureml.core.compute import ComputeTarget, DataFactoryCompute\n",
         "from azureml.exceptions import ComputeTargetException\n",
-    "from azureml.core import Workspace, Run, Experiment\n",
-    "from azureml.pipeline.core import Pipeline, PipelineData\n",
-    "from azureml.pipeline.steps import AdlaStep\n",
+        "from azureml.core import Workspace, Experiment\n",
+        "from azureml.pipeline.core import Pipeline\n",
         "from azureml.core.datastore import Datastore\n",
         "from azureml.data.data_reference import DataReference\n",
-    "from azureml.data.sql_data_reference import SqlDataReference\n",
-    "from azureml.core import attach_legacy_compute_target\n",
-    "from azureml.data.stored_procedure_parameter import StoredProcedureParameter, StoredProcedureParameterType\n",
         "from azureml.pipeline.steps import DataTransferStep\n",
         "\n",
         "# Check core SDK version number\n",
@@ -98,8 +94,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "from msrest.exceptions import HttpOperationError\n",
         "\n",
-    "workspace = ws.name\n",
         "datastore_name='MyAdlsDatastore'\n",
         "subscription_id=os.getenv(\"ADL_SUBSCRIPTION_62\", \"<my-subscription-id>\") # subscription id of ADLS account\n",
         "resource_group=os.getenv(\"ADL_RESOURCE_GROUP_62\", \"<my-resource-group>\") # resource group of ADLS account\n",
@@ -111,7 +107,7 @@
         "try:\n",
         "    adls_datastore = Datastore.get(ws, datastore_name)\n",
         "    print(\"found datastore with name: %s\" % datastore_name)\n",
-    "except:\n",
+        "except HttpOperationError:\n",
         "    adls_datastore = Datastore.register_azure_data_lake(\n",
         "        workspace=ws,\n",
         "        datastore_name=datastore_name,\n",
@@ -133,7 +129,7 @@
         "try:\n",
         "    blob_datastore = Datastore.get(ws, blob_datastore_name)\n",
         "    print(\"found blob datastore with name: %s\" % blob_datastore_name)\n",
-    "except:\n",
+        "except HttpOperationError:\n",
         "    blob_datastore = Datastore.register_azure_blob_container(\n",
         "        workspace=ws,\n",
         "        datastore_name=blob_datastore_name,\n",
@@ -310,9 +306,9 @@
       }
     ],
     "kernelspec": {
-   "display_name": "Python 3",
+      "display_name": "Python 3.6",
       "language": "python",
-   "name": "python3"
+      "name": "python36"
     },
     "language_info": {
       "codemirror_mode": {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb

@@ -46,10 +46,9 @@
       "outputs": [],
       "source": [
         "import azureml.core\n",
-    "from azureml.core import Workspace, Run, Experiment, Datastore\n",
+        "from azureml.core import Workspace, Experiment, Datastore\n",
         "from azureml.core.compute import AmlCompute\n",
         "from azureml.core.compute import ComputeTarget\n",
-    "from azureml.core.compute import DataFactoryCompute\n",
         "from azureml.widgets import RunDetails\n",
         "\n",
         "# Check core SDK version number\n",
@@ -71,12 +70,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "from azureml.data.data_reference import DataReference\n",
-    "from azureml.pipeline.core import Pipeline, PipelineData, StepSequence\n",
+        "from azureml.pipeline.core import Pipeline\n",
         "from azureml.pipeline.steps import PythonScriptStep\n",
-    "from azureml.pipeline.steps import DataTransferStep\n",
-    "from azureml.pipeline.core import PublishedPipeline\n",
-    "from azureml.pipeline.core.graph import PipelineParameter\n",
         "\n",
         "print(\"Pipeline SDK-specific imports completed\")"
       ]
@@ -237,12 +232,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "aml_compute_target = \"aml-compute\"\n",
         "try:\n",
         "    aml_compute = AmlCompute(ws, aml_compute_target)\n",
         "    print(\"found existing compute target.\")\n",
-    "except:\n",
+        "except ComputeTargetException:\n",
         "    print(\"creating new compute target\")\n",
         "    \n",
         "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n",
@@ -260,9 +256,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "# For a more detailed view of current Azure Machine Learning Compute status, use the 'status' property\n",
+        "# For a more detailed view of current Azure Machine Learning Compute status, use get_status()\n",
         "# example: un-comment the following line.\n",
-    "# print(aml_compute.status.serialize())"
+        "# print(aml_compute.get_status().serialize())"
       ]
     },
     {
@@ -584,9 +580,9 @@
       }
     ],
     "kernelspec": {
-   "display_name": "Python 3",
+      "display_name": "Python 3.6",
       "language": "python",
-   "name": "python3"
+      "name": "python36"
     },
     "language_info": {
       "codemirror_mode": {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-publish-and-run-using-rest-endpoint.ipynb

@@ -33,20 +33,16 @@
       "outputs": [],
       "source": [
         "import azureml.core\n",
-    "from azureml.core import Workspace, Run, Experiment, Datastore\n",
+        "from azureml.core import Workspace, Datastore\n",
         "from azureml.core.compute import AmlCompute\n",
         "from azureml.core.compute import ComputeTarget\n",
-    "from azureml.core.compute import DataFactoryCompute\n",
-    "from azureml.widgets import RunDetails\n",
         "\n",
         "# Check core SDK version number\n",
         "print(\"SDK version:\", azureml.core.VERSION)\n",
         "\n",
         "from azureml.data.data_reference import DataReference\n",
-    "from azureml.pipeline.core import Pipeline, PipelineData, StepSequence\n",
+        "from azureml.pipeline.core import Pipeline, PipelineData\n",
         "from azureml.pipeline.steps import PythonScriptStep\n",
-    "from azureml.pipeline.steps import DataTransferStep\n",
-    "from azureml.pipeline.core import PublishedPipeline\n",
         "from azureml.pipeline.core.graph import PipelineParameter\n",
         "\n",
         "print(\"Pipeline SDK-specific imports completed\")\n",
@@ -79,12 +75,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "aml_compute_target = \"aml-compute\"\n",
         "try:\n",
         "    aml_compute = AmlCompute(ws, aml_compute_target)\n",
         "    print(\"found existing compute target.\")\n",
-    "except:\n",
+        "except ComputeTargetException:\n",
         "    print(\"creating new compute target\")\n",
         "    \n",
         "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n",
@@ -100,9 +97,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "# For a more detailed view of current Azure Machine Learning Compute status, use the 'status' property\n",
+        "# For a more detailed view of current Azure Machine Learning Compute status, use get_status()\n",
         "# example: un-comment the following line.\n",
-    "# print(aml_compute.status.serialize())"
+        "# print(aml_compute.get_status().serialize())"
       ]
     },
     {
@@ -346,9 +343,9 @@
       }
     ],
     "kernelspec": {
-   "display_name": "Python 3",
+      "display_name": "Python 3.6",
       "language": "python",
-   "name": "python3"
+      "name": "python36"
     },
     "language_info": {
       "codemirror_mode": {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb

@@ -31,14 +31,12 @@
       "source": [
         "import os\n",
         "import azureml.core\n",
-    "from azureml.core.compute import ComputeTarget, DatabricksCompute\n",
         "from azureml.exceptions import ComputeTargetException\n",
-    "from azureml.core import Workspace, Run, Experiment\n",
+        "from azureml.core import Workspace, Experiment\n",
         "from azureml.pipeline.core import Pipeline, PipelineData\n",
         "from azureml.pipeline.steps import AdlaStep\n",
         "from azureml.core.datastore import Datastore\n",
         "from azureml.data.data_reference import DataReference\n",
-    "from azureml.core import attach_legacy_compute_target\n",
         "\n",
         "# Check core SDK version number\n",
         "print(\"SDK version:\", azureml.core.VERSION)"
@@ -91,8 +89,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "from msrest.exceptions import HttpOperationError\n",
         "\n",
-    "workspace = ws.name\n",
         "datastore_name='MyAdlsDatastore'\n",
         "subscription_id=os.getenv(\"ADL_SUBSCRIPTION_62\", \"<my-subscription-id>\") # subscription id of ADLS account\n",
         "resource_group=os.getenv(\"ADL_RESOURCE_GROUP_62\", \"<my-resource-group>\") # resource group of ADLS account\n",
@@ -104,7 +102,7 @@
         "try:\n",
         "    adls_datastore = Datastore.get(ws, datastore_name)\n",
         "    print(\"found datastore with name: %s\" % datastore_name)\n",
-    "except:\n",
+        "except HttpOperationError:\n",
         "    adls_datastore = Datastore.register_azure_data_lake(\n",
         "        workspace=ws,\n",
         "        datastore_name=datastore_name,\n",
@@ -171,9 +169,9 @@
         "        if 'ComputeTargetNotFound' in e.message:\n",
         "            print('adla compute not found, creating...')\n",
         "            provisioning_config = AdlaCompute.provisioning_configuration()\n",
-    "            adla_compute = ComputeTarget.create(workspace, compute_name, provisioning_config)\n",
-    "            adla_compute.wait_for_completion()\n",
-    "            return adla_compute\n",
+        "            new_adla_compute = ComputeTarget.create(workspace, compute_name, provisioning_config)\n",
+        "            new_adla_compute.wait_for_completion()\n",
+        "            return new_adla_compute\n",
         "        else:\n",
         "            raise e\n",
         "            \n",
@@ -284,7 +282,7 @@
         "    steps=[adla_step],\n",
         "    default_source_directory=script_folder)\n",
         "\n",
-    "pipeline_run = Experiment(workspace, experiment_name).submit(pipeline)\n",
+        "pipeline_run = Experiment(ws, experiment_name).submit(pipeline)\n",
         "pipeline_run.wait_for_completion()"
       ]
     },
@@ -346,9 +344,9 @@
       }
     ],
     "kernelspec": {
-   "display_name": "Python [default]",
+      "display_name": "Python 3.6",
       "language": "python",
-   "name": "python3"
+      "name": "python36"
     },
     "language_info": {
       "codemirror_mode": {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb

@@ -89,7 +89,7 @@
         "from azureml.core.runconfig import JarLibrary\n",
         "from azureml.core.compute import ComputeTarget, DatabricksCompute\n",
         "from azureml.exceptions import ComputeTargetException\n",
-    "from azureml.core import Workspace, Run, Experiment\n",
+        "from azureml.core import Workspace, Experiment\n",
         "from azureml.pipeline.core import Pipeline, PipelineData\n",
         "from azureml.pipeline.steps import DatabricksStep\n",
         "from azureml.core.datastore import Datastore\n",
@@ -146,7 +146,7 @@
         "db_access_token=os.getenv(\"DATABRICKS_ACCESS_TOKEN\", \"<my-access-token>\") # Databricks access token\n",
         " \n",
         "try:\n",
-    "    databricks_compute = ComputeTarget(workspace=ws, name=db_compute_name)\n",
+        "    databricks_compute = DatabricksCompute(workspace=ws, name=db_compute_name)\n",
         "    print('Compute target {} already exists'.format(db_compute_name))\n",
         "except ComputeTargetException:\n",
         "    print('Compute not found, will use below parameters to attach new one')\n",
@@ -297,7 +297,7 @@
         "print('Datastore {} will be used'.format(def_blob_store.name))\n",
         "\n",
         "# We are uploading a sample file in the local directory to be used as a datasource\n",
-    "def_blob_store.upload_files([\"./testdata.txt\"], target_path=\"dbtest\", overwrite=False)\n",
+        "def_blob_store.upload_files(files=[\"./testdata.txt\"], target_path=\"dbtest\", overwrite=False)\n",
         "\n",
         "step_1_input = DataReference(datastore=def_blob_store, path_on_datastore=\"dbtest\",\n",
         "                                     data_reference_name=\"input\")\n",
@@ -317,8 +317,9 @@
         "- **existing_cluster_id:** Cluster ID of an existing Interactive cluster on the Databricks workspace. If you are providing this, do not provide any of the parameters below that are used to create a new cluster such as spark_version, node_type, etc.\n",
         "- **spark_version:** Version of spark for the databricks run cluster. default value: 4.0.x-scala2.11\n",
         "- **node_type:** Azure vm node types for the databricks run cluster. default value: Standard_D3_v2\n",
-    "- **num_workers:** Number of workers for the databricks run cluster\n",
-    "- **autoscale:** The autoscale configuration for the databricks run cluster\n",
+        "- **num_workers:** Specifies a static number of workers for the databricks run cluster\n",
+        "- **min_workers:** Specifies a min number of workers to use for auto-scaling the databricks run cluster\n",
+        "- **max_workers:** Specifies a max number of workers to use for auto-scaling the databricks run cluster\n",
         "- **spark_env_variables:** Spark environment variables for the databricks run cluster (dictionary of {str:str}). default value: {'PYSPARK_PYTHON': '/databricks/python3/bin/python3'}\n",
         "- **notebook_path:** Path to the notebook in the databricks instance. If you are providing this, do not provide python script related paramaters or JAR related parameters.\n",
         "- **notebook_params:** Parameters  for the databricks notebook (dictionary of {str:str}). Fetch this inside the notebook using dbutils.widgets.get(\"myparam\")\n",
@@ -342,7 +343,7 @@
         "- **version:** Optional version tag to denote a change in functionality for the step\n",
         "\n",
         "\\* *denotes required fields*  \n",
-    "*You must provide exactly one of num_workers or autoscale paramaters*  \n",
+        "*You must provide exactly one of num_workers or min_workers and max_workers paramaters*  \n",
         "*You must provide exactly one of databricks_compute or databricks_compute_name parameters*\n",
         "\n",
         "## Use runconfig to specify library dependencies\n",
@@ -388,7 +389,7 @@
       "metadata": {},
       "source": [
         "### 1. Running the demo notebook already added to the Databricks workspace\n",
-    "Create a notebook in the Azure Databricks workspace, and provide the path to that notebook as the value associated with the environment variable \"DATABRICKS_NOTEBOOK_PATH\". This will then set the variable notebook_path when you run the code cell below:"
+        "Create a notebook in the Azure Databricks workspace, and provide the path to that notebook as the value associated with the environment variable \"DATABRICKS_NOTEBOOK_PATH\". This will then set the variable\u00c2\u00a0notebook_path\u00c2\u00a0when you run the code cell below:"
       ]
     },
     {
@@ -425,11 +426,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "#PUBLISHONLY\n",
-    "#steps = [dbNbStep]\n",
-    "#pipeline = Pipeline(workspace=ws, steps=steps)\n",
-    "#pipeline_run = Experiment(ws, 'DB_Notebook_demo').submit(pipeline)\n",
-    "#pipeline_run.wait_for_completion()"
+        "steps = [dbNbStep]\n",
+        "pipeline = Pipeline(workspace=ws, steps=steps)\n",
+        "pipeline_run = Experiment(ws, 'DB_Notebook_demo').submit(pipeline)\n",
+        "pipeline_run.wait_for_completion()"
       ]
     },
     {
@@ -445,9 +445,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "#PUBLISHONLY\n",
-    "#from azureml.widgets import RunDetails\n",
-    "#RunDetails(pipeline_run).show()"
+        "from azureml.widgets import RunDetails\n",
+        "RunDetails(pipeline_run).show()"
       ]
     },
     {
@@ -497,11 +496,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "#PUBLISHONLY\n",
-    "#steps = [dbPythonInDbfsStep]\n",
-    "#pipeline = Pipeline(workspace=ws, steps=steps)\n",
-    "#pipeline_run = Experiment(ws, 'DB_Python_demo').submit(pipeline)\n",
-    "#pipeline_run.wait_for_completion()"
+        "steps = [dbPythonInDbfsStep]\n",
+        "pipeline = Pipeline(workspace=ws, steps=steps)\n",
+        "pipeline_run = Experiment(ws, 'DB_Python_demo').submit(pipeline)\n",
+        "pipeline_run.wait_for_completion()"
       ]
     },
     {
@@ -517,9 +515,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "#PUBLISHONLY\n",
-    "#from azureml.widgets import RunDetails\n",
-    "#RunDetails(pipeline_run).show()"
+        "from azureml.widgets import RunDetails\n",
+        "RunDetails(pipeline_run).show()"
       ]
     },
     {
@@ -640,11 +637,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "#PUBLISHONLY\n",
-    "#steps = [dbJarInDbfsStep]\n",
-    "#pipeline = Pipeline(workspace=ws, steps=steps)\n",
-    "#pipeline_run = Experiment(ws, 'DB_JAR_demo').submit(pipeline)\n",
-    "#pipeline_run.wait_for_completion()"
+        "steps = [dbJarInDbfsStep]\n",
+        "pipeline = Pipeline(workspace=ws, steps=steps)\n",
+        "pipeline_run = Experiment(ws, 'DB_JAR_demo').submit(pipeline)\n",
+        "pipeline_run.wait_for_completion()"
       ]
     },
     {
@@ -660,9 +656,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "#PUBLISHONLY\n",
-    "#from azureml.widgets import RunDetails\n",
-    "#RunDetails(pipeline_run).show()"
+        "from azureml.widgets import RunDetails\n",
+        "RunDetails(pipeline_run).show()"
       ]
     },
     {
@@ -681,9 +676,9 @@
       }
     ],
     "kernelspec": {
-   "display_name": "Python 3",
+      "display_name": "Python 3.6",
       "language": "python",
-   "name": "python3"
+      "name": "python36"
     },
     "language_info": {
       "codemirror_mode": {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb

@@ -33,22 +33,17 @@
       "outputs": [],
       "source": [
         "import azureml.core\n",
-    "from azureml.core import Workspace, Run, Experiment, Datastore\n",
+        "from azureml.core import Workspace, Experiment, Datastore\n",
         "from azureml.core.compute import AmlCompute\n",
         "from azureml.core.compute import ComputeTarget\n",
-    "from azureml.core.compute import DataFactoryCompute\n",
         "from azureml.widgets import RunDetails\n",
         "\n",
         "# Check core SDK version number\n",
         "print(\"SDK version:\", azureml.core.VERSION)\n",
         "\n",
         "from azureml.data.data_reference import DataReference\n",
-    "from azureml.pipeline.core import Pipeline, PipelineData, StepSequence\n",
+        "from azureml.pipeline.core import Pipeline, PipelineData\n",
         "from azureml.pipeline.steps import PythonScriptStep\n",
-    "from azureml.pipeline.steps import DataTransferStep\n",
-    "from azureml.pipeline.core import PublishedPipeline\n",
-    "from azureml.pipeline.core.graph import PipelineParameter\n",
-    "\n",
         "print(\"Pipeline SDK-specific imports completed\")"
       ]
     },
@@ -135,12 +130,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "aml_compute_target = \"aml-compute\"\n",
         "try:\n",
         "    aml_compute = AmlCompute(ws, aml_compute_target)\n",
         "    print(\"found existing compute target.\")\n",
-    "except:\n",
+        "except ComputeTargetException:\n",
         "    print(\"creating new compute target\")\n",
         "    \n",
         "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n",
@@ -158,9 +154,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "# For a more detailed view of current Azure Machine Learning Compute status, use the 'status' property\n",
+        "# For a more detailed view of current Azure Machine Learning Compute status, use get_status()\n",
         "# example: un-comment the following line.\n",
-    "# print(aml_compute.status.serialize())"
+        "# print(aml_compute.get_status().serialize())"
       ]
     },
     {
@@ -396,9 +392,9 @@
       }
     ],
     "kernelspec": {
-   "display_name": "Python 3",
+      "display_name": "Python 3.6",
       "language": "python",
-   "name": "python3"
+      "name": "python36"
     },
     "language_info": {
       "codemirror_mode": {

how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.ipynb

@@ -37,10 +37,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "from azureml.core import Datastore\n",
         "from azureml.core import Experiment\n",
         "from azureml.core.compute import AmlCompute, ComputeTarget\n",
-    "from azureml.core.conda_dependencies import CondaDependencies\n",
         "from azureml.core.datastore import Datastore\n",
         "from azureml.core.runconfig import CondaDependencies, RunConfiguration\n",
         "from azureml.data.data_reference import DataReference\n",
@@ -55,7 +53,7 @@
       "outputs": [],
       "source": [
         "import os\n",
-    "from azureml.core import Workspace, Run, Experiment\n",
+        "from azureml.core import Workspace\n",
         "\n",
         "ws = Workspace.from_config()\n",
         "print('Workspace name: ' + ws.name, \n",
@@ -76,7 +74,7 @@
       "metadata": {},
       "source": [
         "### Set up datastores\n",
-    "First, let’s access the datastore that has the model, labels, and images. \n",
+        "First, let\u00e2\u20ac\u2122s access the datastore that has the model, labels, and images. \n",
         "\n",
         "### Create a datastore that points to a blob container containing sample images\n",
         "\n",
@@ -106,7 +104,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "Next, let’s specify the default datastore for the outputs."
+        "Next, let\u00e2\u20ac\u2122s specify the default datastore for the outputs."
       ]
     },
     {
@@ -166,8 +164,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "import os\n",
-    "\n",
         "# choose a name for your cluster\n",
         "aml_compute_name = os.environ.get(\"AML_COMPUTE_NAME\", \"gpu-cluster\")\n",
         "cluster_min_nodes = os.environ.get(\"AML_COMPUTE_MIN_NODES\", 0)\n",
@@ -193,8 +189,8 @@
         "    # if no min node count is provided it will use the scale settings for the cluster\n",
         "    compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
         "    \n",
-    "     # For a more detailed view of current Azure Machine Learning Compute  status, use the 'status' property    \n",
-    "    print(compute_target.status.serialize())"
+        "     # For a more detailed view of current Azure Machine Learning Compute  status, use get_status()\n",
+        "    print(compute_target.get_status().serialize())"
       ]
     },
     {
@@ -295,7 +291,7 @@
       "metadata": {},
       "source": [
         "## Build and run the batch scoring pipeline\n",
-    "You have everything you need to build the pipeline. Let’s put all these together."
+        "You have everything you need to build the pipeline. Let\u00e2\u20ac\u2122s put all these together."
       ]
     },
     {
@@ -513,8 +509,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "from azureml.pipeline.core import PublishedPipeline\n",
-    "\n",
         "rest_endpoint = published_pipeline.endpoint\n",
         "# specify batch size when running the pipeline\n",
         "response = requests.post(rest_endpoint, \n",
@@ -551,9 +545,9 @@
       }
     ],
     "kernelspec": {
-   "display_name": "Python 3",
+      "display_name": "Python 3.6",
       "language": "python",
-   "name": "python3"
+      "name": "python36"
     },
     "language_info": {
       "codemirror_mode": {

how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer.ipynb

@@ -44,7 +44,7 @@
       "outputs": [],
       "source": [
         "import os\n",
-    "from azureml.core import Workspace, Run, Experiment\n",
+        "from azureml.core import Workspace, Experiment\n",
         "\n",
         "ws = Workspace.from_config()\n",
         "print('Workspace name: ' + ws.name, \n",
@@ -69,7 +69,8 @@
         "from azureml.data.data_reference import DataReference\n",
         "from azureml.pipeline.core import Pipeline, PipelineData\n",
         "from azureml.pipeline.steps import PythonScriptStep, MpiStep\n",
-    "from azureml.core.runconfig import CondaDependencies, RunConfiguration"
+        "from azureml.core.runconfig import CondaDependencies, RunConfiguration\n",
+        "from azureml.core.compute_target import ComputeTargetException"
       ]
     },
     {
@@ -90,7 +91,7 @@
         "try:\n",
         "    cpu_cluster = AmlCompute(ws, cpu_cluster_name)\n",
         "    print(\"found existing cluster.\")\n",
-    "except:\n",
+        "except ComputeTargetException:\n",
         "    print(\"creating new cluster\")\n",
         "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_v2\",\n",
         "                                                                    max_nodes = 1)\n",
@@ -104,7 +105,7 @@
         "try:\n",
         "    gpu_cluster = AmlCompute(ws, gpu_cluster_name)\n",
         "    print(\"found existing cluster.\")\n",
-    "except:\n",
+        "except ComputeTargetException:\n",
         "    print(\"creating new cluster\")\n",
         "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_NC6\",\n",
         "                                                                    max_nodes = 3)\n",
@@ -526,7 +527,6 @@
         "                               \"ParameterAssignments\": {\"style\": \"rain_princess\", \"nodecount\": 3}})    \n",
         "run_id = response.json()[\"Id\"]\n",
         "\n",
-    "from azureml.pipeline.core.run import PipelineRun\n",
         "published_pipeline_run_rain = PipelineRun(ws.experiments[\"style_transfer\"], run_id)\n",
         "\n",
         "RunDetails(published_pipeline_run_rain).show()"
@@ -545,7 +545,6 @@
         "                               \"ParameterAssignments\": {\"style\": \"udnie\", \"nodecount\": 4}})   \n",
         "run_id = response.json()[\"Id\"]\n",
         "\n",
-    "from azureml.pipeline.core.run import PipelineRun\n",
         "published_pipeline_run_udnie = PipelineRun(ws.experiments[\"style_transfer\"], run_id)\n",
         "\n",
         "RunDetails(published_pipeline_run_udnie).show()"
@@ -588,9 +587,9 @@
       }
     ],
     "kernelspec": {
-   "display_name": "Python 3",
+      "display_name": "Python 3.6",
       "language": "python",
-   "name": "python3"
+      "name": "python36"
     },
     "language_info": {
       "codemirror_mode": {

how-to-use-azureml/training-with-deep-learning/README.md

@@ -1,8 +1,16 @@
 ## Azure Machine Learning service training examples
 
 These examples show you:
- * Distributed training of models on Machine Learning Compute cluster
- * Hyperparameter tuning at scale
- * Using Tensorboard with Azure ML Python SDK.
+
+1. [How to use the Estimator pattern in Azure ML](how-to-use-estimator)
+2. [Train using TensorFlow Estimator and tune hyperparameters using Hyperdrive](train-hyperparameter-tune-deploy-with-tensorflow)
+3. [Train using Keras and tune hyperparameters using Hyperdrive](train-hyperparameter-tune-deploy-with-keras)
+4. [Train using Pytorch Estimator and tune hyperparameters using Hyperdrive](train-hyperparameter-tune-deploy-with-pytorch)
+5. [Distributed training using TensorFlow and Parameter Server](distributed-tensorflow-with-parameter-server)
+6. [Distributed training using TensorFlow and Horovod](distributed-tensorflow-with-horovod)
+7. [Distributed training using Pytorch and Horovod](distributed-pytorch-with-horovod)
+8. [Distributed training using CNTK and custom Docker image](distributed-cntk-with-custom-docker)
+9. [Export run history records to Tensorboard](export-run-history-to-tensorboard)
+10. [Use TensorBoard to monitor training execution](tensorboard)
 
 Learn more about how to use `Estimator` class to  [train deep neural networks with Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-ml-models).

how-to-use-azureml/training-with-deep-learning/distributed-cntk-with-custom-docker/distributed-cntk-with-custom-docker.ipynb

@@ -23,7 +23,7 @@
       "source": [
         "## Prerequisites\n",
         "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
-    "* Go through the [00.configuration.ipynb]() notebook to:\n",
+        "* Go through the [configuration notebook](../../../configuration.ipynb) to:\n",
         "    * install the AML SDK\n",
         "    * create a workspace and its configuration file (`config.json`)"
       ]
@@ -69,7 +69,7 @@
       "source": [
         "## Initialize workspace\n",
         "\n",
-    "Initialize a [Workspace](https://review.docs.microsoft.com/en-us/azure/machine-learning/service/concept-azure-machine-learning-architecture?branch=release-ignite-aml#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
+        "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
       ]
     },
     {
@@ -81,10 +81,10 @@
         "from azureml.core.workspace import Workspace\n",
         "\n",
         "ws = Workspace.from_config()\n",
-    "print('Workspace name: ' + ws.name, \n",
+        "print('Workspace name: ' + ws.name,\n",
         "      'Azure region: ' + ws.location, \n",
         "      'Subscription id: ' + ws.subscription_id, \n",
-    "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+        "      'Resource group: ' + ws.resource_group, sep='\\n')"
       ]
     },
     {
@@ -124,8 +124,8 @@
         "\n",
         "    compute_target.wait_for_completion(show_output=True)\n",
         "\n",
-    "# Use the 'status' property to get a detailed status for the current AmlCompute. \n",
-    "print(compute_target.status.serialize())"
+        "# use get_status() to get a detailed status for the current AmlCompute\n",
+        "print(compute_target.get_status().serialize())"
       ]
     },
     {
@@ -282,7 +282,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "from azureml.train.estimator import *\n",
+        "from azureml.train.estimator import Estimator\n",
         "\n",
         "script_params = {\n",
         "    '--num_epochs': 20,\n",
@@ -296,7 +296,7 @@
         "                      script_params=script_params,\n",
         "                      node_count=2,\n",
         "                      process_count_per_node=1,\n",
-    "                      distributed_backend='mpi',     \n",
+        "                      distributed_backend='mpi',\n",
         "                      pip_packages=['cntk-gpu==2.6'],\n",
         "                      custom_docker_base_image='microsoft/mmlspark:gpu-0.12',\n",
         "                      use_gpu=True)"

how-to-use-azureml/training-with-deep-learning/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb

@@ -22,8 +22,8 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-    "* Go through the [Configuration](https://github.com/Azure/MachineLearningNotebooks/blob/master/configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`\n",
-    "* Review the [tutorial](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) on single-node PyTorch training using Azure Machine Learning"
+        "* Go through the [Configuration](../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`\n",
+        "* Review the [tutorial](../train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) on single-node PyTorch training using Azure Machine Learning"
       ]
     },
     {
@@ -82,7 +82,7 @@
         "print('Workspace name: ' + ws.name, \n",
         "      'Azure region: ' + ws.location, \n",
         "      'Subscription id: ' + ws.subscription_id, \n",
-    "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+        "      'Resource group: ' + ws.resource_group, sep='\\n')"
       ]
     },
     {
@@ -122,8 +122,8 @@
         "\n",
         "    compute_target.wait_for_completion(show_output=True)\n",
         "\n",
-    "# Use the 'status' property to get a detailed status for the current AmlCompute. \n",
-    "print(compute_target.status.serialize())"
+        "# use get_status() to get a detailed status for the current AmlCompute. \n",
+        "print(compute_target.get_status().serialize())"
       ]
     },
     {

how-to-use-azureml/training-with-deep-learning/distributed-pytorch-with-horovod/pytorch_horovod_mnist.py

@@ -50,7 +50,7 @@ if args.cuda:
     torch.cuda.manual_seed(args.seed)
 
 
-kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
+kwargs = {}
 train_dataset = \
     datasets.MNIST('data-%d' % hvd.rank(), train=True, download=True,
                    transform=transforms.Compose([

how-to-use-azureml/training-with-deep-learning/distributed-tensorflow-with-horovod/distributed-tensorflow-with-horovod.ipynb

@@ -23,10 +23,10 @@
       "source": [
         "## Prerequisites\n",
         "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning (AML)\n",
-    "* Go through the [00.configuration.ipynb](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) notebook to:\n",
+        "* Go through the [configuration notebook](../../../configuration.ipynb) to:\n",
         "    * install the AML SDK\n",
         "    * create a workspace and its configuration file (`config.json`)\n",
-    "* Review the [tutorial](https://aka.ms/aml-notebook-hyperdrive) on single-node TensorFlow training using the SDK"
+        "* Review the [tutorial](../train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb) on single-node TensorFlow training using the SDK"
       ]
     },
     {
@@ -84,7 +84,7 @@
         "print('Workspace name: ' + ws.name, \n",
         "      'Azure region: ' + ws.location, \n",
         "      'Subscription id: ' + ws.subscription_id, \n",
-    "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+        "      'Resource group: ' + ws.resource_group, sep='\\n')"
       ]
     },
     {
@@ -96,7 +96,7 @@
         "\n",
         "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
         "\n",
-    "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
+        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
       ]
     },
     {
@@ -124,8 +124,8 @@
         "\n",
         "    compute_target.wait_for_completion(show_output=True)\n",
         "\n",
-    "# Use the 'status' property to get a detailed status for the current cluster. \n",
-    "print(compute_target.status.serialize())"
+        "# use get_status() to get a detailed status for the current cluster. \n",
+        "print(compute_target.get_status().serialize())"
       ]
     },
     {
@@ -238,8 +238,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "import os\n",
-    "\n",
         "project_folder = './tf-distr-hvd'\n",
         "os.makedirs(project_folder, exist_ok=True)"
       ]

how-to-use-azureml/training-with-deep-learning/distributed-tensorflow-with-parameter-server/distributed-tensorflow-with-parameter-server.ipynb

@@ -23,7 +23,7 @@
       "source": [
         "## Prerequisites\n",
         "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning (AML)\n",
-    "* Go through the [00.configuration.ipynb](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) notebook to:\n",
+        "* Go through the [configuration notebook](../../../configuration.ipynb) to:\n",
         "    * install the AML SDK\n",
         "    * create a workspace and its configuration file (`config.json`)\n",
         "* Review the [tutorial](https://aka.ms/aml-notebook-hyperdrive) on single-node TensorFlow training using the SDK"
@@ -124,8 +124,8 @@
         "\n",
         "    compute_target.wait_for_completion(show_output=True)\n",
         "\n",
-    "# Use the 'status' property to get a detailed status for the current cluster. \n",
-    "print(compute_target.status.serialize())"
+        "# use get_status() to get a detailed status for the current cluster. \n",
+        "print(compute_target.get_status().serialize())"
       ]
     },
     {

how-to-use-azureml/training-with-deep-learning/export-run-history-to-tensorboard/export-run-history-to-tensorboard.ipynb

@@ -26,7 +26,7 @@
       "source": [
         "## Prerequisites\n",
         "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
-    "* Go through the [00.configuration.ipynb](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) notebook to:\n",
+        "* Go through the [configuration notebook](../../../configuration.ipynb) notebook to:\n",
         "    * install the AML SDK\n",
         "    * create a workspace and its configuration file (`config.json`)"
       ]
@@ -74,14 +74,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "from azureml.core import Workspace, Run, Experiment\n",
-    "\n",
+        "from azureml.core import Workspace, Experiment\n",
         "\n",
         "ws = Workspace.from_config()\n",
         "print('Workspace name: ' + ws.name, \n",
         "      'Azure region: ' + ws.location, \n",
         "      'Subscription id: ' + ws.subscription_id, \n",
-    "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+        "      'Resource group: ' + ws.resource_group, sep='\\n')"
       ]
     },
     {
@@ -143,7 +142,7 @@
         "        # More data science stuff\n",
         "        reg = Ridge(alpha=alpha)\n",
         "        reg.fit(data[\"train\"][\"x\"], data[\"train\"][\"y\"])\n",
-    "        # TODO save model\n",
+        "        \n",
         "        preds = reg.predict(data[\"test\"][\"x\"])\n",
         "        mse = mean_squared_error(preds, data[\"test\"][\"y\"])\n",
         "        # End train and eval\n",
@@ -169,7 +168,6 @@
         "# Export Run History to Tensorboard logs\n",
         "from azureml.contrib.tensorboard.export import export_to_tensorboard\n",
         "import os\n",
-    "import tensorflow as tf\n",
         "\n",
         "logdir = 'exportedTBlogs'\n",
         "log_path = os.path.join(os.getcwd(), logdir)\n",

how-to-use-azureml/training-with-deep-learning/how-to-use-estimator/dummy_train.py

@@ -0,0 +1,16 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+print("*********************************************************")
+print("Hello Azure ML!")
+
+try:
+    from azureml.core import Run
+    run = Run.get_context()
+    print("Log Fibonacci numbers.")
+    run.log_list('Fibonacci numbers', [0, 1, 1, 2, 3, 5, 8, 13, 21, 34])
+    run.complete()
+except:
+    print("Warning: you need to install Azure ML SDK in order to log metrics.")
+
+print("*********************************************************")

how-to-use-azureml/training-with-deep-learning/how-to-use-estimator/how-to-use-estimator.ipynb

@@ -0,0 +1,365 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "nbpresent": {
+          "id": "bf74d2e9-2708-49b1-934b-e0ede342f475"
+        }
+      },
+      "source": [
+        "# How to use Estimator in Azure ML\n",
+        "\n",
+        "## Introduction\n",
+        "This tutorial shows how to use the Estimator pattern in Azure Machine Learning SDK. Estimator is a convenient object in Azure Machine Learning that wraps run configuration information to help simplify the tasks of specifying how a script is executed.\n",
+        "\n",
+        "\n",
+        "## Prerequisite:\n",
+        "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
+        "* Go through the [configuration notebook](../../../configuration.ipynb) to:\n",
+        "    * install the AML SDK\n",
+        "    * create a workspace and its configuration file (`config.json`)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Let's get started. First let's import some Python libraries."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "nbpresent": {
+          "id": "edaa7f2f-2439-4148-b57a-8c794c0945ec"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "import azureml.core\n",
+        "from azureml.core import Workspace\n",
+        "\n",
+        "# check core SDK version number\n",
+        "print(\"Azure ML SDK Version: \", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize workspace\n",
+        "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "print('Workspace name: ' + ws.name, \n",
+        "      'Azure region: ' + ws.location, \n",
+        "      'Subscription id: ' + ws.subscription_id, \n",
+        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "nbpresent": {
+          "id": "59f52294-4a25-4c92-bab8-3b07f0f44d15"
+        }
+      },
+      "source": [
+        "## Create an Azure ML experiment\n",
+        "Let's create an experiment named \"estimator-test\". The script runs will be recorded under this experiment in Azure."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "nbpresent": {
+          "id": "bc70f780-c240-4779-96f3-bc5ef9a37d59"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "\n",
+        "exp = Experiment(workspace=ws, name='estimator-test')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create or Attach existing AmlCompute\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If we could not find the cluster with the given name, then we will create a new cluster here. We will create an `AmlCompute` cluster of `STANDARD_NC6` GPU VMs. This process is broken down into 3 steps:\n",
+        "1. create the configuration (this step is local and only takes a second)\n",
+        "2. create the cluster (this step will take about **20 seconds**)\n",
+        "3. provision the VMs to bring the cluster to the initial size (of 1 in this case). This step will take about **3-5 minutes** and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# choose a name for your cluster\n",
+        "cluster_name = \"cpucluster\"\n",
+        "\n",
+        "try:\n",
+        "    cpu_cluster = ComputeTarget(workspace=ws, name=cluster_name)\n",
+        "    print('Found existing compute target')\n",
+        "except ComputeTargetException:\n",
+        "    print('Creating a new compute target...')\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', max_nodes=4)\n",
+        "\n",
+        "    # create the cluster\n",
+        "    cpu_cluster = ComputeTarget.create(ws, cluster_name, compute_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 uses the scale settings for the cluster\n",
+        "    cpu_cluster.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
+        "\n",
+        "# use get_status() to get a detailed status for the current cluster. \n",
+        "print(cpu_cluster.get_status().serialize())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now that you have created the compute target, let's see what the workspace's `compute_targets` property returns. You should now see one entry named 'cpucluster' of type `AmlCompute`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "compute_targets = ws.compute_targets\n",
+        "for name, ct in compute_targets.items():\n",
+        "    print(name, ct.type, ct.provisioning_state)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "nbpresent": {
+          "id": "2039d2d5-aca6-4f25-a12f-df9ae6529cae"
+        }
+      },
+      "source": [
+        "## Use a simple script\n",
+        "We have already created a simple \"hello world\" script. This is the script that we will submit through the estimator pattern. It prints a hello-world message, and if Azure ML SDK is installed, it will also logs an array of values ([Fibonacci numbers](https://en.wikipedia.org/wiki/Fibonacci_number))."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "with open('./dummy_train.py', 'r') as f:\n",
+        "    print(f.read())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create A Generic Estimator"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "First we import the Estimator class and also a widget to visualize a run."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.estimator import Estimator\n",
+        "from azureml.widgets import RunDetails"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The simplest estimator is to submit the current folder to the local computer. Estimator by default will attempt to use Docker-based execution. Let's turn that off for now. It then builds a conda environment locally, installs Azure ML SDK in it, and runs your script."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# use a conda environment, don't use Docker, on local computer\n",
+        "est = Estimator(source_directory='.', compute_target='local', entry_script='dummy_train.py', use_docker=False)\n",
+        "run = exp.submit(est)\n",
+        "RunDetails(run).show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can also enable Docker and let estimator pick the default CPU image supplied by Azure ML for execution. You can target an AmlCompute cluster (or any other supported compute target types)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# use a conda environment on default Docker image in an AmlCompute cluster\n",
+        "est = Estimator(source_directory='.', compute_target=cpu_cluster, entry_script='dummy_train.py', use_docker=True)\n",
+        "run = exp.submit(est)\n",
+        "RunDetails(run).show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can customize the conda environment by adding conda and/or pip packages."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# add a conda package\n",
+        "est = Estimator(source_directory='.', \n",
+        "                compute_target='local', \n",
+        "                entry_script='dummy_train.py', \n",
+        "                use_docker=False, \n",
+        "                conda_packages=['scikit-learn'])\n",
+        "run = exp.submit(est)\n",
+        "RunDetails(run).show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can also specify a custom Docker image for exeution. In this case, you probably want to tell the system not to build a new conda environment for you. Instead, you can specify the path to an existing Python environment in the custom Docker image.\n",
+        "\n",
+        "**Note**: since the below example points to the preinstalled Python environment in the miniconda3 image maintained by continuum.io on Docker Hub where Azure ML SDK is not present, the logging metric code is not triggered. But a run history record is still recorded. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# use a custom Docker image\n",
+        "from azureml.core import RunConfiguration\n",
+        "\n",
+        "rc = RunConfiguration()\n",
+        "rc.environment.docker.enabled = True\n",
+        "\n",
+        "# this is an image available in Docker Hub\n",
+        "rc.environment.docker.base_image = 'continuumio/miniconda3'\n",
+        "\n",
+        "# you can also point to an image in a private ACR\n",
+        "#rc.environment.docker.base_image = \"mycustomimage:1.0\"\n",
+        "#rc.environment.docker.base_image_registry.address = \"myregistry.azurecr.io\"\n",
+        "#rc.environment.docker.base_image_registry.username = \"username\"\n",
+        "#rc.environment.docker.base_image_registry.password = \"password\"\n",
+        "\n",
+        "# don't let the system build a new conda environment\n",
+        "rc.environment.python.user_managed_dependencies = True\n",
+        "# point to an existing python environment instead\n",
+        "rc.environment.python.interpreter_path = '/opt/conda/bin/python'\n",
+        "\n",
+        "# submit to a local Docker container. if you don't have Docker engine running locally, you can set compute_target to cpu_cluster.\n",
+        "est = Estimator(source_directory='.', compute_target='local', \n",
+        "                entry_script='dummy_train.py',\n",
+        "                environment_definition=rc.environment)\n",
+        "\n",
+        "run = exp.submit(est)\n",
+        "RunDetails(run).show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Next Steps\n",
+        "Now you can proceed to explore the other types of estimators, such as TensorFlow estimator, PyTorch estimator, etc. in the sample folder."
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "minxia"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    },
+    "msauthor": "haining"
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

how-to-use-azureml/training-with-deep-learning/tensorboard/tensorboard.ipynb

@@ -27,7 +27,7 @@
       "source": [
         "## Prerequisites\n",
         "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
-    "* Go through the [00.configuration.ipynb](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) notebook to:\n",
+        "* Go through the [configuration notebook](../../../configuration.ipynb) notebook to:\n",
         "    * install the AML SDK\n",
         "    * create a workspace and its configuration file (`config.json`)"
       ]
@@ -104,7 +104,7 @@
         "print('Workspace name: ' + ws.name, \n",
         "      'Azure region: ' + ws.location, \n",
         "      'Subscription id: ' + ws.subscription_id, \n",
-    "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+        "      'Resource group: ' + ws.resource_group, sep='\\n')"
       ]
     },
     {
@@ -153,7 +153,7 @@
       "source": [
         "import requests\n",
         "import os\n",
-    "import tempfile\n",
+        "\n",
         "tf_code = requests.get(\"https://raw.githubusercontent.com/tensorflow/tensorflow/r1.8/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py\")\n",
         "with open(os.path.join(exp_dir, \"mnist_with_summaries.py\"), \"w\") as file:\n",
         "    file.write(tf_code.text)"
@@ -192,9 +192,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "from azureml.core import Experiment, Run\n",
+        "from azureml.core import Experiment\n",
         "from azureml.core.script_run_config import ScriptRunConfig\n",
-    "import tensorflow as tf\n",
         "\n",
         "logs_dir = os.path.join(os.curdir, \"logs\")\n",
         "data_dir = os.path.abspath(os.path.join(os.curdir, \"mnist_data\"))\n",
@@ -276,7 +275,7 @@
         "Tensorboard uploading works with all compute targets. Here we demonstrate it from a DSVM.\n",
         "Note that the Tensorboard instance itself will be run by the notebook kernel. Again, this means this notebook's kernel must have access to the Tensorboard module.\n",
         "\n",
-    "If you are unfamiliar with DSVM configuration, check [04. Train in a remote VM](04.train-on-remote-vm.ipynb) for a more detailed breakdown.\n",
+        "If you are unfamiliar with DSVM configuration, check [Train in a remote VM](../../training/train-on-remote-vm/train-on-remote-vm.ipynb) for a more detailed breakdown.\n",
         "\n",
         "**Note**: To streamline the compute that Azure Machine Learning creates, we are making updates to support creating only single to multi-node `AmlCompute`. The `DSVMCompute` class will be deprecated in a later release, but the DSVM can be created using the below single line command and then attached(like any VM) using the sample code below. Also note, that we only support Linux VMs for remote execution from AML and the commands below will spin a Linux VM only.\n",
         "\n",
@@ -296,7 +295,6 @@
       "source": [
         "from azureml.core.compute import RemoteCompute\n",
         "from azureml.core.compute_target import ComputeTargetException\n",
-    "import os\n",
         "\n",
         "username = os.getenv('AZUREML_DSVM_USERNAME', default='<my_username>')\n",
         "address = os.getenv('AZUREML_DSVM_ADDRESS', default='<ip_address_or_fqdn>')\n",
@@ -405,7 +403,6 @@
       "outputs": [],
       "source": [
         "from azureml.core.compute import ComputeTarget, AmlCompute\n",
-    "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "# choose a name for your cluster\n",
         "cluster_name = \"cpucluster\"\n",
@@ -423,8 +420,8 @@
         "\n",
         "compute_target.wait_for_completion(show_output=True, min_node_count=1, timeout_in_minutes=20)\n",
         "\n",
-    "# Use the 'status' property to get a detailed status for the current cluster. \n",
-    "print(compute_target.status.serialize())"
+        "# use get_status() to get a detailed status for the current cluster. \n",
+        "print(compute_target.get_status().serialize())"
       ]
     },
     {

how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/keras_mnist.py

@@ -0,0 +1,121 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import numpy as np
+import argparse
+import os
+
+import matplotlib.pyplot as plt
+
+import keras
+from keras.models import Sequential, model_from_json
+from keras.layers import Dense
+from keras.optimizers import RMSprop
+from keras.callbacks import Callback
+
+import tensorflow as tf
+
+from azureml.core import Run
+from utils import load_data, one_hot_encode
+
+print("Keras version:", keras.__version__)
+print("Tensorflow version:", tf.__version__)
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--data-folder', type=str, dest='data_folder', help='data folder mounting point')
+parser.add_argument('--batch-size', type=int, dest='batch_size', default=50, help='mini batch size for training')
+parser.add_argument('--first-layer-neurons', type=int, dest='n_hidden_1', default=100,
+                    help='# of neurons in the first layer')
+parser.add_argument('--second-layer-neurons', type=int, dest='n_hidden_2', default=100,
+                    help='# of neurons in the second layer')
+args = parser.parse_args()
+
+data_folder = args.data_folder
+
+print('training dataset is stored here:', data_folder)
+
+X_train = load_data(os.path.join(data_folder, 'train-images.gz'), False) / 255.0
+X_test = load_data(os.path.join(data_folder, 'test-images.gz'), False) / 255.0
+
+y_train = load_data(os.path.join(data_folder, 'train-labels.gz'), True).reshape(-1)
+y_test = load_data(os.path.join(data_folder, 'test-labels.gz'), True).reshape(-1)
+
+training_set_size = X_train.shape[0]
+
+n_inputs = 28 * 28
+n_h1 = args.n_hidden_1
+n_h2 = args.n_hidden_2
+n_outputs = 10
+
+n_epochs = 20
+batch_size = args.batch_size
+
+y_train = one_hot_encode(y_train, n_outputs)
+y_test = one_hot_encode(y_test, n_outputs)
+print(X_train.shape, y_train.shape, X_test.shape, y_test.shape, sep='\n')
+
+# Build a simple MLP model
+model = Sequential()
+# input layer
+model.add(Dense(n_h1, activation='relu', input_shape=(n_inputs,)))
+# hidden layer
+model.add(Dense(n_h2, activation='relu'))
+# output layer
+model.add(Dense(n_outputs, activation='softmax'))
+
+model.summary()
+
+model.compile(loss='categorical_crossentropy',
+              optimizer=RMSprop(),
+              metrics=['accuracy'])
+
+# start an Azure ML run
+run = Run.get_context()
+
+
+class LogRunMetrics(Callback):
+    # callback at the end of every epoch
+    def on_epoch_end(self, epoch, log):
+        # log a value repeated which creates a list
+        run.log('Loss', log['loss'])
+        run.log('Accuracy', log['acc'])
+
+
+history = model.fit(X_train, y_train,
+                    batch_size=batch_size,
+                    epochs=n_epochs,
+                    verbose=2,
+                    validation_data=(X_test, y_test),
+                    callbacks=[LogRunMetrics()])
+
+score = model.evaluate(X_test, y_test, verbose=0)
+
+# log a single value
+run.log("Final test loss", score[0])
+print('Test loss:', score[0])
+
+run.log('Final test accuracy', score[1])
+print('Test accuracy:', score[1])
+
+plt.figure(figsize=(6, 3))
+plt.title('MNIST with Keras MLP ({} epochs)'.format(n_epochs), fontsize=14)
+plt.plot(history.history['acc'], 'b-', label='Accuracy', lw=4, alpha=0.5)
+plt.plot(history.history['loss'], 'r--', label='Loss', lw=4, alpha=0.5)
+plt.legend(fontsize=12)
+plt.grid(True)
+
+# log an image
+run.log_image('Accuracy vs Loss', plot=plt)
+
+# create a ./outputs/model folder in the compute target
+# files saved in the "./outputs" folder are automatically uploaded into run history
+os.makedirs('./outputs/model', exist_ok=True)
+
+# serialize NN architecture to JSON
+model_json = model.to_json()
+# save model JSON
+with open('./outputs/model/model.json', 'w') as f:
+    f.write(model_json)
+# save model weights
+model.save_weights('./outputs/model/model.h5')
+print("model saved in ./outputs/model folder")

how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-keras/utils.py

@@ -0,0 +1,27 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import gzip
+import numpy as np
+import struct
+
+
+# load compressed MNIST gz files and return numpy arrays
+def load_data(filename, label=False):
+    with gzip.open(filename) as gz:
+        struct.unpack('I', gz.read(4))
+        n_items = struct.unpack('>I', gz.read(4))
+        if not label:
+            n_rows = struct.unpack('>I', gz.read(4))[0]
+            n_cols = struct.unpack('>I', gz.read(4))[0]
+            res = np.frombuffer(gz.read(n_items[0] * n_rows * n_cols), dtype=np.uint8)
+            res = res.reshape(n_items[0], n_rows * n_cols)
+        else:
+            res = np.frombuffer(gz.read(n_items[0]), dtype=np.uint8)
+            res = res.reshape(n_items[0], 1)
+    return res
+
+
+# one-hot encode a 1-D array
+def one_hot_encode(array, num_of_classes):
+    return np.eye(num_of_classes)[array.reshape(-1)]

how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb

@@ -25,7 +25,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-    "* Go through the [Configuration](https://github.com/Azure/MachineLearningNotebooks/blob/master/configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`"
+        "* Go through the [Configuration](../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`"
       ]
     },
     {
@@ -83,7 +83,7 @@
         "print('Workspace name: ' + ws.name, \n",
         "      'Azure region: ' + ws.location, \n",
         "      'Subscription id: ' + ws.subscription_id, \n",
-    "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+        "      'Resource group: ' + ws.resource_group, sep='\\n')"
       ]
     },
     {
@@ -123,8 +123,8 @@
         "\n",
         "    compute_target.wait_for_completion(show_output=True)\n",
         "\n",
-    "# Use the 'status' property to get a detailed status for the current cluster. \n",
-    "print(compute_target.status.serialize())"
+        "# use get_status() to get a detailed status for the current cluster. \n",
+        "print(compute_target.get_status().serialize())"
       ]
     },
     {
@@ -359,7 +359,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "from azureml.train.hyperdrive import *\n",
+        "from azureml.train.hyperdrive import RandomParameterSampling, BanditPolicy, HyperDriveRunConfig, uniform, PrimaryMetricGoal\n",
         "\n",
         "param_sampling = RandomParameterSampling( {\n",
         "        'learning_rate': uniform(0.0005, 0.005),\n",
@@ -409,8 +409,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "from azureml.widgets import RunDetails\n",
-    "\n",
         "RunDetails(hyperdrive_run).show()"
       ]
     },
@@ -649,7 +647,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "import os, json\n",
+        "import json\n",
         "from PIL import Image\n",
         "import matplotlib.pyplot as plt\n",
         "\n",

how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb

@@ -26,7 +26,7 @@
         "\n",
         "## Prerequisite:\n",
         "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
-    "* Go through the [00.configuration.ipynb](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) notebook to:\n",
+        "* Go through the [configuration notebook](../../../configuration.ipynb) to:\n",
         "    * install the AML SDK\n",
         "    * create a workspace and its configuration file (`config.json`)"
       ]
@@ -51,7 +51,6 @@
         "%matplotlib inline\n",
         "import numpy as np\n",
         "import os\n",
-    "import matplotlib\n",
         "import matplotlib.pyplot as plt"
       ]
     },
@@ -66,7 +65,7 @@
       "outputs": [],
       "source": [
         "import azureml\n",
-    "from azureml.core import Workspace, Run\n",
+        "from azureml.core import Workspace\n",
         "\n",
         "# check core SDK version number\n",
         "print(\"Azure ML SDK Version: \", azureml.core.VERSION)"
@@ -109,8 +108,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "from azureml.core.workspace import Workspace\n",
-    "\n",
         "ws = Workspace.from_config()\n",
         "print('Workspace name: ' + ws.name, \n",
         "      'Azure region: ' + ws.location, \n",
@@ -166,7 +163,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "import os\n",
         "import urllib\n",
         "\n",
         "os.makedirs('./data/mnist', exist_ok=True)\n",
@@ -299,8 +295,8 @@
         "    # if no min node count is provided it uses 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",
-    "# Use the 'status' property to get a detailed status for the current cluster. \n",
-    "print(compute_target.status.serialize())"
+        "# use get_status() to get a detailed status for the current cluster. \n",
+        "print(compute_target.get_status().serialize())"
       ]
     },
     {
@@ -431,7 +427,7 @@
       "metadata": {},
       "source": [
         "## Submit job to run\n",
-    "Calling the `fit` function on the estimator submits the job to Azure ML for execution. Submitting the job should only take a few seconds."
+        "Submit the estimator to an Azure ML experiment to kick off the execution."
       ]
     },
     {
@@ -552,7 +548,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "import os\n",
         "\n",
         "os.makedirs('./imgs', exist_ok=True)\n",
         "metrics = run.get_metrics()\n",
@@ -685,7 +680,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "from azureml.train.hyperdrive import *\n",
+        "from azureml.train.hyperdrive import RandomParameterSampling, BanditPolicy, HyperDriveRunConfig, PrimaryMetricGoal\n",
+        "from azureml.train.hyperdrive import choice, loguniform\n",
         "\n",
         "ps = RandomParameterSampling(\n",
         "    {\n",
@@ -1079,7 +1075,6 @@
       "outputs": [],
       "source": [
         "import requests\n",
-    "import json\n",
         "\n",
         "# send a random row from the test set to score\n",
         "random_index = np.random.randint(0, len(X_test)-1)\n",
@@ -1163,7 +1158,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+      "version": "3.6.8"
     },
     "msauthor": "minxia"
   },

how-to-use-azureml/training/README.md

@@ -6,4 +6,5 @@ Follow these sample notebooks to learn:
 2. [Train on local](train-on-local): train a model using local computer as compute target.
 3. [Train on remote VM](train-on-remote-vm): train a model using a remote Azure VM as compute target.
 4. [Train on AmlCompute](train-on-amlcompute): train a model using an AmlCompute cluster as compute target.
-5. [Logging API](logging-api): experiment with various logging functions to create runs and automatically generate graphs.
+5. [Train in an HDI Spark cluster](train-in-spark): train a Spark ML model using an HDInsight Spark cluster as compute target.
+6. [Logging API](logging-api): experiment with various logging functions to create runs and automatically generate graphs.

how-to-use-azureml/training/logging-api/logging-api.ipynb

@@ -22,7 +22,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-    "Make sure you go through the [00. Installation and Configuration](../../00.configuration.ipynb) Notebook first if you haven't. Also make sure you have tqdm and matplotlib installed in the current kernel.\n",
+        "Make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't. Also make sure you have tqdm and matplotlib installed in the current kernel.\n",
         "\n",
         "```\n",
         "(myenv) $ conda install -y tqdm matplotlib\n",
@@ -46,7 +46,7 @@
       },
       "outputs": [],
       "source": [
-    "from azureml.core import Experiment, Run, Workspace\n",
+        "from azureml.core import Experiment, Workspace\n",
         "import azureml.core\n",
         "import numpy as np\n",
         "\n",

how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb

@@ -31,7 +31,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-    "Make sure you go through the [00.configuration.ipynb](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) Notebook first if you haven't."
+        "Make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't."
       ]
     },
     {
@@ -119,7 +119,7 @@
         "\n",
         "First lets check which VM families are available in your region. Azure is a regional service and some specialized SKUs (especially GPUs) are only available in certain regions. Since AmlCompute is created in the region of your workspace, we will use the supported_vms () function to see if the VM family we want to use ('STANDARD_D2_V2') is supported.\n",
         "\n",
-    "You can also pass a different region to check availability and then re-create your workspace in that region through the [00. Installation and Configuration](00.configuration.ipynb)"
+        "You can also pass a different region to check availability and then re-create your workspace in that region through the [configuration notebook](../../../configuration.ipynb)"
       ]
     },
     {
@@ -214,6 +214,13 @@
         "run"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)."
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -449,8 +456,7 @@
       "outputs": [],
       "source": [
         "#Get_status () gets the latest status of the AmlCompute target\n",
-    "cpu_cluster.get_status()\n",
-    "cpu_cluster.serialize()"
+        "cpu_cluster.get_status().serialize()\n"
       ]
     },
     {

how-to-use-azureml/training/train-on-local/train-on-local.ipynb

@@ -29,7 +29,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-    "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
+        "Make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't."
       ]
     },
     {
@@ -143,7 +143,7 @@
         "run_config_user_managed.environment.python.user_managed_dependencies = True\n",
         "\n",
         "# You can choose a specific Python environment by pointing to a Python path \n",
-    "#run_config.environment.python.interpreter_path = '/home/johndoe/miniconda3/envs/sdk2/bin/python'"
+        "#run_config.environment.python.interpreter_path = '/home/johndoe/miniconda3/envs/myenv/bin/python'"
       ]
     },
     {
@@ -182,6 +182,13 @@
         "run"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -212,7 +219,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "from azureml.core.runconfig import RunConfiguration\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
         "\n",
         "run_config_system_managed = RunConfiguration()\n",
@@ -281,6 +287,7 @@
       "source": [
         "### Docker-based execution\n",
         "**IMPORTANT**: You must have Docker engine installed locally in order to use this execution mode. If your kernel is already running in a Docker container, such as **Azure Notebooks**, this mode will **NOT** work.\n",
+        "\n",
         "NOTE: The GPU base image must be used on Microsoft Azure Services only such as ACI, AML Compute, Azure VMs, and AKS.\n",
         "\n",
         "You can also ask the system to pull down a Docker image and execute your scripts in it."
@@ -296,6 +303,8 @@
         "run_config_docker.environment.python.user_managed_dependencies = False\n",
         "run_config_docker.auto_prepare_environment = True\n",
         "run_config_docker.environment.docker.enabled = True\n",
+        "\n",
+        "# use the default CPU-based Docker image from Azure ML\n",
         "run_config_docker.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
         "\n",
         "# Specify conda dependencies with scikit-learn\n",
@@ -309,7 +318,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "Submit script to run in the system-managed environment\n",
+        "### Submit script to run in the system-managed environment\n",
         "A new conda environment is built based on the conda dependencies object. If you are running this for the first time, this might take up to 5 mninutes. But this conda environment is reused so long as you don't change the conda dependencies.\n",
         "\n",
         "\n"
@@ -353,6 +362,33 @@
         "run.wait_for_completion(show_output=True)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Use a custom Docker image\n",
+        "\n",
+        "You can also specify a custom Docker image if you don't want to use the default image provided by Azure ML.\n",
+        "\n",
+        "```python\n",
+        "# use an image available in Docker Hub without authentication\n",
+        "run_config_docker.environment.docker.base_image = \"continuumio/miniconda3\"\n",
+        "\n",
+        "# or, use an image available in a private Azure Container Registry\n",
+        "run_config_docker.environment.docker.base_image = \"mycustomimage:1.0\"\n",
+        "run_config_docker.environment.docker.base_image_registry.address = \"myregistry.azurecr.io\"\n",
+        "run_config_docker.environment.docker.base_image_registry.username = \"username\"\n",
+        "run_config_docker.environment.docker.base_image_registry.password = \"password\"\n",
+        "```\n",
+        "\n",
+        "When you are using a custom Docker image, you might already have your environment setup properly in a Python environment in the Docker image. In that case, you can skip specifying conda dependencies, and just use `user_managed_dependencies` option instead:\n",
+        "```python\n",
+        "run_config_docker.environment.python.user_managed_dependencies = True\n",
+        "# path to the Python environment in the custom Docker image\n",
+        "run_config.environment.python.interpreter_path = '/opt/conda/bin/python'\n",
+        "```"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -452,7 +488,7 @@
   "metadata": {
     "authors": [
       {
-    "name": "roastala"
+        "name": "haining"
       }
     ],
     "kernelspec": {

how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb

@@ -30,7 +30,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-    "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
+        "Make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't."
       ]
     },
     {
@@ -190,7 +190,7 @@
       "source": [
         "## Create and Attach a DSVM as a compute target\n",
         "\n",
-    "**Note**: To streamline the compute that Azure Machine Learning creates, we are making updates to support creating only single to multi-node `AmlCompute`. The `DSVMCompute` class will be deprecated in a later release, but the DSVM can be created using the below single line command and then attached(like any VM) using the sample code below. Also note, that we only support Linux VMs for remote execution from AML and the commands below will spin a Linux VM only.\n",
+        "**Note**: To streamline the compute that Azure Machine Learning creates, we are making updates to support creating only single to multi-node `AmlCompute`. The DSVM can be created using the below single line command and then attached(like any VM) using the sample code below. Also note, that we only support Linux VMs for remote execution from AML and the commands below will spin a Linux VM only.\n",
         "\n",
         "```shell\n",
         "# create a DSVM in your resource group\n",
@@ -209,9 +209,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "from azureml.core.compute import RemoteCompute\n",
+        "from azureml.core.compute import ComputeTarget, RemoteCompute\n",
         "from azureml.core.compute_target import ComputeTargetException\n",
-    "import os\n",
         "\n",
         "username = os.getenv('AZUREML_DSVM_USERNAME', default='<my_username>')\n",
         "address = os.getenv('AZUREML_DSVM_ADDRESS', default='<ip_address_or_fqdn>')\n",
@@ -222,13 +221,13 @@
         "    attached_dsvm_compute = RemoteCompute(workspace=ws, name=compute_target_name)\n",
         "    print('found existing:', attached_dsvm_compute.name)\n",
         "except ComputeTargetException:\n",
-    "    attached_dsvm_compute = RemoteCompute.attach(workspace=ws,\n",
-    "                                                 name=compute_target_name,\n",
-    "                                                 username=username,\n",
-    "                                                 address=address,\n",
+        "    attach_config = RemoteCompute.attach_configuration(address=address,\n",
         "                                                        ssh_port=22,\n",
+        "                                                        username=username,\n",
         "                                                        private_key_file='./.ssh/id_rsa')\n",
-    "    \n",
+        "    attached_dsvm_compute = ComputeTarget.attach(workspace=ws,\n",
+        "                                                 name=compute_target_name,\n",
+        "                                                 attach_config=attach_config)\n",
         "    attached_dsvm_compute.wait_for_completion(show_output=True)"
       ]
     },
@@ -296,7 +295,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "from azureml.core import Run\n",
         "from azureml.core import ScriptRunConfig\n",
         "\n",
         "src = ScriptRunConfig(source_directory=script_folder, \n",
@@ -308,6 +306,13 @@
         "run = exp.submit(config=src)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)."
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -386,7 +391,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "You can choose to SSH into the VM and install Azure ML SDK, and any other missing dependencies, in that Python environment. For demonstration purposes, we simply are going to create another script `train2.py` that doesn't have azureml dependencies, and submit it instead."
+        "You can choose to SSH into the VM and install Azure ML SDK, and any other missing dependencies, in that Python environment. For demonstration purposes, we simply are going to use another script `train2.py` that doesn't have azureml dependencies, and submit it instead."
       ]
     },
     {
@@ -395,11 +400,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "%%writefile $script_folder/train2.py\n",
+        "# copy train2.py into the script folder\n",
+        "shutil.copy('./train2.py', os.path.join(script_folder, 'train2.py'))\n",
         "\n",
-    "print('####################################')\n",
-    "print('Hello World (without Azure ML SDK)!')\n",
-    "print('####################################')"
+        "with open(os.path.join(script_folder, './train2.py'), 'r') as training_script:\n",
+        "    print(training_script.read())"
       ]
     },
     {
@@ -452,10 +457,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "from azureml.core.runconfig import RunConfiguration\n",
-    "from azureml.core.conda_dependencies import CondaDependencies\n",
-    "\n",
-    "\n",
         "# Load the \"cpu-dsvm.runconfig\" file (created by the above attach operation) in memory\n",
         "docker_run_config = RunConfiguration(framework=\"python\")\n",
         "\n",
@@ -507,6 +508,33 @@
         "run.wait_for_completion(show_output=True)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Use a custom Docker image instead\n",
+        "\n",
+        "You can also specify a custom Docker image if you don't want to use the default image provided by Azure ML.\n",
+        "\n",
+        "```python\n",
+        "# use an image available in Docker Hub without authentication\n",
+        "run_config_docker.environment.docker.base_image = \"continuumio/miniconda3\"\n",
+        "\n",
+        "# or, use an image available in a private Azure Container Registry\n",
+        "run_config_docker.environment.docker.base_image = \"mycustomimage:1.0\"\n",
+        "run_config_docker.environment.docker.base_image_registry.address = \"myregistry.azurecr.io\"\n",
+        "run_config_docker.environment.docker.base_image_registry.username = \"username\"\n",
+        "run_config_docker.environment.docker.base_image_registry.password = \"password\"\n",
+        "```\n",
+        "\n",
+        "When you are using a custom Docker image, you might already have your environment setup properly in a Python environment in the Docker image. In that case, you can skip specifying conda dependencies, and just use `user_managed_dependencies` option instead:\n",
+        "```python\n",
+        "run_config_docker.environment.python.user_managed_dependencies = True\n",
+        "# path to the Python environment in the custom Docker image\n",
+        "run_config.environment.python.interpreter_path = '/opt/conda/bin/python'\n",
+        "```"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

how-to-use-azureml/training/train-on-remote-vm/train2.py

@@ -0,0 +1,6 @@
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+print('####################################')
+print('Hello World (without Azure ML SDK)!')
+print('####################################')

how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb

@@ -57,7 +57,7 @@
         "---\n",
         "\n",
         "## Setup\n",
-    "Make sure you have completed the [Configuration](..\\..\\configuration.ipnyb) notebook to set up your Azure Machine Learning workspace and ensure other common prerequisites are met.  From the configuration, the important sections are the workspace configuration and ACI regristration.\n",
+        "Make sure you have completed the [Configuration](../../../configuration.ipnyb) notebook to set up your Azure Machine Learning workspace and ensure other common prerequisites are met.  From the configuration, the important sections are the workspace configuration and ACI regristration.\n",
         "\n",
         "We will also need the following libraries install to our conda environment.  If these are not installed, use the following command to do so and restart the notebook.\n",
         "```shell\n",
@@ -78,10 +78,10 @@
       "outputs": [],
       "source": [
         "import azureml.core\n",
-    "from azureml.core import Experiment, Run, Workspace\n",
+        "from azureml.core import Experiment, Workspace\n",
         "\n",
         "# Check core SDK version number\n",
-    "print(\"This notebook was created using version 1.0.2 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.0.10 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")\n",
         "print(\"\")\n",
         "\n",
@@ -157,7 +157,8 @@
         "experiment = Experiment(workspace=ws, name=\"train-within-notebook\")\n",
         "\n",
         "# Create a run object in the experiment\n",
-    "run = experiment.start_logging()# Log the algorithm parameter alpha to the run\n",
+        "run = experiment.start_logging()\n",
+        "# Log the algorithm parameter alpha to the run\n",
         "run.log('alpha', 0.03)\n",
         "\n",
         "# Create, fit, and test the scikit-learn Ridge regression model\n",
@@ -215,7 +216,6 @@
       "outputs": [],
       "source": [
         "import numpy as np\n",
-    "import os\n",
         "from tqdm import tqdm\n",
         "\n",
         "model_name = \"model.pkl\"\n",
@@ -568,7 +568,6 @@
       "outputs": [],
       "source": [
         "import requests\n",
-    "import json\n",
         "\n",
         "# use the first row from the test set again\n",
         "test_samples = json.dumps({\"data\": X_test[0:1, :].tolist()})\n",
@@ -598,7 +597,6 @@
       "outputs": [],
       "source": [
         "%matplotlib inline\n",
-    "import matplotlib\n",
         "import matplotlib.pyplot as plt\n",
         "\n",
         "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios':[3, 1], 'wspace':0, 'hspace': 0})\n",
@@ -607,13 +605,13 @@
         "f.set_figheight(6)\n",
         "f.set_figwidth(14)\n",
         "\n",
-    "a0.plot(residual, 'bo', alpha=0.4);\n",
+        "a0.plot(residual, 'bo', alpha=0.4)\n",
         "a0.plot([0,90], [0,0], 'r', lw=2)\n",
         "a0.set_ylabel('residue values', fontsize=14)\n",
         "a0.set_xlabel('test data set', fontsize=14)\n",
         "\n",
-    "a1.hist(residual, orientation='horizontal', color='blue', bins=10, histtype='step');\n",
-    "a1.hist(residual, orientation='horizontal', color='blue', alpha=0.2, bins=10);\n",
+        "a1.hist(residual, orientation='horizontal', color='blue', bins=10, histtype='step')\n",
+        "a1.hist(residual, orientation='horizontal', color='blue', alpha=0.2, bins=10)\n",
         "a1.set_yticklabels([])\n",
         "\n",
         "plt.show()"
@@ -682,11 +680,11 @@
   "metadata": {
     "authors": [
       {
-    "name": "roastala"
+        "name": "haining"
       }
     ],
     "kernelspec": {
-   "display_name": "Python [Python 3.6]",
+      "display_name": "Python 3.6",
       "language": "python",
       "name": "python36"
     },

tutorials/img-classification-part1-training.ipynb

@@ -66,11 +66,10 @@
       "source": [
         "%matplotlib inline\n",
         "import numpy as np\n",
-    "import matplotlib\n",
         "import matplotlib.pyplot as plt\n",
         "\n",
-    "import azureml\n",
-    "from azureml.core import Workspace, Run\n",
+        "import azureml.core\n",
+        "from azureml.core import Workspace\n",
         "\n",
         "# check core SDK version number\n",
         "print(\"Azure ML SDK Version: \", azureml.core.VERSION)"
@@ -176,8 +175,8 @@
         "    # if no min node count is provided it will use the scale settings for the cluster\n",
         "    compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
         "    \n",
-    "     # For a more detailed view of current AmlCompute status, use the 'status' property    \n",
-    "    print(compute_target.status.serialize())"
+        "     # For a more detailed view of current AmlCompute status, use get_status()\n",
+        "    print(compute_target.get_status().serialize())"
       ]
     },
     {
@@ -205,7 +204,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "import os\n",
         "import urllib.request\n",
         "\n",
         "os.makedirs('./data', exist_ok = True)\n",
@@ -354,7 +352,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "import os\n",
         "script_folder = './sklearn-mnist'\n",
         "os.makedirs(script_folder, exist_ok=True)"
       ]
@@ -573,6 +570,13 @@
         "RunDetails(run).show()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -690,7 +694,7 @@
   "metadata": {
     "authors": [
       {
-    "name": "roastala"
+        "name": "haining"
       }
     ],
     "kernelspec": {

tutorials/img-classification-part2-deploy.ipynb

@@ -99,11 +99,9 @@
       "source": [
         "%matplotlib inline\n",
         "import numpy as np\n",
-    "import matplotlib\n",
         "import matplotlib.pyplot as plt\n",
         " \n",
         "import azureml\n",
-    "from azureml.core import Workspace, Run\n",
         "\n",
         "# display the core SDK version number\n",
         "print(\"Azure ML SDK Version: \", azureml.core.VERSION)"
@@ -129,13 +127,9 @@
       },
       "outputs": [],
       "source": [
-    "from azureml.core import Workspace\n",
-    "from azureml.core.model import Model\n",
-    "\n",
         "ws = Workspace.from_config()\n",
         "model=Model(ws, 'sklearn_mnist')\n",
         "model.download(target_dir='.', exist_ok=True)\n",
-    "import os \n",
         "# verify the downloaded model file\n",
         "os.stat('./sklearn_mnist_model.pkl')"
       ]
@@ -521,7 +515,6 @@
       "outputs": [],
       "source": [
         "import requests\n",
-    "import json\n",
         "\n",
         "# send a random row from the test set to score\n",
         "random_index = np.random.randint(0, len(X_test)-1)\n",
@@ -581,14 +574,14 @@
         "> * Deploy the model to ACI\n",
         "> * Test the deployed model\n",
         " \n",
-    "You can also try out the [Automatic algorithm selection tutorial](03.auto-train-models.ipynb) to see how Azure Machine Learning can auto-select and tune the best algorithm for your model and build that model for you."
+        "You can also try out the [regression tutorial](regression-part1-data-prep.ipynb)."
       ]
     }
   ],
   "metadata": {
     "authors": [
       {
-    "name": "roastala"
+        "name": "haining"
       }
     ],
     "kernelspec": {

tutorials/regression-part2-automated-ml.ipynb

@@ -26,7 +26,7 @@
         "> * Explore the results\n",
         "> * Register the best model\n",
         "\n",
-    "If you don’t have an Azure subscription, create a [free account](https://aka.ms/AMLfree) before you begin. \n",
+        "If you don\u00e2\u20ac\u2122t have an Azure subscription, create a [free account](https://aka.ms/AMLfree) before you begin. \n",
         "\n",
         "> Code in this article was tested with Azure Machine Learning SDK version 1.0.0\n",
         "\n",
@@ -55,8 +55,6 @@
         "import azureml.core\n",
         "import pandas as pd\n",
         "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl.run import AutoMLRun\n",
-    "import time\n",
         "import logging"
       ]
     },
@@ -93,7 +91,8 @@
         "output['Location'] = ws.location\n",
         "output['Project Directory'] = project_folder\n",
         "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data=output, index=['']).T"
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
       ]
     },
     {
@@ -112,7 +111,6 @@
       "outputs": [],
       "source": [
         "import azureml.dataprep as dprep\n",
-    "import os\n",
         "\n",
         "file_path = os.path.join(os.getcwd(), \"dflows.dprep\")\n",
         "\n",
@@ -308,7 +306,6 @@
         "    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"
       ]
@@ -351,7 +348,7 @@
         "description = 'Automated Machine Learning Model'\n",
         "tags = None\n",
         "local_run.register_model(description=description, tags=tags)\n",
-    "local_run.model_id # Use this id to deploy the model as a web service in Azure"
+        "print(local_run.model_id) # Use this id to deploy the model as a web service in Azure"
       ]
     },
     {
@@ -471,7 +468,7 @@
         "> * Explored and reviewed training results\n",
         "> * Registered the best model\n",
         "\n",
-    "[Deploy your model](02.deploy-models.ipynb) with Azure Machine Learning."
+        "You can also try out the [image classification tutorial](img-classification-part1-training.ipynb)."
       ]
     }
   ],