Merge pull request #182 from rastala/master

version 1.0.10

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.
 
-You can find instructions on setting up notebooks [here](./NBSETUP.md)
+![Azure ML workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/service/media/overview-what-is-azure-ml/aml.png)
 
-You can find full documentation for Azure Machine Learning [here](https://aka.ms/aml-docs)
+## 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 1.0.2 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\")"
       ]
     },
@@ -368,7 +368,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.6.5"
     }
   },
   "nbformat": 4,

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/README.md

@@ -13,3 +13,4 @@ As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) not
 * [enable-data-collection-for-models-in-aks](./deployment/enable-data-collection-for-models-in-aks) Learn about data collection APIs for deployed model.
 * [enable-app-insights-in-production-service](./deployment/enable-app-insights-in-production-service) Learn how to use App Insights with production web service.
  
+Find quickstarts, end-to-end tutorials, and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/).

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

@@ -34,7 +34,8 @@ Below are the three execution environments supported by AutoML.
 **NOTE**: Please create your Azure Databricks cluster as v4.x (high concurrency preferred) with **Python 3** (dropdown).
 **NOTE**: You should at least have contributor access to your Azure subcription to run the notebook.
 - Please remove the previous SDK version if there is any and install the latest SDK by installing **azureml-sdk[automl_databricks]** as a PyPi library in Azure Databricks workspace.
-- Download the sample notebook 16a.auto-ml-classification-local-azuredatabricks from [GitHub](https://github.com/Azure/MachineLearningNotebooks) and import into the Azure databricks workspace.
+- You can find the detail Readme instructions at [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks).
+- Download the sample notebook automl-databricks-local-01.ipynb from [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks) and import into the Azure databricks workspace.
 - Attach the notebook to the cluster.
 
 <a name="localconda"></a>
@@ -57,7 +58,7 @@ jupyter notebook
 ```
 
 
-### 1. Install mini-conda from [here](https://conda.io/miniconda.html), choose Python 3.7 or higher.
+### 1. Install mini-conda from [here](https://conda.io/miniconda.html), choose 64-bit Python 3.7 or higher.
 - **Note**: if you already have conda installed, you can keep using it but it should be version 4.4.10 or later (as shown by: conda -V).  If you have a previous version installed, you can update it using the command: conda update conda.
 There's no need to install mini-conda specifically.
 
@@ -123,7 +124,7 @@ bash automl_setup_linux.sh
 
 - [auto-ml-remote-batchai.ipynb](remote-batchai/auto-ml-remote-batchai.ipynb)
     - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
-    - Example of using automated ML for classification using a remote Batch AI compute for training
+    - Example of using automated ML for classification using remote AmlCompute for training
     - Parallel execution of iterations
     - Async tracking of progress
     - Cancelling individual iterations or entire run
@@ -174,118 +175,21 @@ 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_tensorflow.ipynb](classification_with_tensorflow/auto-ml-classification_with_tensorflow.ipynb)
+- [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.checkout
+    - Simple example of using Auto ML for classification with whitelisting tensorflow models.
     - Uses local compute for training
 
-- [auto-ml-forecasting-a.ipynb](forecasting-a/auto-ml-forecasting-a.ipynb)
+- [auto-ml-forecasting-energy-demand.ipynb](forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb)
     - Dataset: [NYC energy demand data](forecasting-a/nyc_energy.csv)
     - Example of using AutoML for training a forecasting model
 
-- [auto-ml-forecasting-b.ipynb](forecasting-b/auto-ml-forecasting-b.ipynb)
+- [auto-ml-forecasting-orange-juice-sales.ipynb](forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb)
     - Dataset: [Dominick's grocery sales of orange juice](forecasting-b/dominicks_OJ.csv)
     - Example of training an AutoML forecasting model on multiple time-series
 
 <a name="documentation"></a>
-# Documentation
-## Table of Contents
-1. [Automated ML Settings ](#automlsettings)
-1. [Cross validation split options](#cvsplits)
-1. [Get Data Syntax](#getdata)
-1. [Data pre-processing and featurization](#preprocessing)
-
-<a name="automlsettings"></a>
-## Automated ML Settings
-
-|Property|Description|Default|
-|-|-|-|
-|**primary_metric**|This is the metric that you want to optimize.<br><br> Classification supports the following primary metrics <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i><br><br> Regression supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i><br><i>normalized_root_mean_squared_log_error</i>| Classification: accuracy <br><br> Regression: spearman_correlation
-|**iteration_timeout_minutes**|Time limit in minutes for each iteration|None|
-|**iterations**|Number of iterations. In each iteration trains the data with a specific pipeline.  To get the best result, use at least 100. |100|
-|**n_cross_validations**|Number of cross validation splits|None|
-|**validation_size**|Size of validation set as percentage of all training samples|None|
-|**max_concurrent_iterations**|Max number of iterations that would be executed in parallel|1|
-|**preprocess**|*True/False* <br>Setting this to *True* enables preprocessing <br>on the input to handle missing data, and perform some common feature extraction<br>*Note: If input data is Sparse you cannot use preprocess=True*|False|
-|**max_cores_per_iteration**| Indicates how many cores on the compute target would be used to train a single pipeline.<br> You can set it to *-1* to use all cores|1|
-|**experiment_exit_score**|*double* value indicating the target for *primary_metric*. <br> Once the target is surpassed the run terminates|None|
-|**blacklist_models**|*Array* of *strings* indicating models to ignore for Auto ML from the list of models.|None|
-|**whitelist_models**|*Array* of *strings* use only models listed for Auto ML from the list of models..|None|
- <a name="cvsplits"></a>
-## List of models for white list/blacklist
-**Classification**
-<br><i>LogisticRegression</i>
-<br><i>SGD</i>
-<br><i>MultinomialNaiveBayes</i>
-<br><i>BernoulliNaiveBayes</i>
-<br><i>SVM</i>
-<br><i>LinearSVM</i>
-<br><i>KNN</i>
-<br><i>DecisionTree</i>
-<br><i>RandomForest</i>
-<br><i>ExtremeRandomTrees</i>
-<br><i>LightGBM</i>
-<br><i>GradientBoosting</i>
-<br><i>TensorFlowDNN</i>
-<br><i>TensorFlowLinearClassifier</i>
-<br><br>**Regression**
-<br><i>ElasticNet</i>
-<br><i>GradientBoosting</i>
-<br><i>DecisionTree</i>
-<br><i>KNN</i>
-<br><i>LassoLars</i>
-<br><i>SGD</i>
-<br><i>RandomForest</i>
-<br><i>ExtremeRandomTrees</i>
-<br><i>LightGBM</i>
-<br><i>TensorFlowLinearRegressor</i>
-<br><i>TensorFlowDNN</i>
-
-## Cross validation split options
-### K-Folds Cross Validation
-Use *n_cross_validations* setting to specify the number of cross validations. The training data set will be randomly split into *n_cross_validations* folds of equal size. During each cross validation round, one of the folds will be used for validation of the model trained on the remaining folds. This process repeats for *n_cross_validations* rounds until each fold is used once as validation set. Finally, the average scores accross all *n_cross_validations* rounds will be reported, and the corresponding model will be retrained on the whole training data set.
-
-### Monte Carlo Cross Validation (a.k.a. Repeated Random Sub-Sampling)
-Use *validation_size* to specify the percentage of the training data set that should be used for validation, and use *n_cross_validations* to specify the number of cross validations. During each cross validation round, a subset of size *validation_size* will be randomly selected for validation of the model trained on the remaining data. Finally, the average scores accross all *n_cross_validations* rounds will be reported, and the corresponding model will be retrained on the whole training data set.
-
-### Custom train and validation set
-You can specify seperate train and validation set either through the get_data() or directly to the fit method.
-
-<a name="getdata"></a>
-## get_data() syntax
-The *get_data()* function can be used to return a dictionary with these values:
-
-|Key|Type|Dependency|Mutually Exclusive with|Description|
-|:-|:-|:-|:-|:-|
-|X|Pandas Dataframe or Numpy Array|y|data_train, label, columns|All features to train with|
-|y|Pandas Dataframe or Numpy Array|X|label|Label data to train with.  For classification, this should be an array of integers. |
-|X_valid|Pandas Dataframe or Numpy Array|X, y, y_valid|data_train, label|*Optional* All features to validate with.  If this is not specified, X is split between train and validate|
-|y_valid|Pandas Dataframe or Numpy Array|X, y, X_valid|data_train, label|*Optional* The label data to validate with.  If this is not specified, y is split between train and validate|
-|sample_weight|Pandas Dataframe or Numpy Array|y|data_train, label, columns|*Optional* A weight value for each label. Higher values indicate that the sample is more important.|
-|sample_weight_valid|Pandas Dataframe or Numpy Array|y_valid|data_train, label, columns|*Optional* A weight value for each validation label. Higher values indicate that the sample is more important.  If this is not specified, sample_weight is split between train and validate|
-|data_train|Pandas Dataframe|label|X, y, X_valid, y_valid|All data (features+label) to train with|
-|label|string|data_train|X, y, X_valid, y_valid|Which column in data_train represents the label|
-|columns|Array of strings|data_train||*Optional* Whitelist of columns to use for features|
-|cv_splits_indices|Array of integers|data_train||*Optional* List of indexes to split the data for cross validation|
-
-<a name="preprocessing"></a>
-## Data pre-processing and featurization
-If you use `preprocess=True`, the following data preprocessing steps are performed automatically for you:
-
-1. Dropping high cardinality or no variance features
-    - Features with no useful information are dropped from training and validation sets. These include features with all values missing, same value across all rows or with extremely high cardinality (e.g., hashes, IDs or GUIDs).
-2. Missing value imputation
-    - For numerical features, missing values are imputed with average of values in the column.
-    - For categorical features, missing values are imputed with most frequent value.
-3. Generating additional features
-    - For DateTime features: Year, Month, Day, Day of week, Day of year, Quarter, Week of the year, Hour, Minute, Second.
-    - For Text features: Term frequency based on bi-grams and tri-grams, Count vectorizer.
-4. Transformations and encodings
-    - Numeric features with very few unique values are transformed into categorical features.
+See [Configure automated machine learning experiments](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-auto-train) to learn how more about the the settings and features available for automated machine learning experiments.
 
 <a name="pythoncommand"></a>
 # Running using python command
@@ -302,8 +206,9 @@ The main code of the file must be indented so that it is under this condition.
 # Troubleshooting
 ## automl_setup fails
 1. On windows, make sure that you are running automl_setup from an Anconda Prompt window rather than a regular cmd window.  You can launch the "Anaconda Prompt" window by hitting the Start button and typing "Anaconda Prompt".  If you don't see the application "Anaconda Prompt", you might not have conda or mini conda installed.  In that case, you can install it [here](https://conda.io/miniconda.html)
-2. Check that you have conda 4.4.10 or later.  You can check the version with the command `conda -V`.  If you have a previous version installed, you can update it using the command: `conda update conda`.
-3. Pass a new name as the first parameter to automl_setup so that it creates a new conda environment. You can view existing conda environments using `conda env list` and remove them with `conda env remove -n <environmentname>`. 
+2. Check that you have conda 64-bit installed rather than 32-bit.  You can check this with the command `conda info`.  The `platform` should be `win-64` for Windows or `osx-64` for Mac.
+3. Check that you have conda 4.4.10 or later.  You can check the version with the command `conda -V`.  If you have a previous version installed, you can update it using the command: `conda update conda`.
+4. Pass a new name as the first parameter to automl_setup so that it creates a new conda environment. You can view existing conda environments using `conda env list` and remove them with `conda env remove -n <environmentname>`. 
 
 ## configuration.ipynb fails
 1) For local conda, make sure that you have susccessfully run automl_setup first.

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"
@@ -21,25 +22,23 @@ if not errorlevel 1 (
 call conda activate %conda_env_name% 2>nul:
 if errorlevel 1 goto ErrorExit
 
-call pip install psutil
-
 call python -m ipykernel install --user --name %conda_env_name% --display-name "Python (%conda_env_name%)"
 
-call jupyter nbextension install --py azureml.widgets --user
-if errorlevel 1 goto ErrorExit
-
-call jupyter nbextension enable --py azureml.widgets --user
-if errorlevel 1 goto ErrorExit
+REM azureml.widgets is now installed as part of the pip install under the conda env.
+REM Removing the old user install so that the notebooks will use the latest widget.
+call jupyter nbextension uninstall --user --py azureml.widgets
 
 echo.
 echo.
 echo ***************************************
 echo * AutoML setup completed successfully *
 echo ***************************************
+IF NOT "%options%"=="nolaunch" (
   echo.
   echo Starting jupyter notebook - please run the configuration notebook 
   echo.
-jupyter notebook --log-level=50
+  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" == "" ]
@@ -22,22 +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 install --py azureml.widgets --user &&
-   jupyter nbextension enable --py azureml.widgets --user &&
+   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
+      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" == "" ]
@@ -22,24 +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)" &&
-   jupyter nbextension install --py azureml.widgets --user &&
-   jupyter nbextension enable --py azureml.widgets --user &&
-   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
+      jupyter notebook --log-level=50 --notebook-dir '../..'
+   fi
 fi
 
 if [ $? -gt 0 ]

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

@@ -1,568 +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: Classification local on Azure DataBricks\n",
-    "\n",
-    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
-    "\n",
-    "In this notebook you will learn how to:\n",
-    "1. Create Azure Machine Learning Workspace object and initialize your notebook directory to easily reload this object from a configuration file.\n",
-    "2. Create an `Experiment` in an existing `Workspace`.\n",
-    "3. Configure AutoML using `AutoMLConfig`.\n",
-    "4. Train the model using AzureDataBricks.\n",
-    "5. Explore the results.\n",
-    "6. Test the best fitted model.\n",
-    "\n",
-    "Prerequisites:\n",
-    "Before running this notebook, run the install instructions described in README.md."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Register Machine Learning Services Resource Provider\n",
-    "Microsoft.MachineLearningServices only needs to be registed once in the subscription. To register it:\n",
-    "Start the Azure portal.\n",
-    "Select your All services and then Subscription.\n",
-    "Select the subscription that you want to use.\n",
-    "Click on Resource providers\n",
-    "Click the Register link next to Microsoft.MachineLearningServices"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Check the Azure ML Core SDK Version to Validate Your Installation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import azureml.core\n",
-    "\n",
-    "print(\"SDK Version:\", azureml.core.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialize an Azure ML Workspace\n",
-    "### What is an Azure ML Workspace and Why Do I Need One?\n",
-    "\n",
-    "An Azure ML workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an Azure ML workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, operationalization, and the monitoring of operationalized models.\n",
-    "\n",
-    "\n",
-    "### What do I Need?\n",
-    "\n",
-    "To create or access an Azure ML workspace, you will need to import the Azure ML library and specify following information:\n",
-    "* A name for your workspace. You can choose one.\n",
-    "* Your subscription id. Use the `id` value from the `az account show` command output above.\n",
-    "* The resource group name. The resource group organizes Azure resources and provides a default region for the resources in the group. The resource group will be created if it doesn't exist. Resource groups can be created and viewed in the [Azure portal](https://portal.azure.com)\n",
-    "* Supported regions include `eastus2`, `eastus`,`westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "subscription_id = \"<SubscriptionId>\"\n",
-    "resource_group = \"myrg\"\n",
-    "workspace_name = \"myws\"\n",
-    "workspace_region = \"eastus2\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Creating a Workspace\n",
-    "If you already have access to an Azure ML workspace you want to use, you can skip this cell.  Otherwise, this cell will create an Azure ML workspace for you in the specified subscription, provided you have the correct permissions for the given `subscription_id`.\n",
-    "\n",
-    "This will fail when:\n",
-    "1. The workspace already exists.\n",
-    "2. You do not have permission to create a workspace in the resource group.\n",
-    "3. You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription.\n",
-    "\n",
-    "If workspace creation fails for any reason other than already existing, please work with your IT administrator to provide you with the appropriate permissions or to provision the required resources.\n",
-    "\n",
-    "**Note:** Creation of a new workspace can take several minutes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import the Workspace class and check the Azure ML SDK version.\n",
-    "from azureml.core import Workspace\n",
-    "\n",
-    "ws = Workspace.create(name = workspace_name,\n",
-    "                      subscription_id = subscription_id,\n",
-    "                      resource_group = resource_group, \n",
-    "                      location = workspace_region,\n",
-    "                      exist_ok=True)\n",
-    "ws.get_details()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Configuring Your Local Environment\n",
-    "You can validate that you have access to the specified workspace and write a configuration file to the default configuration location, `./aml_config/config.json`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core import Workspace\n",
-    "\n",
-    "ws = Workspace(workspace_name = workspace_name,\n",
-    "               subscription_id = subscription_id,\n",
-    "               resource_group = resource_group)\n",
-    "\n",
-    "# Persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
-    "ws.write_config()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create a Folder to Host Sample Projects\n",
-    "Finally, create a folder where all the sample projects will be hosted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "sample_projects_folder = './sample_projects'\n",
-    "\n",
-    "if not os.path.isdir(sample_projects_folder):\n",
-    "    os.mkdir(sample_projects_folder)\n",
-    "    \n",
-    "print('Sample projects will be created in {}.'.format(sample_projects_folder))"
-   ]
-  },
-  {
-   "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": [
-    "ws = Workspace.from_config()\n",
-    "\n",
-    "# Choose a name for the experiment and specify the project folder.\n",
-    "experiment_name = 'automl-local-classification'\n",
-    "project_folder = './sample_projects/automl-local-classification'\n",
-    "\n",
-    "experiment = Experiment(ws, experiment_name)\n",
-    "\n",
-    "output = {}\n",
-    "output['SDK version'] = azureml.core.VERSION\n",
-    "output['Subscription ID'] = ws.subscription_id\n",
-    "output['Workspace Name'] = ws.name\n",
-    "output['Resource Group'] = ws.resource_group\n",
-    "output['Location'] = ws.location\n",
-    "output['Project Directory'] = project_folder\n",
-    "output['Experiment Name'] = experiment.name\n",
-    "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data = output, index = ['']).T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnostics\n",
-    "\n",
-    "Opt-in diagnostics for better experience, quality, and security of future releases."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.telemetry import set_diagnostics_collection\n",
-    "set_diagnostics_collection(send_diagnostics = True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load Training Data Using DataPrep\n"
-   ]
-  },
-  {
-   "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 = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1)  # Remove the header row.\n",
-    "\n",
-    "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
-    "# and convert column types manually.\n",
-    "# Here we read a comma delimited file and convert all columns to integers.\n",
-    "y = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## 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.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",
-    "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
-    "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
-    "|**n_cross_validations**|Number of cross validation splits.|\n",
-    "|**spark_context**|Spark Context object.|\n",
-    "|**max_cuncurrent_iterations**|Maximum number of iterations to execute in parallel. This should be less than the number of cores on the ADB..|\n",
-    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
-    "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "automl_settings = {\n",
-    "    \"iteration_timeout_minutes\": 10,\n",
-    "    \"iterations\": 10,\n",
-    "    \"n_cross_validations\": 5,\n",
-    "    \"primary_metric\": 'AUC_weighted',\n",
-    "    \"preprocess\": False,\n",
-    "    \"max_concurrent_iterations\": 2,\n",
-    "    \"verbosity\": logging.INFO,\n",
-    "    \"spark_context\": sc\n",
-    "}\n",
-    " \n",
-    "automl_config = AutoMLConfig(task = 'classification',\n",
-    "                             debug_log = 'automl_errors.log',\n",
-    "                             path = project_folder, \n",
-    "                             X = X, \n",
-    "                             y = y,\n",
-    "                             **automl_settings\n",
-    "                            )\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Train the Models\n",
-    "\n",
-    "Call the `submit` method on the experiment object and pass the run configuration. 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 = False)"
-   ]
-  },
-  {
-   "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."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(local_run.get_portal_url())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "The following will show the child runs and waits for the parent run to complete."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "local_run.wait_for_completion(show_output = True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Retrieve All Child Runs\n",
-    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "children = list(local_run.get_children())\n",
-    "metricslist = {}\n",
-    "for run in children:\n",
-    "    properties = run.get_properties()\n",
-    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
-    "    metricslist[int(properties['iteration'])] = metrics\n",
-    "\n",
-    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
-    "rundata"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Retrieve the Best Model\n",
-    "\n",
-    "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_run, fitted_model = local_run.get_output()\n",
-    "print(best_run)\n",
-    "print(fitted_model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Best Model Based on Any Other Metric\n",
-    "Show the run and the model that has the smallest `log_loss` value:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lookup_metric = \"log_loss\"\n",
-    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
-    "print(best_run)\n",
-    "print(fitted_model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Model from a Specific Iteration\n",
-    "Show the run and the model from the third iteration:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iteration = 3\n",
-    "third_run, third_model = local_run.get_output(iteration = iteration)\n",
-    "print(third_run)\n",
-    "print(third_model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Test the Best Fitted Model\n",
-    "\n",
-    "#### Load Test Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "digits = datasets.load_digits()\n",
-    "X_test = digits.data[:10, :]\n",
-    "y_test = digits.target[:10]\n",
-    "images = digits.images[:10]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Testing Our Best Fitted Model\n",
-    "We will try to predict 2 digits and see how our model works."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Randomly select digits and test.\n",
-    "for index in np.random.choice(len(y_test), 2, replace = False):\n",
-    "    print(index)\n",
-    "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
-    "    label = y_test[index]\n",
-    "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
-    "    fig = plt.figure(1, figsize = (3,3))\n",
-    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
-    "    ax1.set_title(title)\n",
-    "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
-    "    display(fig)"
-   ]
-  }
- ],
- "metadata": {
-  "authors": [
-   {
-    "name": "savitam"
-   }
-  ],
-  "kernelspec": {
-   "display_name": "Python [conda env:AutoML_ADB]",
-   "language": "python",
-   "name": "conda-env-AutoML_ADB-py"
-  },
-  "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"
-  },
-  "name": "auto-ml-classification-local-adb",
-  "notebookId": 3742842704905931
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}

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

@@ -13,11 +13,26 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "# Automated Machine Learning: Classification with Deployment\n",
+        "# Automated Machine Learning\n",
+        "_**Classification with Deployment**_\n",
+        "\n",
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Train](#Train)\n",
+        "1. [Deploy](#Deploy)\n",
+        "1. [Test](#Test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
         "\n",
         "In this example we use the scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) to showcase how you can use AutoML for a simple classification problem and deploy it to an Azure Container Instance (ACI).\n",
         "\n",
-    "Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
         "\n",
         "In this notebook you will learn how to:\n",
         "1. Create an experiment using an existing workspace.\n",
@@ -27,14 +42,14 @@
         "5. Register the model.\n",
         "6. Create a container image.\n",
         "7. Create an Azure Container Instance (ACI) service.\n",
-    "8. Test the ACI service.\n"
+        "8. Test the ACI service."
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Create an Experiment\n",
+        "## Setup\n",
         "\n",
         "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
       ]
@@ -47,11 +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",
@@ -87,15 +99,14 @@
         "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"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Diagnostics\n",
-    "\n",
         "Opt-in diagnostics for better experience, quality, and security of future releases."
       ]
     },
@@ -113,7 +124,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Configure AutoML\n",
+        "## Train\n",
         "\n",
         "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
         "\n",
@@ -156,8 +167,6 @@
       "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."
       ]
@@ -171,10 +180,21 @@
         "local_run = experiment.submit(automl_config, show_output = True)"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "local_run"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
+        "## Deploy\n",
+        "\n",
         "### Retrieve the Best Model\n",
         "\n",
         "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
@@ -206,7 +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."
       ]
     },
     {
@@ -442,7 +463,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "### Test a Web Service"
+        "## Test"
       ]
     },
     {

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

@@ -0,0 +1,398 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Automated Machine Learning\n",
+        "_**Classification using whitelist models**_\n",
+        "\n",
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Data](#Data)\n",
+        "1. [Train](#Train)\n",
+        "1. [Results](#Results)\n",
+        "1. [Test](#Test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "\n",
+        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
+        "\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
+        "This notebooks shows how can automl can be trained on a a selected list of models,see the readme.md for the models.\n",
+        "This trains the model exclusively on tensorflow based models.\n",
+        "\n",
+        "In this notebook you will learn how to:\n",
+        "1. Create an `Experiment` in an existing `Workspace`.\n",
+        "2. Configure AutoML using `AutoMLConfig`.\n",
+        "3. Train the model on a whilelisted models using local compute. \n",
+        "4. Explore the results.\n",
+        "5. Test the best fitted model."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup\n",
+        "\n",
+        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import logging\n",
+        "\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import datasets\n",
+        "\n",
+        "import azureml.core\n",
+        "from azureml.core.experiment import Experiment\n",
+        "from azureml.core.workspace import Workspace\n",
+        "from azureml.train.automl import AutoMLConfig"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "\n",
+        "# Choose a name for the experiment and specify the project folder.\n",
+        "experiment_name = 'automl-local-whitelist'\n",
+        "project_folder = './sample_projects/automl-local-whitelist'\n",
+        "\n",
+        "experiment = Experiment(ws, experiment_name)\n",
+        "\n",
+        "output = {}\n",
+        "output['SDK version'] = azureml.core.VERSION\n",
+        "output['Subscription ID'] = ws.subscription_id\n",
+        "output['Workspace Name'] = ws.name\n",
+        "output['Resource Group'] = ws.resource_group\n",
+        "output['Location'] = ws.location\n",
+        "output['Project Directory'] = project_folder\n",
+        "output['Experiment Name'] = experiment.name\n",
+        "pd.set_option('display.max_colwidth', -1)\n",
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "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": [
+        "## Data\n",
+        "\n",
+        "This uses scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "digits = datasets.load_digits()\n",
+        "\n",
+        "# Exclude the first 100 rows from training so that they can be used for test.\n",
+        "X_train = digits.data[100:,:]\n",
+        "y_train = digits.target[100:]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train\n",
+        "\n",
+        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
+        "\n",
+        "|Property|Description|\n",
+        "|-|-|\n",
+        "|**task**|classification or regression|\n",
+        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
+        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
+        "|**n_cross_validations**|Number of cross validation splits.|\n",
+        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+        "|**y**|(sparse) array-like, shape = [n_samples, ], [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",
+        "|**whitelist_models**|List of models that AutoML should use.  The possible values are listed [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#configure-your-experiment-settings).|"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "automl_config = AutoMLConfig(task = 'classification',\n",
+        "                             debug_log = 'automl_errors.log',\n",
+        "                             primary_metric = 'AUC_weighted',\n",
+        "                             iteration_timeout_minutes = 60,\n",
+        "                             iterations = 10,\n",
+        "                             n_cross_validations = 3,\n",
+        "                             verbosity = logging.INFO,\n",
+        "                             X = X_train, \n",
+        "                             y = y_train,\n",
+        "                             enable_tf=True,\n",
+        "                             whitelist_models=[\"TensorFlowLinearClassifier\", \"TensorFlowDNN\"],\n",
+        "                             path = project_folder)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
+        "In this example, we specify `show_output = True` to print currently running iterations to the console."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "local_run = experiment.submit(automl_config, show_output = True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "local_run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Results"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Widget for Monitoring Runs\n",
+        "\n",
+        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
+        "\n",
+        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.widgets import RunDetails\n",
+        "RunDetails(local_run).show() "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n",
+        "#### Retrieve All Child Runs\n",
+        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "children = list(local_run.get_children())\n",
+        "metricslist = {}\n",
+        "for run in children:\n",
+        "    properties = run.get_properties()\n",
+        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
+        "    metricslist[int(properties['iteration'])] = metrics\n",
+        "\n",
+        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
+        "rundata"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Retrieve the Best Model\n",
+        "\n",
+        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "best_run, fitted_model = local_run.get_output()\n",
+        "print(best_run)\n",
+        "print(fitted_model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Best Model Based on Any Other Metric\n",
+        "Show the run and the model that has the smallest `log_loss` value:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "lookup_metric = \"log_loss\"\n",
+        "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
+        "print(best_run)\n",
+        "print(fitted_model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Model from a Specific Iteration\n",
+        "Show the run and the model from the third iteration:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "iteration = 3\n",
+        "third_run, third_model = local_run.get_output(iteration = iteration)\n",
+        "print(third_run)\n",
+        "print(third_model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Test\n",
+        "\n",
+        "#### Load Test Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "digits = datasets.load_digits()\n",
+        "X_test = digits.data[:10, :]\n",
+        "y_test = digits.target[:10]\n",
+        "images = digits.images[:10]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Testing Our Best Fitted Model\n",
+        "We will try to predict 2 digits and see how our model works."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Randomly select digits and test.\n",
+        "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+        "    print(index)\n",
+        "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+        "    label = y_test[index]\n",
+        "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
+        "    fig = plt.figure(1, figsize = (3,3))\n",
+        "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+        "    ax1.set_title(title)\n",
+        "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+        "    plt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "savitam"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

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

@@ -13,25 +13,42 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "# Automated Machine Learning: Classification with Local Compute\n",
+        "# Automated Machine Learning\n",
+        "_**Classification with Local Compute**_\n",
         "\n",
-    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
-    "\n",
-    "Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
-    "\n",
-    "In this notebook you will learn how to:\n",
-    "1. Create an `Experiment` in an existing `Workspace`.\n",
-    "2. Configure AutoML using `AutoMLConfig`.\n",
-    "3. Train the model using local compute.\n",
-    "4. Explore the results.\n",
-    "5. Test the best fitted model.\n"
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Data](#Data)\n",
+        "1. [Train](#Train)\n",
+        "1. [Results](#Results)\n",
+        "1. [Test](#Test)\n",
+        "\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Create an Experiment\n",
+        "## Introduction\n",
+        "\n",
+        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
+        "\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
+        "\n",
+        "In this notebook you will learn how to:\n",
+        "1. Create an `Experiment` in an existing `Workspace`.\n",
+        "2. Configure AutoML using `AutoMLConfig`.\n",
+        "3. Train the model using local compute.\n",
+        "4. Explore the results.\n",
+        "5. Test the best fitted model."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup\n",
         "\n",
         "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
       ]
@@ -43,11 +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",
@@ -55,8 +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"
       ]
     },
     {
@@ -82,15 +95,14 @@
         "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"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Diagnostics\n",
-    "\n",
         "Opt-in diagnostics for better experience, quality, and security of future releases."
       ]
     },
@@ -108,7 +120,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Load Training Data\n",
+        "## Data\n",
         "\n",
         "This uses scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
       ]
@@ -119,8 +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",
@@ -132,7 +142,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Configure AutoML\n",
+        "## Train\n",
         "\n",
         "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
         "\n",
@@ -170,8 +180,6 @@
       "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."
       ]
@@ -213,20 +221,11 @@
         "                                          iterations = 5)"
       ]
     },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "local_run"
-   ]
-  },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Explore the Results"
+        "## Results"
       ]
     },
     {
@@ -340,7 +339,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "### Test the Best Fitted Model\n",
+        "## Test \n",
         "\n",
         "#### Load Test Data"
       ]

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

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

how-to-use-azureml/automated-machine-learning/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

@@ -13,10 +13,26 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "# Automated Machine Learning: Prepare Data using `azureml.dataprep` for Remote Execution (DSVM)\n",
+        "# Automated Machine Learning\n",
+        "_**Prepare Data using `azureml.dataprep` for Remote Execution (DSVM)**_\n",
+        "\n",
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Data](#Data)\n",
+        "1. [Train](#Train)\n",
+        "1. [Results](#Results)\n",
+        "1. [Test](#Test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
         "In this example we showcase how you can use the `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone; full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
         "\n",
-    "Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
         "\n",
         "In this notebook you will learn how to:\n",
         "1. Define data loading and preparation steps in a `Dataflow` using `azureml.dataprep`.\n",
@@ -28,7 +44,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "# Compatibility\n",
+        "## Setup\n",
         "\n",
         "Currently, Data Prep only supports __Ubuntu 16__ and __Red Hat Enterprise Linux 7__. We are working on supporting more linux distros."
       ]
@@ -37,8 +53,6 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Diagnostics\n",
-    "\n",
         "Opt-in diagnostics for better experience, quality, and security of future releases."
       ]
     },
@@ -56,8 +70,6 @@
       "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."
       ]
     },
@@ -68,7 +80,6 @@
       "outputs": [],
       "source": [
         "import logging\n",
-    "import os\n",
         "import time\n",
         "\n",
         "import pandas as pd\n",
@@ -105,14 +116,15 @@
         "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"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Loading Data using DataPrep"
+        "## Data"
       ]
     },
     {
@@ -136,8 +148,6 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Review the Data Preparation Result\n",
-    "\n",
         "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets."
       ]
     },
@@ -154,7 +164,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Configure AutoML\n",
+        "## Train\n",
         "\n",
         "This creates a general AutoML settings object applicable for both local and remote runs."
       ]
@@ -175,13 +185,6 @@
         "}"
       ]
     },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Remote Run"
-   ]
-  },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -262,11 +265,20 @@
         "remote_run = experiment.submit(automl_config, show_output = True)"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Explore the Results"
+        "## Results"
       ]
     },
     {
@@ -311,7 +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"
       ]
@@ -380,7 +391,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "### Test the Best Fitted Model\n",
+        "## Test\n",
         "\n",
         "#### Load Test Data"
       ]
@@ -415,8 +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",
@@ -470,7 +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

@@ -13,10 +13,26 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "# Automated Machine Learning: Prepare Data using `azureml.dataprep` for Local Execution\n",
+        "# Automated Machine Learning\n",
+        "_**Prepare Data using `azureml.dataprep` for Local Execution**_\n",
+        "\n",
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Data](#Data)\n",
+        "1. [Train](#Train)\n",
+        "1. [Results](#Results)\n",
+        "1. [Test](#Test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
         "In this example we showcase how you can use the `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone; full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
         "\n",
-    "Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
         "\n",
         "In this notebook you will learn how to:\n",
         "1. Define data loading and preparation steps in a `Dataflow` using `azureml.dataprep`.\n",
@@ -28,7 +44,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "# Compatibility\n",
+        "## Setup\n",
         "\n",
         "Currently, Data Prep only supports __Ubuntu 16__ and __Red Hat Enterprise Linux 7__. We are working on supporting more linux distros."
       ]
@@ -37,8 +53,6 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Diagnostics\n",
-    "\n",
         "Opt-in diagnostics for better experience, quality, and security of future releases."
       ]
     },
@@ -56,8 +70,6 @@
       "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."
       ]
     },
@@ -68,7 +80,6 @@
       "outputs": [],
       "source": [
         "import logging\n",
-    "import os\n",
         "\n",
         "import pandas as pd\n",
         "\n",
@@ -103,14 +114,15 @@
         "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"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Loading Data using DataPrep"
+        "## Data"
       ]
     },
     {
@@ -134,7 +146,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Review the Data Preparation Result\n",
+        "### Review the Data Preparation Result\n",
         "\n",
         "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets."
       ]
@@ -152,7 +164,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Configure AutoML\n",
+        "## Train\n",
         "\n",
         "This creates a general AutoML settings object applicable for both local and remote runs."
       ]
@@ -173,13 +185,6 @@
         "}"
       ]
     },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Local Run"
-   ]
-  },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -211,11 +216,20 @@
         "local_run = experiment.submit(automl_config, show_output = True)"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "local_run"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Explore the Results"
+        "## Results"
       ]
     },
     {
@@ -260,7 +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"
       ]
@@ -329,7 +342,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "### Test the Best Fitted Model\n",
+        "## Test\n",
         "\n",
         "#### Load Test Data"
       ]
@@ -364,8 +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",
@@ -419,7 +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

@@ -13,24 +13,38 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "# Automated Machine Learning: Exploring Previous Runs\n",
+        "# Automated Machine Learning\n",
+        "_**Exploring Previous Runs**_\n",
         "\n",
-    "In this example we present some examples on navigating previously executed runs. We also show how you can download a fitted model for any previous run.\n",
-    "\n",
-    "Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
-    "\n",
-    "In this notebook you will learn how to:\n",
-    "1. List all experiments in a workspace.\n",
-    "2. List all AutoML runs in an experiment.\n",
-    "3. Get details for an AutoML run, including settings, run widget, and all metrics.\n",
-    "4. Download a fitted pipeline for any iteration.\n"
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Explore](#Explore)\n",
+        "1. [Download](#Download)\n",
+        "1. [Register](#Register)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "# List all AutoML Experiments in a Workspace"
+        "## Introduction\n",
+        "In this example we present some examples on navigating previously executed runs. We also show how you can download a fitted model for any previous run.\n",
+        "\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
+        "\n",
+        "In this notebook you will learn how to:\n",
+        "1. List all experiments in a workspace.\n",
+        "2. List all AutoML runs in an experiment.\n",
+        "3. Get details for an AutoML run, including settings, run widget, and all metrics.\n",
+        "4. Download a fitted pipeline for any iteration."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup"
       ]
     },
     {
@@ -39,22 +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"
       ]
     },
@@ -64,29 +67,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "ws = Workspace.from_config()\n",
-    "experiment_list = Experiment.list(workspace=ws)\n",
-    "\n",
-    "summary_df = pd.DataFrame(index = ['No of Runs'])\n",
-    "pattern = re.compile('^AutoML_[^_]*$')\n",
-    "for experiment in experiment_list:\n",
-    "    all_runs = list(experiment.get_runs())\n",
-    "    automl_runs = []\n",
-    "    for run in all_runs:\n",
-    "        if(pattern.match(run.id)):\n",
-    "            automl_runs.append(run)    \n",
-    "    summary_df[experiment.name] = [len(automl_runs)]\n",
-    "    \n",
-    "pd.set_option('display.max_colwidth', -1)\n",
-    "summary_df.T"
+        "ws = Workspace.from_config()"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Diagnostics\n",
-    "\n",
         "Opt-in diagnostics for better experience, quality, and security of future releases."
       ]
     },
@@ -104,7 +91,38 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "# List AutoML runs for an experiment\n",
+        "## Explore"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### List Experiments"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "experiment_list = Experiment.list(workspace=ws)\n",
+        "\n",
+        "summary_df = pd.DataFrame(index = ['No of Runs'])\n",
+        "for experiment in experiment_list:\n",
+        "    automl_runs = list(experiment.get_runs(type='automl'))\n",
+        "    summary_df[experiment.name] = [len(automl_runs)]\n",
+        "    \n",
+        "pd.set_option('display.max_colwidth', -1)\n",
+        "summary_df.T"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### List runs for an experiment\n",
         "Set `experiment_name` to any experiment name from the result of the Experiment.list cell to load the AutoML runs."
       ]
     },
@@ -118,14 +136,12 @@
         "\n",
         "proj = ws.experiments[experiment_name]\n",
         "summary_df = pd.DataFrame(index = ['Type', 'Status', 'Primary Metric', 'Iterations', 'Compute', 'Name'])\n",
-    "pattern = re.compile('^AutoML_[^_]*$')\n",
-    "all_runs = list(proj.get_runs(properties={'azureml.runsource': 'automl'}))\n",
+        "automl_runs = list(proj.get_runs(type='automl'))\n",
         "automl_runs_project = []\n",
-    "for run in all_runs:\n",
-    "    if(pattern.match(run.id)):\n",
+        "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",
@@ -146,7 +162,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "# Get details for an AutoML run\n",
+        "### Get details for a run\n",
         "\n",
         "Copy the project name and run id from the previous cell output to find more details on a particular run."
       ]
@@ -169,7 +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",
@@ -207,14 +223,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "# Download fitted models"
+        "## Download"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Download the Best Model for Any Given Metric"
+        "### Download the Best Model for Any Given Metric"
       ]
     },
     {
@@ -232,7 +248,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Download the Model for Any Given Iteration"
+        "### Download the Model for Any Given Iteration"
       ]
     },
     {
@@ -250,7 +266,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "# Register fitted model for deployment\n",
+        "## Register"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Register fitted model for deployment\n",
         "If neither `metric` nor `iteration` are specified in the `register_model` call, the iteration with the best primary metric is registered."
       ]
     },
@@ -263,14 +286,14 @@
         "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."
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Register the Best Model for Any Given Metric"
+        "### Register the Best Model for Any Given Metric"
       ]
     },
     {
@@ -290,7 +313,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Register the Model for Any Given Iteration"
+        "### Register the Model for Any Given Iteration"
       ]
     },
     {

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

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

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

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

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

@@ -0,0 +1,405 @@
+{
+  "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\n",
+        "_**Energy Demand Forecasting**_\n",
+        "\n",
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Data](#Data)\n",
+        "1. [Train](#Train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "In this example, we show how AutoML can be used for energy demand forecasting.\n",
+        "\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
+        "\n",
+        "In this notebook you would see\n",
+        "1. Creating an Experiment in an existing Workspace\n",
+        "2. Instantiating AutoMLConfig with new task type \"forecasting\" for timeseries data training, and other timeseries related settings: for this dataset we use the basic one: \"time_column_name\" \n",
+        "3. Training the Model using local compute\n",
+        "4. Exploring the results\n",
+        "5. Testing the fitted model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 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\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import azureml.core\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import logging\n",
+        "import warnings\n",
+        "# Squash warning messages for cleaner output in the notebook\n",
+        "warnings.showwarning = lambda *args, **kwargs: None\n",
+        "\n",
+        "\n",
+        "from azureml.core.workspace import Workspace\n",
+        "from azureml.core.experiment import Experiment\n",
+        "from azureml.train.automl import AutoMLConfig\n",
+        "from matplotlib import pyplot as plt\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,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "\n",
+        "# choose a name for the run history container in the workspace\n",
+        "experiment_name = 'automl-energydemandforecasting'\n",
+        "# project folder\n",
+        "project_folder = './sample_projects/automl-local-energydemandforecasting'\n",
+        "\n",
+        "experiment = Experiment(ws, experiment_name)\n",
+        "\n",
+        "output = {}\n",
+        "output['SDK version'] = azureml.core.VERSION\n",
+        "output['Subscription ID'] = ws.subscription_id\n",
+        "output['Workspace'] = ws.name\n",
+        "output['Resource Group'] = ws.resource_group\n",
+        "output['Location'] = ws.location\n",
+        "output['Project Directory'] = project_folder\n",
+        "output['Run History Name'] = experiment_name\n",
+        "pd.set_option('display.max_colwidth', -1)\n",
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Data\n",
+        "Read energy demanding data from file, and preview data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "data = pd.read_csv(\"nyc_energy.csv\", parse_dates=['timeStamp'])\n",
+        "data.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Split the data to train and test\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "train = data[data['timeStamp'] < '2017-02-01']\n",
+        "test = data[data['timeStamp'] >= '2017-02-01']\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Prepare the test data, we will feed X_test to the fitted model and get prediction"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "y_test = test.pop('demand').values\n",
+        "X_test = test"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Split the train data to train and valid\n",
+        "\n",
+        "Use one month's data as valid data\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_train = train[train['timeStamp'] < '2017-01-01']\n",
+        "X_valid = train[train['timeStamp'] >= '2017-01-01']\n",
+        "y_train = X_train.pop('demand').values\n",
+        "y_valid = X_valid.pop('demand').values\n",
+        "print(X_train.shape)\n",
+        "print(y_train.shape)\n",
+        "print(X_valid.shape)\n",
+        "print(y_valid.shape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train\n",
+        "\n",
+        "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
+        "\n",
+        "|Property|Description|\n",
+        "|-|-|\n",
+        "|**task**|forecasting|\n",
+        "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
+        "|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
+        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
+        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+        "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
+        "|**X_valid**|Data used to evaluate a model in a iteration. (sparse) array-like, shape = [n_samples, n_features]|\n",
+        "|**y_valid**|Data used to evaluate a model in a iteration. (sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
+        "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "time_column_name = 'timeStamp'\n",
+        "automl_settings = {\n",
+        "    \"time_column_name\": time_column_name,\n",
+        "}\n",
+        "\n",
+        "\n",
+        "automl_config = AutoMLConfig(task = 'forecasting',\n",
+        "                             debug_log = 'automl_nyc_energy_errors.log',\n",
+        "                             primary_metric='normalized_root_mean_squared_error',\n",
+        "                             iterations = 10,\n",
+        "                             iteration_timeout_minutes = 5,\n",
+        "                             X = X_train,\n",
+        "                             y = y_train,\n",
+        "                             X_valid = X_valid,\n",
+        "                             y_valid = y_valid,\n",
+        "                             path=project_folder,\n",
+        "                             verbosity = logging.INFO,\n",
+        "                            **automl_settings)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
+        "You will see the currently running iterations printing to the console."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "local_run = experiment.submit(automl_config, show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "local_run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Retrieve the Best Model\n",
+        "Below we select the best pipeline from our iterations. The get_output method on automl_classifier returns the best run and the fitted model for the last fit invocation. There are overloads on get_output that allow you to retrieve the best run and fitted model for any logged metric or a particular iteration."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "best_run, fitted_model = local_run.get_output()\n",
+        "fitted_model.steps"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Test the Best Fitted Model\n",
+        "\n",
+        "Predict on training and test set, and calculate residual values."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "y_pred = fitted_model.predict(X_test)\n",
+        "y_pred"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Use the Check Data Function to remove the nan values from y_test to avoid error when calculate metrics "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "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))]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Calculate metrics for the prediction\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
+        "# Explained variance score: 1 is perfect prediction\n",
+        "print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
+        "print('R2 score: %.2f' % r2_score(y_test, y_pred))\n",
+        "\n",
+        "\n",
+        "\n",
+        "# Plot outputs\n",
+        "%matplotlib notebook\n",
+        "test_pred = plt.scatter(y_test, y_pred, color='b')\n",
+        "test_test = plt.scatter(y_test, y_test, color='g')\n",
+        "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
+        "plt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "xiaga"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

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

@@ -0,0 +1,441 @@
+{
+  "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\n",
+        "_**Orange Juice Sales Forecasting**_\n",
+        "\n",
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Data](#Data)\n",
+        "1. [Train](#Train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "In this example, we use AutoML to find and tune a time-series forecasting model.\n",
+        "\n",
+        "Make sure you have executed the [configuration notebook](../../../configuration.ipynb) before running this notebook.\n",
+        "\n",
+        "In this notebook, you will:\n",
+        "1. Create an Experiment in an existing Workspace\n",
+        "2. Instantiate an AutoMLConfig \n",
+        "3. Find and train a forecasting model using local compute\n",
+        "4. Evaluate the performance of the model\n",
+        "\n",
+        "The examples in the follow code samples use the [University of Chicago's Dominick's Finer Foods dataset](https://research.chicagobooth.edu/kilts/marketing-databases/dominicks) to forecast orange juice sales. Dominick's was a grocery chain in the Chicago metropolitan area."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 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\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import azureml.core\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import logging\n",
+        "import warnings\n",
+        "# Squash warning messages for cleaner output in the notebook\n",
+        "warnings.showwarning = lambda *args, **kwargs: None\n",
+        "\n",
+        "\n",
+        "from azureml.core.workspace import Workspace\n",
+        "from azureml.core.experiment import Experiment\n",
+        "from azureml.train.automl import AutoMLConfig\n",
+        "from 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,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ws = Workspace.from_config()\n",
+        "\n",
+        "# choose a name for the run history container in the workspace\n",
+        "experiment_name = 'automl-ojsalesforecasting'\n",
+        "# project folder\n",
+        "project_folder = './sample_projects/automl-local-ojsalesforecasting'\n",
+        "\n",
+        "experiment = Experiment(ws, experiment_name)\n",
+        "\n",
+        "output = {}\n",
+        "output['SDK version'] = azureml.core.VERSION\n",
+        "output['Subscription ID'] = ws.subscription_id\n",
+        "output['Workspace'] = ws.name\n",
+        "output['Resource Group'] = ws.resource_group\n",
+        "output['Location'] = ws.location\n",
+        "output['Project Directory'] = project_folder\n",
+        "output['Run History Name'] = experiment_name\n",
+        "pd.set_option('display.max_colwidth', -1)\n",
+        "outputDf = pd.DataFrame(data = output, index = [''])\n",
+        "outputDf.T"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Data\n",
+        "You are now ready to load the historical orange juice sales data. We will load the CSV file into a plain pandas DataFrame; the time column in the CSV is called _WeekStarting_, so it will be specially parsed into the datetime type."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "time_column_name = 'WeekStarting'\n",
+        "data = pd.read_csv(\"dominicks_OJ.csv\", parse_dates=[time_column_name])\n",
+        "data.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Each row in the DataFrame holds a quantity of weekly sales for an OJ brand at a single store. The data also includes the sales price, a flag indicating if the OJ brand was advertised in the store that week, and some customer demographic information based on the store location. For historical reasons, the data also include the logarithm of the sales quantity. The Dominick's grocery data is commonly used to illustrate econometric modeling techniques where logarithms of quantities are generally preferred.    \n",
+        "\n",
+        "The task is now to build a time-series model for the _Quantity_ column. It is important to note that this dataset is comprised of many individual time-series - one for each unique combination of _Store_ and _Brand_. To distinguish the individual time-series, we thus define the **grain** - the columns whose values determine the boundaries between time-series: "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "grain_column_names = ['Store', 'Brand']\n",
+        "nseries = data.groupby(grain_column_names).ngroups\n",
+        "print('Data contains {0} individual time-series.'.format(nseries))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Data Splitting\n",
+        "For the purposes of demonstration and later forecast evaluation, we now split the data into a training and a testing set. The test set will contain the final 20 weeks of observed sales for each time-series."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "ntest_periods = 20\n",
+        "\n",
+        "def split_last_n_by_grain(df, n):\n",
+        "    \"\"\"\n",
+        "    Group df by grain and split on last n rows for each group\n",
+        "    \"\"\"\n",
+        "    df_grouped = (df.sort_values(time_column_name) # Sort by ascending time\n",
+        "                  .groupby(grain_column_names, group_keys=False))\n",
+        "    df_head = df_grouped.apply(lambda dfg: dfg.iloc[:-n])\n",
+        "    df_tail = df_grouped.apply(lambda dfg: dfg.iloc[-n:])\n",
+        "    return df_head, df_tail\n",
+        "\n",
+        "X_train, X_test = split_last_n_by_grain(data, ntest_periods)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Modeling\n",
+        "\n",
+        "For forecasting tasks, AutoML uses pre-processing and estimation steps that are specific to time-series. AutoML will undertake the following pre-processing steps:\n",
+        "* Detect time-series sample frequency (e.g. hourly, daily, weekly) and create new records for absent time points to make the series regular. A regular time series has a well-defined frequency and has a value at every sample point in a contiguous time span \n",
+        "* Impute missing values in the target (via forward-fill) and feature columns (using median column values) \n",
+        "* Create grain-based features to enable fixed effects across different series\n",
+        "* Create time-based features to assist in learning seasonal patterns\n",
+        "* Encode categorical variables to numeric quantities\n",
+        "\n",
+        "AutoML will currently train a single, regression-type model across **all** time-series in a given training set. This allows the model to generalize across related series.\n",
+        "\n",
+        "You are almost ready to start an AutoML training job. We will first need to create a validation set from the existing training set (i.e. for hyper-parameter tuning): "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "nvalidation_periods = 20\n",
+        "X_train, X_validate = split_last_n_by_grain(X_train, nvalidation_periods)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We also need to separate the target column from the rest of the DataFrame: "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "target_column_name = 'Quantity'\n",
+        "y_train = X_train.pop(target_column_name).values\n",
+        "y_validate = X_validate.pop(target_column_name).values "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train\n",
+        "\n",
+        "The AutoMLConfig object defines the settings and data for an AutoML training job. Here, we set necessary inputs like the task type, the number of AutoML iterations to try, and the training and validation data. \n",
+        "\n",
+        "For forecasting tasks, there are some additional parameters that can be set: the name of the column holding the date/time and the grain column names. A time column is required for forecasting, while the grain is optional. If a grain is not given, the forecaster assumes that the whole dataset is a single time-series. We also pass a list of columns to drop prior to modeling. The _logQuantity_ column is completely correlated with the target quantity, so it must be removed to prevent a target leak. \n",
+        "\n",
+        "|Property|Description|\n",
+        "|-|-|\n",
+        "|**task**|forecasting|\n",
+        "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
+        "|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
+        "|**X**|Training matrix of features, shape = [n_training_samples, n_features]|\n",
+        "|**y**|Target values, shape = [n_training_samples, ]|\n",
+        "|**X_valid**|Validation matrix of features, shape = [n_validation_samples, n_features]|\n",
+        "|**y_valid**|Target values for validation, shape = [n_validation_samples, ]\n",
+        "|**enable_ensembling**|Allow AutoML to create ensembles of the best performing models\n",
+        "|**debug_log**|Log file path for writing debugging information\n",
+        "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "automl_settings = {\n",
+        "    'time_column_name': time_column_name,\n",
+        "    'grain_column_names': grain_column_names,\n",
+        "    'drop_column_names': ['logQuantity']\n",
+        "}\n",
+        "\n",
+        "automl_config = AutoMLConfig(task='forecasting',\n",
+        "                             debug_log='automl_oj_sales_errors.log',\n",
+        "                             primary_metric='normalized_root_mean_squared_error',\n",
+        "                             iterations=10,\n",
+        "                             X=X_train,\n",
+        "                             y=y_train,\n",
+        "                             X_valid=X_validate,\n",
+        "                             y_valid=y_validate,\n",
+        "                             enable_ensembling=False,\n",
+        "                             path=project_folder,\n",
+        "                             verbosity=logging.INFO,\n",
+        "                            **automl_settings)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can now submit a new training run. For local runs, the execution is synchronous. Depending on the data and number of iterations this operation may take several minutes.\n",
+        "Information from each iteration will be printed to the console."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "local_run = experiment.submit(automl_config, show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "local_run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Retrieve the Best Model\n",
+        "Each run within an Experiment stores serialized (i.e. pickled) pipelines from the AutoML iterations. We can now retrieve the pipeline with the best performance on the validation dataset:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "best_run, fitted_pipeline = local_run.get_output()\n",
+        "fitted_pipeline.steps"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Make Predictions from the Best Fitted Model\n",
+        "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. First, we remove the target values from the test set:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "y_test = X_test.pop(target_column_name).values"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_test.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To produce predictions on the test set, we need to know the feature values at all dates in the test set. This requirement is somewhat reasonable for the OJ sales data since the features mainly consist of price, which is usually set in advance, and customer demographics which are approximately constant for each store over the 20 week forecast horizon in the testing data. \n",
+        "\n",
+        "The target predictions can be retrieved by calling the `predict` method on the best model:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "y_pred = fitted_pipeline.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Calculate evaluation metrics for the prediction\n",
+        "To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def MAPE(actual, pred):\n",
+        "    \"\"\"\n",
+        "    Calculate mean absolute percentage error.\n",
+        "    Remove NA and values where actual is close to zero\n",
+        "    \"\"\"\n",
+        "    not_na = ~(np.isnan(actual) | np.isnan(pred))\n",
+        "    not_zero = ~np.isclose(actual, 0.0)\n",
+        "    actual_safe = actual[not_na & not_zero]\n",
+        "    pred_safe = pred[not_na & not_zero]\n",
+        "    APE = 100*np.abs((actual_safe - pred_safe)/actual_safe)\n",
+        "    return np.mean(APE)\n",
+        "\n",
+        "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
+        "print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
+        "print('MAPE: %.2f' % MAPE(y_test, y_pred))"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "erwright"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

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

@@ -13,11 +13,26 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "# Automated Machine Learning: Blacklisting Models, Early Termination, and Handling Missing Data\n",
+        "# Automated Machine Learning\n",
+        "_**Blacklisting Models, Early Termination, and Handling Missing Data**_\n",
         "\n",
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Data](#Data)\n",
+        "1. [Train](#Train)\n",
+        "1. [Results](#Results)\n",
+        "1. [Test](#Test)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
         "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for handling missing values in data. We also provide a stopping metric indicating a target for the primary metrics so that AutoML can terminate the run without necessarly going through all the iterations. Finally, if you want to avoid a certain pipeline, we allow you to specify a blacklist of algorithms that AutoML will ignore for this run.\n",
         "\n",
-    "Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
         "\n",
         "In this notebook you will learn how to:\n",
         "1. Create an `Experiment` in an existing `Workspace`.\n",
@@ -29,14 +44,14 @@
         "In addition this notebook showcases the following features\n",
         "- **Blacklisting** certain pipelines\n",
         "- Specifying **target metrics** to indicate stopping criteria\n",
-    "- Handling **missing data** in the input\n"
+        "- Handling **missing data** in the input"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Create an Experiment\n",
+        "## Setup\n",
         "\n",
         "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
       ]
@@ -48,11 +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",
@@ -60,8 +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"
       ]
     },
     {
@@ -87,15 +98,14 @@
         "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"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Diagnostics\n",
-    "\n",
         "Opt-in diagnostics for better experience, quality, and security of future releases."
       ]
     },
@@ -113,7 +123,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "### Creating missing data"
+        "## Data"
       ]
     },
     {
@@ -122,8 +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",
@@ -153,7 +161,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Configure AutoML\n",
+        "## Train\n",
         "\n",
         "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.  This includes setting `experiment_exit_score`, which should cause the run to complete before the `iterations` count is reached.\n",
         "\n",
@@ -197,8 +205,6 @@
       "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."
       ]
@@ -212,11 +218,20 @@
         "local_run = experiment.submit(automl_config, show_output = True)"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "local_run"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Explore the Results"
+        "## Results"
       ]
     },
     {
@@ -324,7 +339,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "### Testing the best Fitted Model"
+        "## Test"
       ]
     },
     {

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

@@ -13,25 +13,39 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "# Automated Machine Learning: Explain classification model and visualize the explanation\n",
+        "# Automated Machine Learning\n",
+        "_**Explain classification model and visualize the explanation**_\n",
         "\n",
-    "In this example we use the sklearn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) to showcase how you can use the AutoML Classifier for a simple classification problem.\n",
-    "\n",
-    "Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
-    "\n",
-    "In this notebook you would see\n",
-    "1. Creating an Experiment in an existing Workspace\n",
-    "2. Instantiating AutoMLConfig\n",
-    "3. Training the Model using local compute and explain the model\n",
-    "4. Visualization model's feature importance in widget\n",
-    "5. Explore best model's explanation\n"
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Data](#Data)\n",
+        "1. [Train](#Train)\n",
+        "1. [Results](#Results)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Create Experiment\n",
+        "## Introduction\n",
+        "In this example we use the sklearn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) to showcase how you can use the AutoML Classifier for a simple classification problem.\n",
+        "\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
+        "\n",
+        "In this notebook you would see\n",
+        "1. Creating an Experiment in an existing Workspace\n",
+        "2. Instantiating AutoMLConfig\n",
+        "3. Training the Model using local compute and explain the model\n",
+        "4. Visualization model's feature importance in widget\n",
+        "5. Explore best model's explanation"
+      ]
+    },
+    {
+      "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."
       ]
@@ -43,15 +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"
       ]
     },
     {
@@ -78,15 +89,14 @@
         "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"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Diagnostics\n",
-    "\n",
         "Opt-in diagnostics for better experience, quality, and security of future releases"
       ]
     },
@@ -104,7 +114,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Load Iris Data Set"
+        "## Data"
       ]
     },
     {
@@ -136,7 +146,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Instantiate Auto ML Config\n",
+        "## Train\n",
         "\n",
         "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
         "\n",
@@ -178,8 +188,6 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Training the Model\n",
-    "\n",
         "You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
         "You will see the currently running iterations printing to the console."
       ]
@@ -193,11 +201,20 @@
         "local_run = experiment.submit(automl_config, show_output=True)"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "local_run"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Exploring the results"
+        "## Results"
       ]
     },
     {

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

@@ -13,25 +13,40 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "# AutoML: Regression with Local Compute\n",
+        "# Automated Machine Learning\n",
+        "_**Regression with Local Compute**_\n",
         "\n",
-    "In this example we use the scikit-learn's [diabetes dataset](http://scikit-learn.org/stable/datasets/index.html#diabetes-dataset) to showcase how you can use AutoML for a simple regression problem.\n",
-    "\n",
-    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
-    "\n",
-    "In this notebook you will learn how to:\n",
-    "1. Create an `Experiment` in an existing `Workspace`.\n",
-    "2. Configure AutoML using `AutoMLConfig`.\n",
-    "3. Train the model using local compute.\n",
-    "4. Explore the results.\n",
-    "5. Test the best fitted model.\n"
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Data](#Data)\n",
+        "1. [Train](#Train)\n",
+        "1. [Results](#Results)\n",
+        "1. [Test](#Test)\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Create an Experiment\n",
+        "## Introduction\n",
+        "In this example we use the scikit-learn's [diabetes dataset](http://scikit-learn.org/stable/datasets/index.html#diabetes-dataset) to showcase how you can use AutoML for a simple regression problem.\n",
+        "\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
+        "\n",
+        "In this notebook you will learn how to:\n",
+        "1. Create an `Experiment` in an existing `Workspace`.\n",
+        "2. Configure AutoML using `AutoMLConfig`.\n",
+        "3. Train the model using local compute.\n",
+        "4. Explore the results.\n",
+        "5. Test the best fitted model."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup\n",
         "\n",
         "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
       ]
@@ -43,20 +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"
       ]
     },
     {
@@ -82,15 +92,14 @@
         "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"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Diagnostics\n",
-    "\n",
         "Opt-in diagnostics for better experience, quality, and security of future releases."
       ]
     },
@@ -108,7 +117,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "### Load Training Data\n",
+        "## Data\n",
         "This uses scikit-learn's [load_diabetes](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html) method."
       ]
     },
@@ -120,8 +129,6 @@
       "source": [
         "# Load the diabetes dataset, a well-known built-in small dataset that comes with scikit-learn.\n",
         "from sklearn.datasets import load_diabetes\n",
-    "from sklearn.linear_model import Ridge\n",
-    "from sklearn.metrics import mean_squared_error\n",
         "from sklearn.model_selection import train_test_split\n",
         "\n",
         "X, y = load_diabetes(return_X_y = True)\n",
@@ -135,7 +142,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Configure AutoML\n",
+        "## Train\n",
         "\n",
         "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
         "\n",
@@ -173,8 +180,6 @@
       "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."
       ]
@@ -201,7 +206,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Explore the Results"
+        "## Results"
       ]
     },
     {
@@ -315,7 +320,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "### Test the Best Fitted Model"
+        "## Test"
       ]
     },
     {
@@ -345,9 +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",
@@ -366,8 +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

@@ -13,11 +13,26 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "# Automated Machine Learning: Remote Execution using attach\n",
+        "# Automated Machine Learning\n",
+        "_**Remote Execution using attach**_\n",
         "\n",
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Data](#Data)\n",
+        "1. [Train](#Train)\n",
+        "1. [Results](#Results)\n",
+        "1. [Test](#Test)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
         "In this example we use the scikit-learn's [20newsgroup](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_20newsgroups.html) to showcase how you can use AutoML to handle text data with remote attach.\n",
         "\n",
-    "Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
         "\n",
         "In this notebook you will learn how to:\n",
         "1. Create an `Experiment` in an existing `Workspace`.\n",
@@ -33,14 +48,14 @@
         "- **Cancellation** of individual iterations or the entire run\n",
         "- Retrieving models for any iteration or logged metric\n",
         "- Specifying AutoML settings as `**kwargs`\n",
-    "- Handling **text** data using the `preprocess` flag\n"
+        "- Handling **text** data using the `preprocess` flag"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Create an Experiment\n",
+        "## Setup\n",
         "\n",
         "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
       ]
@@ -51,21 +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"
       ]
     },
     {
@@ -77,8 +86,8 @@
         "ws = Workspace.from_config()\n",
         "\n",
         "# Choose a name for the run history container in the workspace.\n",
-    "experiment_name = 'automl-remote-dsvm-blobstore'\n",
-    "project_folder = './sample_projects/automl-remote-dsvm-blobstore'\n",
+        "experiment_name = 'automl-remote-attach'\n",
+        "project_folder = './sample_projects/automl-remote-attach'\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
@@ -91,15 +100,14 @@
         "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"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Diagnostics\n",
-    "\n",
         "Opt-in diagnostics for better experience, quality, and security of future releases."
       ]
     },
@@ -117,7 +125,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Attach a Remote Linux DSVM\n",
+        "### Attach a Remote Linux DSVM\n",
         "To use a remote Docker compute target:\n",
         "1. Create a Linux DSVM in Azure, following these [quick instructions](https://docs.microsoft.com/en-us/azure/machine-learning/desktop-workbench/how-to-create-dsvm-hdi). Make sure you use the Ubuntu flavor (not CentOS). Make sure that disk space is available under `/tmp` because AutoML creates files under `/tmp/azureml_run`s. The DSVM should have more cores than the number of parallel runs that you plan to enable. It should also have at least 4GB per core.\n",
         "2. Enter the IP address, user name and password below.\n",
@@ -184,7 +192,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Create Get Data File\n",
+        "## Data\n",
         "For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
         "In this example, the `get_data()` function returns a [dictionary](README.md#getdata)."
       ]
@@ -232,7 +240,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Configure AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
+        "## Train\n",
         "\n",
         "You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
         "\n",
@@ -277,8 +285,6 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Train the Models <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
-    "\n",
         "Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run."
       ]
     },
@@ -291,11 +297,20 @@
         "remote_run = experiment.submit(automl_config)"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
+        "## Results\n",
         "#### Widget for Monitoring Runs\n",
         "\n",
         "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
@@ -329,7 +344,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Pre-process cache cleanup\n",
+        "### Pre-process cache cleanup\n",
         "The preprocess data gets cache at user default file store. When the run is completed the cache can be cleaned by running below cell"
       ]
     },
@@ -372,7 +387,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Cancelling Runs\n",
+        "### Cancelling Runs\n",
         "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
       ]
     },
@@ -448,7 +463,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
+        "## Test"
       ]
     },
     {

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

@@ -13,17 +13,32 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "# Automated Machine Learning: Remote Execution using Batch AI\n",
+        "# Automated Machine Learning\n",
+        "_**Remote Execution using AmlCompute**_\n",
         "\n",
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Data](#Data)\n",
+        "1. [Train](#Train)\n",
+        "1. [Results](#Results)\n",
+        "1. [Test](#Test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
         "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
         "\n",
-    "Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
         "\n",
         "In this notebook you would see\n",
         "1. Create an `Experiment` in an existing `Workspace`.\n",
-    "2. Attach an existing Batch AI compute to a workspace.\n",
+        "2. Create or Attach existing AmlCompute to a workspace.\n",
         "3. Configure AutoML using `AutoMLConfig`.\n",
-    "4. Train the model using Batch AI.\n",
+        "4. Train the model using AmlCompute\n",
         "5. Explore the results.\n",
         "6. Test the best fitted model.\n",
         "\n",
@@ -32,14 +47,14 @@
         "- **Asynchronous** tracking of progress\n",
         "- **Cancellation** of individual iterations or the entire run\n",
         "- Retrieving models for any iteration or logged metric\n",
-    "- Specifying AutoML settings as `**kwargs`\n"
+        "- Specifying AutoML settings as `**kwargs`"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Create an Experiment\n",
+        "## Setup\n",
         "\n",
         "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
       ]
@@ -52,10 +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",
@@ -63,8 +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"
       ]
     },
     {
@@ -76,8 +88,8 @@
         "ws = Workspace.from_config()\n",
         "\n",
         "# Choose a name for the run history container in the workspace.\n",
-    "experiment_name = 'automl-remote-batchai'\n",
-    "project_folder = './sample_projects/automl-remote-batchai'\n",
+        "experiment_name = 'automl-remote-amlcompute'\n",
+        "project_folder = './sample_projects/automl-remote-amlcompute'\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
@@ -90,15 +102,14 @@
         "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"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Diagnostics\n",
-    "\n",
         "Opt-in diagnostics for better experience, quality, and security of future releases."
       ]
     },
@@ -116,12 +127,12 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Create Batch AI Cluster\n",
-    "The cluster is created as Machine Learning Compute and will appear under your workspace.\n",
+        "### 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 your AutoML run. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
         "\n",
-    "**Note:** The creation of the Batch AI cluster can take over 10 minutes, please be patient.\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. Batch AI cluster size) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
+        "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."
       ]
     },
     {
@@ -134,15 +145,15 @@
         "from azureml.core.compute import ComputeTarget\n",
         "\n",
         "# Choose a name for your cluster.\n",
-    "batchai_cluster_name = \"automlcl\"\n",
+        "amlcompute_cluster_name = \"automlcl\"\n",
         "\n",
         "found = False\n",
         "# Check if this compute target already exists in the workspace.\n",
         "cts = ws.compute_targets\n",
-    "if batchai_cluster_name in cts and cts[batchai_cluster_name].type == 'BatchAI':\n",
+        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
         "    found = True\n",
         "    print('Found existing compute target.')\n",
-    "    compute_target = cts[batchai_cluster_name]\n",
+        "    compute_target = cts[amlcompute_cluster_name]\n",
         "    \n",
         "if not found:\n",
         "    print('Creating a new compute target...')\n",
@@ -151,13 +162,13 @@
         "                                                                max_nodes = 6)\n",
         "\n",
         "    # Create the cluster.\n",
-    "    compute_target = ComputeTarget.create(ws, batchai_cluster_name, provisioning_config)\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
         "    \n",
         "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
         "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
         "    compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
         "    \n",
-    "     # For a more detailed view of current Batch AI cluster status, use the 'status' property."
+        "     # For a more detailed view of current AmlCompute status, use get_status()."
       ]
     },
     {
@@ -172,7 +183,7 @@
         "# create a new RunConfig object\n",
         "conda_run_config = RunConfiguration(framework=\"python\")\n",
         "\n",
-    "# Set compute target to the Batch AI cluster\n",
+        "# Set compute target to AmlCompute\n",
         "conda_run_config.target = compute_target\n",
         "conda_run_config.environment.docker.enabled = True\n",
         "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
@@ -185,7 +196,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Create Get Data File\n",
+        "## Data\n",
         "For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
         "In this example, the `get_data()` function returns data using scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
       ]
@@ -225,11 +236,11 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
+        "## Train\n",
         "\n",
         "You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
         "\n",
-    "**Note:** When using Batch AI, you can't pass Numpy arrays directly to the fit method.\n",
+        "**Note:** When using AmlCompute, you can't pass Numpy arrays directly to the fit method.\n",
         "\n",
         "|Property|Description|\n",
         "|-|-|\n",
@@ -269,8 +280,6 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Train the Models\n",
-    "\n",
         "Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run.\n",
         "In this example, we specify `show_output = False` to suppress console output while the run is in progress."
       ]
@@ -284,11 +293,20 @@
         "remote_run = experiment.submit(automl_config, show_output = False)"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Explore the Results\n",
+        "## Results\n",
         "\n",
         "#### Loading executed runs\n",
         "In case you need to load a previously executed run, enable the cell below and replace the `run_id` value."
@@ -373,7 +391,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Cancelling Runs\n",
+        "### Cancelling Runs\n",
         "\n",
         "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
       ]
@@ -455,7 +473,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n",
+        "## Test\n",
         "\n",
         "#### Load Test Data"
       ]

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

@@ -13,26 +13,40 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "# Automated Machine Learning: Remote Execution with DataStore\n",
+        "# Automated Machine Learning\n",
+        "_**Remote Execution with DataStore**_\n",
         "\n",
-    "This sample accesses a data file on a remote DSVM through DataStore. Advantages of using data store are:\n",
-    "1. DataStore secures the access details.\n",
-    "2. DataStore supports read, write to blob and file store\n",
-    "3. AutoML natively supports copying data from DataStore to DSVM\n",
-    "\n",
-    "Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
-    "\n",
-    "In this notebook you would see\n",
-    "1. Storing data in DataStore.\n",
-    "2. get_data returning data from DataStore.\n",
-    "\n"
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Data](#Data)\n",
+        "1. [Train](#Train)\n",
+        "1. [Results](#Results)\n",
+        "1. [Test](#Test)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Create Experiment\n",
+        "## Introduction\n",
+        "This sample accesses a data file on a remote DSVM through DataStore. Advantages of using data store are:\n",
+        "1. DataStore secures the access details.\n",
+        "2. DataStore supports read, write to blob and file store\n",
+        "3. AutoML natively supports copying data from DataStore to DSVM\n",
+        "\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
+        "\n",
+        "In this notebook you would see\n",
+        "1. Storing data in DataStore.\n",
+        "2. get_data returning data from DataStore."
+      ]
+    },
+    {
+      "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."
       ]
@@ -45,21 +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"
       ]
     },
     {
@@ -73,7 +82,7 @@
         "# choose a name for experiment\n",
         "experiment_name = 'automl-remote-datastore-file'\n",
         "# project folder\n",
-    "project_folder = './sample_projects/automl-remote-dsvm-file'\n",
+        "project_folder = './sample_projects/automl-remote-datastore-file'\n",
         "\n",
         "experiment=Experiment(ws, experiment_name)\n",
         "\n",
@@ -86,15 +95,14 @@
         "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"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Diagnostics\n",
-    "\n",
         "Opt-in diagnostics for better experience, quality, and security of future releases"
       ]
     },
@@ -112,7 +120,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Create a Remote Linux DSVM\n",
+        "### Create a Remote Linux DSVM\n",
         "Note: If creation fails with a message about Marketplace purchase eligibilty, go to portal.azure.com, start creating DSVM there, and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled it, you can exit without actually creating VM.\n",
         "\n",
         "**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you can switch to a different port (such as 5022), you can append the port number to the address. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on this."
@@ -144,7 +152,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Copy data file to local\n",
+        "## Data\n",
+        "\n",
+        "### Copy data file to local\n",
         "\n",
         "Download the data file.\n"
       ]
@@ -155,7 +165,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "mkdir data"
+        "if not os.path.isdir('data'):\n",
+        "    os.mkdir('data')    "
       ]
     },
     {
@@ -186,7 +197,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Upload data to the cloud"
+        "### Upload data to the cloud"
       ]
     },
     {
@@ -204,7 +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)"
@@ -224,7 +234,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Configure & Run\n",
+        "### Configure & Run\n",
         "\n",
         "First let's create a DataReferenceConfigruation object to inform the system what data folder to download to the compute target.\n",
         "The path_on_compute should be an absolute path to ensure that the data files are downloaded only once.   The get_data method should use this same path to access the data files."
@@ -269,7 +279,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Create Get Data File\n",
+        "### Create Get Data File\n",
         "For remote executions you should author a get_data.py file containing a get_data() function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
         "\n",
         "The *get_data()* function returns a [dictionary](README.md#getdata).\n",
@@ -308,7 +318,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
+        "## Train\n",
         "\n",
         "You can specify automl_settings as **kwargs** as well. Also note that you can use the get_data() symantic for local excutions too. \n",
         "\n",
@@ -355,8 +365,6 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Training the Models <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
-    "\n",
         "For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets/models even when the experiment is running to retreive the best model up to that point. Once you are satisfied with the model you can cancel a particular iteration or the whole run."
       ]
     },
@@ -369,11 +377,20 @@
         "remote_run = experiment.submit(automl_config, show_output=False)"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
+        "## Results\n",
         "#### Widget for monitoring runs\n",
         "\n",
         "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
@@ -433,7 +450,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Canceling Runs\n",
+        "### Canceling Runs\n",
         "You can cancel ongoing remote runs using the *cancel()* and *cancel_iteration()* functions"
       ]
     },
@@ -454,7 +471,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Pre-process cache cleanup\n",
+        "### Pre-process cache cleanup\n",
         "The preprocess data gets cache at user default file store. When the run is completed the cache can be cleaned by running below cell"
       ]
     },
@@ -523,7 +540,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "### Testing the Best Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
+        "## Test\n"
       ]
     },
     {

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

@@ -13,11 +13,26 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "# Automated Machine Learning: Remote Execution using DSVM (Ubuntu)\n",
+        "# Automated Machine Learning\n",
+        "_**Remote Execution using DSVM (Ubuntu)**_\n",
         "\n",
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Data](#Data)\n",
+        "1. [Train](#Train)\n",
+        "1. [Results](#Results)\n",
+        "1. [Test](#Test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
         "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
         "\n",
-    "Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
         "\n",
         "In this notebook you wiil learn how to:\n",
         "1. Create an `Experiment` in an existing `Workspace`.\n",
@@ -32,14 +47,14 @@
         "- **Asynchronous** tracking of progress\n",
         "- **Cancellation** of individual iterations or the entire run\n",
         "- Retrieving models for any iteration or logged metric\n",
-    "- Specifying AutoML settings as `**kwargs`\n"
+        "- Specifying AutoML settings as `**kwargs`"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Create an Experiment\n",
+        "## Setup\n",
         "\n",
         "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
       ]
@@ -52,11 +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",
@@ -64,8 +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"
       ]
     },
     {
@@ -77,8 +89,8 @@
         "ws = Workspace.from_config()\n",
         "\n",
         "# Choose a name for the run history container in the workspace.\n",
-    "experiment_name = 'automl-remote-dsvm4'\n",
-    "project_folder = './sample_projects/automl-remote-dsvm4'\n",
+        "experiment_name = 'automl-remote-dsvm'\n",
+        "project_folder = './sample_projects/automl-remote-dsvm'\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
@@ -91,15 +103,14 @@
         "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"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Diagnostics\n",
-    "\n",
         "Opt-in diagnostics for better experience, quality, and security of future releases."
       ]
     },
@@ -117,7 +128,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Create a Remote Linux DSVM\n",
+        "### Create a Remote Linux DSVM\n",
         "**Note:** If creation fails with a message about Marketplace purchase eligibilty, start creation of a DSVM through the [Azure portal](https://portal.azure.com), and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled this setting, you can exit the portal without actually creating the DSVM, and creation of the DSVM through the notebook should work.\n"
       ]
     },
@@ -135,11 +146,11 @@
         "    print('Found an existing DSVM.')\n",
         "except:\n",
         "    print('Creating a new DSVM.')\n",
-    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
+        "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2s_v3\")\n",
         "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
         "    dsvm_compute.wait_for_completion(show_output = True)\n",
         "    print(\"Waiting one minute for ssh to be accessible\")\n",
-    "    time.sleep(60) # Wait for ssh to be accessible"
+        "    time.sleep(90) # Wait for ssh to be accessible"
       ]
     },
     {
@@ -165,7 +176,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Create Get Data File\n",
+        "## Data\n",
         "For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
         "In this example, the `get_data()` function returns data using scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
       ]
@@ -205,7 +216,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Configure AutoML <a class=\"anchor\" id=\"Instantiate-AutoML-Remote-DSVM\"></a>\n",
+        "## Train\n",
         "\n",
         "You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
         "\n",
@@ -256,8 +267,6 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Train the Models\n",
-    "\n",
         "Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run.\n",
         "\n",
         "In this example, we specify `show_output = False` to suppress console output while the run is in progress."
@@ -272,11 +281,20 @@
         "remote_run = experiment.submit(automl_config, show_output = False)"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "remote_run"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Explore the Results\n",
+        "## Results\n",
         "\n",
         "#### Loading Executed Runs\n",
         "In case you need to load a previously executed run, enable the cell below and replace the `run_id` value."
@@ -352,7 +370,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Cancelling Runs\n",
+        "### Cancelling Runs\n",
         "\n",
         "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
       ]
@@ -434,7 +452,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "### Test the Best Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n",
+        "## Test\n",
         "\n",
         "#### Load Test Data"
       ]

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

@@ -13,20 +13,33 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "# Automated Machine Learning: Sample Weight\n",
+        "# Automated Machine Learning\n",
+        "_**Sample Weight**_\n",
         "\n",
-    "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use sample weight with AutoML. Sample weight is used where some sample values are more important than others.\n",
-    "\n",
-    "Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
-    "\n",
-    "In this notebook you will learn how to configure AutoML to use `sample_weight` and you will see the difference sample weight makes to the test results.\n"
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Train](#Train)\n",
+        "1. [Test](#Test)\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Create an Experiment\n",
+        "## Introduction\n",
+        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use sample weight with AutoML. Sample weight is used where some sample values are more important than others.\n",
+        "\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
+        "\n",
+        "In this notebook you will learn how to configure AutoML to use `sample_weight` and you will see the difference sample weight makes to the test results."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Setup\n",
         "\n",
         "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
       ]
@@ -38,11 +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",
@@ -50,8 +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"
       ]
     },
     {
@@ -80,15 +89,14 @@
         "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"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Diagnostics\n",
-    "\n",
         "Opt-in diagnostics for better experience, quality, and security of future releases."
       ]
     },
@@ -106,7 +114,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Configure AutoML\n",
+        "## Train\n",
         "\n",
         "Instantiate two `AutoMLConfig` objects. One will be used with `sample_weight` and one without."
       ]
@@ -153,8 +161,6 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Train the Models\n",
-    "\n",
         "Call the `submit` method on the experiment objects and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
         "In this example, we specify `show_output = True` to print currently running iterations to the console."
       ]
@@ -176,7 +182,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "### Test the Best Fitted Model\n",
+        "## Test\n",
         "\n",
         "#### Load Test Data"
       ]

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

@@ -13,11 +13,25 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "# Automated Machine Learning: Train Test Split and Handling Sparse Data\n",
+        "# Automated Machine Learning\n",
+        "_**Train Test Split and Handling Sparse Data**_\n",
         "\n",
+        "## Contents\n",
+        "1. [Introduction](#Introduction)\n",
+        "1. [Setup](#Setup)\n",
+        "1. [Data](#Data)\n",
+        "1. [Train](#Train)\n",
+        "1. [Results](#Results)\n",
+        "1. [Test](#Test)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
         "In this example we use the scikit-learn's [20newsgroup](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_20newsgroups.html) to showcase how you can use AutoML for handling sparse data and how to specify custom cross validations splits.\n",
-    "\n",
-    "Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
+        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
         "\n",
         "In this notebook you will learn how to:\n",
         "1. Create an `Experiment` in an existing `Workspace`.\n",
@@ -35,7 +49,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Create an Experiment\n",
+        "## Setup\n",
         "\n",
         "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
       ]
@@ -47,20 +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"
       ]
     },
     {
@@ -87,15 +94,14 @@
         "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"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Diagnostics\n",
-    "\n",
         "Opt-in diagnostics for better experience, quality, and security of future releases."
       ]
     },
@@ -113,7 +119,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Creating Sparse Data"
+        "## Data"
       ]
     },
     {
@@ -155,7 +161,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Configure AutoML\n",
+        "## Train\n",
         "\n",
         "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
         "\n",
@@ -197,8 +203,6 @@
       "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."
       ]
@@ -212,11 +216,20 @@
         "local_run = experiment.submit(automl_config, show_output=True)"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "local_run"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Explore the Results"
+        "## Results"
       ]
     },
     {
@@ -324,7 +337,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "### Testing the Best Fitted Model"
+        "## Test"
       ]
     },
     {

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

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

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

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

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

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

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

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

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

@@ -0,0 +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 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, 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.
+
+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.
+
+**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.
+
+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. 
+
+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.
+
+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

@@ -0,0 +1,380 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
+        "\n",
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![04ACI](files/tables/image2.JPG)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#Model Building"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import pprint\n",
+        "import numpy as np\n",
+        "\n",
+        "from pyspark.ml import Pipeline, PipelineModel\n",
+        "from pyspark.ml.feature import OneHotEncoder, StringIndexer, VectorAssembler\n",
+        "from pyspark.ml.classification import LogisticRegression\n",
+        "from pyspark.ml.evaluation import BinaryClassificationEvaluator\n",
+        "from pyspark.ml.tuning import CrossValidator, ParamGridBuilder"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import azureml.core\n",
+        "\n",
+        "# Check core SDK version number\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# import the Workspace class and check the azureml SDK version\n",
+        "from azureml.core import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config(auth = auth)\n",
+        "print('Workspace name: ' + ws.name, \n",
+        "      'Azure region: ' + ws.location, \n",
+        "      'Subscription id: ' + ws.subscription_id, \n",
+        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# import the Workspace class and check the azureml SDK version\n",
+        "from azureml.core import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print('Workspace name: ' + ws.name, \n",
+        "      'Azure region: ' + ws.location, \n",
+        "      'Subscription id: ' + ws.subscription_id, \n",
+        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#get the train and test datasets\n",
+        "train_data_path = \"AdultCensusIncomeTrain\"\n",
+        "test_data_path = \"AdultCensusIncomeTest\"\n",
+        "\n",
+        "train = spark.read.parquet(train_data_path)\n",
+        "test = spark.read.parquet(test_data_path)\n",
+        "\n",
+        "print(\"train: ({}, {})\".format(train.count(), len(train.columns)))\n",
+        "print(\"test: ({}, {})\".format(test.count(), len(test.columns)))\n",
+        "\n",
+        "train.printSchema()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#Define Model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "label = \"income\"\n",
+        "dtypes = dict(train.dtypes)\n",
+        "dtypes.pop(label)\n",
+        "\n",
+        "si_xvars = []\n",
+        "ohe_xvars = []\n",
+        "featureCols = []\n",
+        "for idx,key in enumerate(dtypes):\n",
+        "    if dtypes[key] == \"string\":\n",
+        "        featureCol = \"-\".join([key, \"encoded\"])\n",
+        "        featureCols.append(featureCol)\n",
+        "        \n",
+        "        tmpCol = \"-\".join([key, \"tmp\"])\n",
+        "        # string-index and one-hot encode the string column\n",
+        "        #https://spark.apache.org/docs/2.3.0/api/java/org/apache/spark/ml/feature/StringIndexer.html\n",
+        "        #handleInvalid: Param for how to handle invalid data (unseen labels or NULL values). \n",
+        "        #Options are 'skip' (filter out rows with invalid data), 'error' (throw an error), \n",
+        "        #or 'keep' (put invalid data in a special additional bucket, at index numLabels). Default: \"error\"\n",
+        "        si_xvars.append(StringIndexer(inputCol=key, outputCol=tmpCol, handleInvalid=\"skip\"))\n",
+        "        ohe_xvars.append(OneHotEncoder(inputCol=tmpCol, outputCol=featureCol))\n",
+        "    else:\n",
+        "        featureCols.append(key)\n",
+        "\n",
+        "# string-index the label column into a column named \"label\"\n",
+        "si_label = StringIndexer(inputCol=label, outputCol='label')\n",
+        "\n",
+        "# assemble the encoded feature columns in to a column named \"features\"\n",
+        "assembler = VectorAssembler(inputCols=featureCols, outputCol=\"features\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.run import Run\n",
+        "from azureml.core.experiment import Experiment\n",
+        "import numpy as np\n",
+        "import os\n",
+        "import shutil\n",
+        "\n",
+        "model_name = \"AdultCensus_runHistory.mml\"\n",
+        "model_dbfs = os.path.join(\"/dbfs\", model_name)\n",
+        "run_history_name = 'spark-ml-notebook'\n",
+        "\n",
+        "# start a training run by defining an experiment\n",
+        "myexperiment = Experiment(ws, \"Ignite_AI_Talk\")\n",
+        "root_run = myexperiment.start_logging()\n",
+        "\n",
+        "# Regularization Rates - \n",
+        "regs = [0.0001, 0.001, 0.01, 0.1]\n",
+        " \n",
+        "# try a bunch of regularization rate in a Logistic Regression model\n",
+        "for reg in regs:\n",
+        "    print(\"Regularization rate: {}\".format(reg))\n",
+        "    # create a bunch of child runs\n",
+        "    with root_run.child_run(\"reg-\" + str(reg)) as run:\n",
+        "        # create a new Logistic Regression model.\n",
+        "        lr = LogisticRegression(regParam=reg)\n",
+        "        \n",
+        "        # put together the pipeline\n",
+        "        pipe = Pipeline(stages=[*si_xvars, *ohe_xvars, si_label, assembler, lr])\n",
+        "\n",
+        "        # train the model\n",
+        "        model_p = pipe.fit(train)\n",
+        "        \n",
+        "        # make prediction\n",
+        "        pred = model_p.transform(test)\n",
+        "        \n",
+        "        # evaluate. note only 2 metrics are supported out of the box by Spark ML.\n",
+        "        bce = BinaryClassificationEvaluator(rawPredictionCol='rawPrediction')\n",
+        "        au_roc = bce.setMetricName('areaUnderROC').evaluate(pred)\n",
+        "        au_prc = bce.setMetricName('areaUnderPR').evaluate(pred)\n",
+        "\n",
+        "        print(\"Area under ROC: {}\".format(au_roc))\n",
+        "        print(\"Area Under PR: {}\".format(au_prc))\n",
+        "      \n",
+        "        # log reg, au_roc, au_prc and feature names in run history\n",
+        "        run.log(\"reg\", reg)\n",
+        "        run.log(\"au_roc\", au_roc)\n",
+        "        run.log(\"au_prc\", au_prc)\n",
+        "        run.log_list(\"columns\", train.columns)\n",
+        "\n",
+        "        # save model\n",
+        "        model_p.write().overwrite().save(model_name)\n",
+        "        \n",
+        "        # upload the serialized model into run history record\n",
+        "        mdl, ext = model_name.split(\".\")\n",
+        "        model_zip = mdl + \".zip\"\n",
+        "        shutil.make_archive(mdl, 'zip', model_dbfs)\n",
+        "        run.upload_file(\"outputs/\" + model_name, model_zip)        \n",
+        "        #run.upload_file(\"outputs/\" + model_name, path_or_stream = model_dbfs) #cannot deal with folders\n",
+        "\n",
+        "        # now delete the serialized model from local folder since it is already uploaded to run history \n",
+        "        shutil.rmtree(model_dbfs)\n",
+        "        os.remove(model_zip)\n",
+        "        \n",
+        "# Declare run completed\n",
+        "root_run.complete()\n",
+        "root_run_id = root_run.id\n",
+        "print (\"run id:\", root_run.id)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "metrics = root_run.get_metrics(recursive=True)\n",
+        "best_run_id = max(metrics, key = lambda k: metrics[k]['au_roc'])\n",
+        "print(best_run_id, metrics[best_run_id]['au_roc'], metrics[best_run_id]['reg'])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#Get the best run\n",
+        "child_runs = {}\n",
+        "\n",
+        "for r in root_run.get_children():\n",
+        "    child_runs[r.id] = r\n",
+        "   \n",
+        "best_run = child_runs[best_run_id]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#Download the model from the best run to a local folder\n",
+        "best_model_file_name = \"best_model.zip\"\n",
+        "best_run.download_file(name = 'outputs/' + model_name, output_file_path = best_model_file_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#Model Evaluation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "##unzip the model to dbfs (as load() seems to require that) and load it.\n",
+        "if os.path.isfile(model_dbfs) or os.path.isdir(model_dbfs):\n",
+        "    shutil.rmtree(model_dbfs)\n",
+        "shutil.unpack_archive(best_model_file_name, model_dbfs)\n",
+        "\n",
+        "model_p_best = PipelineModel.load(model_name)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# make prediction\n",
+        "pred = model_p_best.transform(test)\n",
+        "output = pred[['hours_per_week','age','workclass','marital_status','income','prediction']]\n",
+        "display(output.limit(5))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# evaluate. note only 2 metrics are supported out of the box by Spark ML.\n",
+        "bce = BinaryClassificationEvaluator(rawPredictionCol='rawPrediction')\n",
+        "au_roc = bce.setMetricName('areaUnderROC').evaluate(pred)\n",
+        "au_prc = bce.setMetricName('areaUnderPR').evaluate(pred)\n",
+        "\n",
+        "print(\"Area under ROC: {}\".format(au_roc))\n",
+        "print(\"Area Under PR: {}\".format(au_prc))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#Model Persistence"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "##NOTE: by default the model is saved to and loaded from /dbfs/ instead of cwd!\n",
+        "model_p_best.write().overwrite().save(model_name)\n",
+        "print(\"saved model to {}\".format(model_dbfs))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%sh\n",
+        "\n",
+        "ls -la /dbfs/AdultCensus_runHistory.mml/*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "dbutils.notebook.exit(\"success\")"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "pasha"
+      },
+      {
+        "name": "wamartin"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "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": "03.Build_model_runHistory",
+    "notebookId": 3836944406456339
+  },
+  "nbformat": 4,
+  "nbformat_minor": 1
+}

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

@@ -0,0 +1,338 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
+        "\n",
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Please ensure you have run all previous notebooks in sequence before running this.\n",
+        "\n",
+        "Please Register Azure Container Instance(ACI) using Azure Portal: https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-manager-supported-services#portal in your subscription before using the SDK to deploy your ML model to ACI."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![04ACI](files/tables/image3.JPG)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import azureml.core\n",
+        "\n",
+        "# Check core SDK version number\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace\n",
+        "\n",
+        "#'''\n",
+        "ws = Workspace.from_config(auth = auth)\n",
+        "print('Workspace name: ' + ws.name, \n",
+        "      'Azure region: ' + ws.location, \n",
+        "      'Subscription id: ' + ws.subscription_id, \n",
+        "      'Resource group: ' + ws.resource_group, sep = '\\n')\n",
+        "#'''"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace\n",
+        "import azureml.core\n",
+        "\n",
+        "# Check core SDK version number\n",
+        "print(\"SDK version:\", azureml.core.VERSION)\n",
+        "\n",
+        "#'''\n",
+        "ws = Workspace.from_config()\n",
+        "print('Workspace name: ' + ws.name, \n",
+        "      'Azure region: ' + ws.location, \n",
+        "      'Subscription id: ' + ws.subscription_id, \n",
+        "      'Resource group: ' + ws.resource_group, sep = '\\n')\n",
+        "#'''"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "##NOTE: service deployment always gets the model from the current working dir.\n",
+        "import os\n",
+        "\n",
+        "model_name = \"AdultCensus_runHistory.mml\" # \n",
+        "model_name_dbfs = os.path.join(\"/dbfs\", model_name)\n",
+        "\n",
+        "print(\"copy model from dbfs to local\")\n",
+        "model_local = \"file:\" + os.getcwd() + \"/\" + model_name\n",
+        "dbutils.fs.cp(model_name, model_local, True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#Register the model\n",
+        "from azureml.core.model import Model\n",
+        "mymodel = Model.register(model_path = model_name, # this points to a local file\n",
+        "                       model_name = model_name, # this is the name the model is registered as, am using same name for both path and name.                 \n",
+        "                       description = \"ADB trained model by Parashar\",\n",
+        "                       workspace = ws)\n",
+        "\n",
+        "print(mymodel.name, mymodel.description, mymodel.version)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#%%writefile score_sparkml.py\n",
+        "score_sparkml = \"\"\"\n",
+        " \n",
+        "import json\n",
+        " \n",
+        "def init():\n",
+        "    # One-time initialization of PySpark and predictive model\n",
+        "    import pyspark\n",
+        "    from azureml.core.model import Model\n",
+        "    from pyspark.ml import PipelineModel\n",
+        " \n",
+        "    global trainedModel\n",
+        "    global spark\n",
+        " \n",
+        "    spark = pyspark.sql.SparkSession.builder.appName(\"ADB and AML notebook by Parashar\").getOrCreate()\n",
+        "    model_name = \"{model_name}\" #interpolated\n",
+        "    model_path = Model.get_model_path(model_name)\n",
+        "    trainedModel = PipelineModel.load(model_path)\n",
+        "    \n",
+        "def run(input_json):\n",
+        "    if isinstance(trainedModel, Exception):\n",
+        "        return json.dumps({{\"trainedModel\":str(trainedModel)}})\n",
+        "      \n",
+        "    try:\n",
+        "        sc = spark.sparkContext\n",
+        "        input_list = json.loads(input_json)\n",
+        "        input_rdd = sc.parallelize(input_list)\n",
+        "        input_df = spark.read.json(input_rdd)\n",
+        "    \n",
+        "        # Compute prediction\n",
+        "        prediction = trainedModel.transform(input_df)\n",
+        "        #result = prediction.first().prediction\n",
+        "        predictions = prediction.collect()\n",
+        " \n",
+        "        #Get each scored result\n",
+        "        preds = [str(x['prediction']) for x in predictions]\n",
+        "        result = \",\".join(preds)\n",
+        "        # you can return any data type as long as it is JSON-serializable\n",
+        "        return result.tolist()\n",
+        "    except Exception as e:\n",
+        "        result = str(e)\n",
+        "        return result\n",
+        "    \n",
+        "\"\"\".format(model_name=model_name)\n",
+        " \n",
+        "exec(score_sparkml)\n",
+        " \n",
+        "with open(\"score_sparkml.py\", \"w\") as file:\n",
+        "    file.write(score_sparkml)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.conda_dependencies import CondaDependencies \n",
+        "\n",
+        "myacienv = CondaDependencies.create(conda_packages=['scikit-learn','numpy','pandas']) #showing how to add libs as an eg. - not needed for this model.\n",
+        "\n",
+        "with open(\"mydeployenv.yml\",\"w\") as f:\n",
+        "    f.write(myacienv.serialize_to_string())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#deploy to ACI\n",
+        "from azureml.core.webservice import AciWebservice, Webservice\n",
+        "\n",
+        "myaci_config = AciWebservice.deploy_configuration(\n",
+        "    cpu_cores = 2, \n",
+        "    memory_gb = 2, \n",
+        "    tags = {'name':'Databricks Azure ML ACI'}, \n",
+        "    description = 'This is for ADB and AML example. Azure Databricks & Azure ML SDK demo with ACI by Parashar.')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# this will take 10-15 minutes to finish\n",
+        "\n",
+        "service_name = \"aciws\"\n",
+        "runtime = \"spark-py\" \n",
+        "driver_file = \"score_sparkml.py\"\n",
+        "my_conda_file = \"mydeployenv.yml\"\n",
+        "\n",
+        "# image creation\n",
+        "from azureml.core.image import ContainerImage\n",
+        "myimage_config = ContainerImage.image_configuration(execution_script = driver_file, \n",
+        "                                    runtime = runtime, \n",
+        "                                    conda_file = my_conda_file)\n",
+        "\n",
+        "# Webservice creation\n",
+        "myservice = Webservice.deploy_from_model(\n",
+        "  workspace=ws, \n",
+        "  name=service_name,\n",
+        "  deployment_config = myaci_config,\n",
+        "  models = [mymodel],\n",
+        "  image_config = myimage_config\n",
+        "    )\n",
+        "\n",
+        "myservice.wait_for_deployment(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "help(Webservice)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# List images by ws\n",
+        "\n",
+        "for i in ContainerImage.list(workspace = ws):\n",
+        "    print('{}(v.{} [{}]) stored at {} with build log {}'.format(i.name, i.version, i.creation_state, i.image_location, i.image_build_log_uri))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#for using the Web HTTP API \n",
+        "print(myservice.scoring_uri)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import json\n",
+        "\n",
+        "#get the some sample data\n",
+        "test_data_path = \"AdultCensusIncomeTest\"\n",
+        "test = spark.read.parquet(test_data_path).limit(5)\n",
+        "\n",
+        "test_json = json.dumps(test.toJSON().collect())\n",
+        "\n",
+        "print(test_json)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#using data defined above predict if income is >50K (1) or <=50K (0)\n",
+        "myservice.run(input_data=test_json)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#comment to not delete the web service\n",
+        "#myservice.delete()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "pasha"
+      },
+      {
+        "name": "wamartin"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "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": "04.DeploytoACI",
+    "notebookId": 3836944406456376
+  },
+  "nbformat": 4,
+  "nbformat_minor": 1
+}

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

@@ -0,0 +1,182 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
+        "\n",
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![04ACI](files/tables/image1.JPG)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#Data Ingestion"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import urllib"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Download AdultCensusIncome.csv from Azure CDN. This file has 32,561 rows.\n",
+        "basedataurl = \"https://amldockerdatasets.azureedge.net\"\n",
+        "datafile = \"AdultCensusIncome.csv\"\n",
+        "datafile_dbfs = os.path.join(\"/dbfs\", datafile)\n",
+        "\n",
+        "if os.path.isfile(datafile_dbfs):\n",
+        "    print(\"found {} at {}\".format(datafile, datafile_dbfs))\n",
+        "else:\n",
+        "    print(\"downloading {} to {}\".format(datafile, datafile_dbfs))\n",
+        "    urllib.request.urlretrieve(os.path.join(basedataurl, datafile), datafile_dbfs)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Create a Spark dataframe out of the csv file.\n",
+        "data_all = sqlContext.read.format('csv').options(header='true', inferSchema='true', ignoreLeadingWhiteSpace='true', ignoreTrailingWhiteSpace='true').load(datafile)\n",
+        "print(\"({}, {})\".format(data_all.count(), len(data_all.columns)))\n",
+        "data_all.printSchema()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#renaming columns\n",
+        "columns_new = [col.replace(\"-\", \"_\") for col in data_all.columns]\n",
+        "data_all = data_all.toDF(*columns_new)\n",
+        "data_all.printSchema()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "display(data_all.limit(5))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#Data Preparation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Choose feature columns and the label column.\n",
+        "label = \"income\"\n",
+        "xvars = set(data_all.columns) - {label}\n",
+        "\n",
+        "print(\"label = {}\".format(label))\n",
+        "print(\"features = {}\".format(xvars))\n",
+        "\n",
+        "data = data_all.select([*xvars, label])\n",
+        "\n",
+        "# Split data into train and test.\n",
+        "train, test = data.randomSplit([0.75, 0.25], seed=123)\n",
+        "\n",
+        "print(\"train ({}, {})\".format(train.count(), len(train.columns)))\n",
+        "print(\"test ({}, {})\".format(test.count(), len(test.columns)))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#Data Persistence"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Write the train and test data sets to intermediate storage\n",
+        "train_data_path = \"AdultCensusIncomeTrain\"\n",
+        "test_data_path = \"AdultCensusIncomeTest\"\n",
+        "\n",
+        "train_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTrain\")\n",
+        "test_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTest\")\n",
+        "\n",
+        "train.write.mode('overwrite').parquet(train_data_path)\n",
+        "test.write.mode('overwrite').parquet(test_data_path)\n",
+        "print(\"train and test datasets saved to {} and {}\".format(train_data_path_dbfs, test_data_path_dbfs))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "pasha"
+      },
+      {
+        "name": "wamartin"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "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": "02.Ingest_data",
+    "notebookId": 3836944406456362
+  },
+  "nbformat": 4,
+  "nbformat_minor": 1
+}

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

@@ -17,7 +17,7 @@
       "source": [
         "We support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package. You can select the option to attach the library to all clusters or just one cluster.\n",
         "\n",
-        "**azureml-sdk**\n",
+        "**install azureml-sdk**\n",
         "* Source: Upload Python Egg or PyPi\n",
         "* PyPi Name: `azureml-sdk[databricks]`\n",
         "* Select Install Library"

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

@@ -0,0 +1,559 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Automated ML on Azure Databricks\n",
+        "\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 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",
+        "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": [
+        "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"
+      ]
+    },
+    {
+      "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>\" #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"
+      ]
+    },
+    {
+      "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": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    },
+    {
+      "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 Automated ML 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": [
+        "#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",
+        "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 = 30,\n",
+        "                             n_cross_validations = 10,\n",
+        "                             max_concurrent_iterations = 2, #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",
+        "                             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'>Your experiment in 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": [
+        "### Test the Best Fitted Model\n",
+        "\n",
+        "#### Load Test Data - you can split the dataset beforehand & pass Train dataset to AutoML and use Test dataset to evaluate the best model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn import datasets\n",
+        "digits = datasets.load_digits()\n",
+        "X_test = digits.data[:10, :]\n",
+        "y_test = digits.target[:10]\n",
+        "images = digits.images[:10]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Testing Our Best Fitted Model\n",
+        "We will try to predict digits and see how our model works. This is just an example to show you."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Randomly select digits and test.\n",
+        "for index in np.random.choice(len(y_test), 2, replace = False):\n",
+        "    print(index)\n",
+        "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
+        "    label = y_test[index]\n",
+        "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
+        "    fig = plt.figure(1, figsize = (3,3))\n",
+        "    ax1 = fig.add_axes((0,0,.8,.8))\n",
+        "    ax1.set_title(title)\n",
+        "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
+        "    display(fig)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "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>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "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": 817220787969977
+  },
+  "nbformat": 4,
+  "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/azure-databricks/automl_adb_readme.md

@@ -1,47 +0,0 @@
-**PREVIEW capability** 
-
-Automated ML now supports Azure Databricks as a local compute to perform training (**public preview**). Azure Databricks is a managed Spark offering on Azure and customers already use it for advanced analytics. It provides a collaborative Notebook based environment with CPU or GPU based compute cluster. 
-- Customers who use Azure Databricks for advanced analytics can now use the same cluster to run automated machine learning experiments. 
-- You can keep the data within the same cluster. 
-- You can leverage the local worker nodes with autoscale and auto termination capabilities. 
-- You can use multiple cores of your Azure Databricks cluster to perform simultenous training. 
-- You can further tune the model generated by automated machine learning if you chose to. 
-- Every run (including the best run) is available as a pipeline. 
-- The model from the pipeline can be registered in Azure ML SDK workspace and then deployed to Azure managed compute (ACI or AKS) using the Azure Machine learning SDK.
-
-**Create Azure Databricks Cluster:**
-
-Select New Cluster and fill in following detail:
- - Cluster name: _yourclustername_
- - Cluster Mode: Any. **High Concurrency** preferred
- - Databricks Runtime: Any 4.x runtime.
- - Python version: **3**
- - Workers: 2 or higher.  
- - Max. number of **concurrent iterations** in Automated ML settings is **<=** to the number of **worker nodes** in your Databricks cluster.
- - Worker node VM types: **Memory optimized VM** preferred. 
- - Uncheck _Enable Autoscaling_
-
-
-It will take few minutes to create the cluster. Please ensure that the cluster state is running before proceeding further.
-
-**Install Azure ML with Automated ML SDK on your Azure Databricks cluster**
-
-- Select Import library
-
-- Source: Upload Python Egg or PyPI
-
-- PyPi Name: **azureml-sdk[automl_databricks]**
-
-- Click Install Library
-
-- Do not select _Attach automatically to all clusters_. In case you have selected earlier then you can go to your Home folder and deselect it.
-
-- Select the check box _Attach_ next to your cluster name
-
-(More details on how to attach and detach libs are here - [https://docs.databricks.com/user-guide/libraries.html#attach-a-library-to-a-cluster](https://docs.databricks.com/user-guide/libraries.html#attach-a-library-to-a-cluster) )
-
-- Ensure that there are no errors until Status changes to _Attached_. It may take a couple of minutes.
-
-**Note** - If you have the old build the please deselect it from cluster’s installed libs > move to trash. Install the new build and restart the cluster. And if still there is an issue then detach and reattach your cluster.
-
-**Now you can run the Automated ML sample notebook on your Azure Databricks cluster. Please let us know your feedback.**

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)"
       ]
     },
@@ -52,8 +50,7 @@
       "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\n"
       ]
     },
     {
@@ -103,8 +100,7 @@
         "\n",
         "### b. In your run function add:\n",
         "```python\n",
-    "print (\"saving input data\" + time.strftime(\"%H:%M:%S\"))\n",
-    "print (\"saving prediction data\" + time.strftime(\"%H:%M:%S\"))```"
+        "print (\"Prediction created\" + time.strftime(\"%H:%M:%S\"))```"
       ]
     },
     {
@@ -120,7 +116,6 @@
         "from sklearn.externals import joblib\n",
         "from sklearn.linear_model import Ridge\n",
         "from azureml.core.model import Model\n",
-    "from azureml.monitoring import ModelDataCollector\n",
         "import time\n",
         "\n",
         "def init():\n",
@@ -135,32 +130,14 @@
         "    # deserialize the model file back into a sklearn model\n",
         "    model = joblib.load(model_path)\n",
         "  \n",
-    "    global inputs_dc, prediction_dc\n",
-    "    \n",
-    "    # this setup will help us save our inputs under the \"inputs\" path in our Azure Blob\n",
-    "    inputs_dc = ModelDataCollector(model_name=\"sklearn_regression_model\", identifier=\"inputs\", feature_names=[\"feat1\", \"feat2\"]) \n",
-    "    \n",
-    "    # this setup will help us save our ipredictions under the \"predictions\" path in our Azure Blob\n",
-    "    prediction_dc = ModelDataCollector(\"sklearn_regression_model\", identifier=\"predictions\", feature_names=[\"prediction1\", \"prediction2\"]) \n",
         "\n",
         "# note you can pass in multiple rows for scoring\n",
         "def run(raw_data):\n",
-    "    global inputs_dc, prediction_dc\n",
         "    try:\n",
         "        data = json.loads(raw_data)['data']\n",
         "        data = numpy.array(data)\n",
         "        result = model.predict(data)\n",
-    "        \n",
-    "        #Print statement for appinsights custom traces:\n",
-    "        print (\"saving input data\" + time.strftime(\"%H:%M:%S\"))\n",
-    "        \n",
-    "        #this call is saving our input data into our blob\n",
-    "        inputs_dc.collect(data) \n",
-    "        #this call is saving our prediction data into our blob\n",
-    "        prediction_dc.collect(result)\n",
-    "        \n",
-    "        #Print statement for appinsights custom traces:\n",
-    "        print (\"saving prediction data\" + time.strftime(\"%H:%M:%S\"))\n",
+        "        print (\"Prediction created\" + time.strftime(\"%H:%M:%S\"))\n",
         "        # you can return any datatype as long as it is JSON-serializable\n",
         "        return result.tolist()\n",
         "    except Exception as e:\n",
@@ -221,6 +198,74 @@
         "image.wait_for_creation(show_output = True)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Deploy to ACI (Optional)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import AciWebservice\n",
+        "\n",
+        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
+        "                                               memory_gb = 1, \n",
+        "                                               tags = {'area': \"diabetes\", 'type': \"regression\"}, \n",
+        "                                               description = 'Predict diabetes using regression model',\n",
+        "                                               enable_app_insights = True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import Webservice\n",
+        "\n",
+        "aci_service_name = 'my-aci-service-4'\n",
+        "print(aci_service_name)\n",
+        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
+        "                                           image = image,\n",
+        "                                           name = aci_service_name,\n",
+        "                                           workspace = ws)\n",
+        "aci_service.wait_for_deployment(True)\n",
+        "print(aci_service.state)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "\n",
+        "test_sample = json.dumps({'data': [\n",
+        "    [1,28,13,45,54,6,57,8,8,10], \n",
+        "    [101,9,8,37,6,45,4,3,2,41]\n",
+        "]})\n",
+        "test_sample = bytes(test_sample,encoding='utf8')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "if aci_service.state == \"Healthy\":\n",
+        "    prediction = aci_service.run(input_data=test_sample)\n",
+        "    print(prediction)\n",
+        "else:\n",
+        "    raise ValueError(\"Service deployment isn't healthy, can't call the service\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -232,7 +277,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "### Create AKS compute if you haven't done so (Notebook 11)"
+        "### Create AKS compute if you haven't done so."
       ]
     },
     {
@@ -244,7 +289,7 @@
         "# Use the default configuration (can also provide parameters to customize)\n",
         "prov_config = AksCompute.provisioning_configuration()\n",
         "\n",
-    "aks_name = 'my-aks-test2' \n",
+        "aks_name = 'my-aks-test3' \n",
         "# Create the cluster\n",
         "aks_target = ComputeTarget.create(workspace = ws, \n",
         "                                  name = aks_name, \n",
@@ -258,7 +303,15 @@
       "outputs": [],
       "source": [
         "%%time\n",
-    "aks_target.wait_for_completion(show_output = True)\n",
+        "aks_target.wait_for_completion(show_output = True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
         "print(aks_target.provisioning_state)\n",
         "print(aks_target.provisioning_errors)"
       ]
@@ -317,9 +370,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "%%time\n",
-    "aks_service_name ='aks-w-dc3'\n",
-    "\n",
+        "if aks_target.provisioning_state== \"Succeeded\": \n",
+        "    aks_service_name ='aks-w-dc5'\n",
         "    aks_service = Webservice.deploy_from_image(workspace = ws, \n",
         "                                               name = aks_service_name,\n",
         "                                               image = image,\n",
@@ -327,7 +379,9 @@
         "                                               deployment_target = aks_target\n",
         "                                               )\n",
         "    aks_service.wait_for_deployment(show_output = True)\n",
-    "print(aks_service.state)"
+        "    print(aks_service.state)\n",
+        "else:\n",
+        "    raise ValueError(\"AKS provisioning failed.\")"
       ]
     },
     {
@@ -344,7 +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",
@@ -352,8 +405,11 @@
         "]})\n",
         "test_sample = bytes(test_sample,encoding='utf8')\n",
         "\n",
+        "if aks_service.state == \"Healthy\":\n",
         "    prediction = aks_service.run(input_data=test_sample)\n",
-    "print(prediction)"
+        "    print(prediction)\n",
+        "else:\n",
+        "    raise ValueError(\"Service deployment isn't healthy, can't call the service\")"
       ]
     },
     {
@@ -384,12 +440,32 @@
       "source": [
         "aks_service.update(enable_app_insights=False)"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Clean up"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "aks_service.delete()\n",
+        "aci_service.delete()\n",
+        "image.delete()\n",
+        "model.delete()"
+      ]
     }
   ],
   "metadata": {
     "authors": [
       {
-    "name": "marthalc"
+        "name": "jocier"
       }
     ],
     "kernelspec": {
@@ -407,7 +483,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+      "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"
       ]
     },
     {
@@ -237,7 +233,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "### Create AKS compute if you haven't done so (Notebook 11)"
+        "### Create AKS compute if you haven't done so."
       ]
     },
     {
@@ -277,9 +273,7 @@
     },
     {
       "cell_type": "markdown",
-   "metadata": {
-    "scrolled": true
-   },
+      "metadata": {},
       "source": [
         "```python \n",
         "    %%time\n",
@@ -324,9 +318,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "%%time\n",
-    "aks_service_name ='aks-w-dc2'\n",
-    "\n",
+        "if aks_target.provisioning_state== \"Succeeded\": \n",
+        "    aks_service_name ='aks-w-dc0'\n",
         "    aks_service = Webservice.deploy_from_image(workspace = ws, \n",
         "                                               name = aks_service_name,\n",
         "                                               image = image,\n",
@@ -334,7 +327,9 @@
         "                                               deployment_target = aks_target\n",
         "                                               )\n",
         "    aks_service.wait_for_deployment(show_output = True)\n",
-    "print(aks_service.state)"
+        "    print(aks_service.state)\n",
+        "else: \n",
+        "    raise ValueError(\"aks provisioning failed, can't deploy service\")"
       ]
     },
     {
@@ -363,8 +358,11 @@
         "]})\n",
         "test_sample = bytes(test_sample,encoding = 'utf8')\n",
         "\n",
+        "if aks_service.state == \"Healthy\":\n",
         "    prediction = aks_service.run(input_data=test_sample)\n",
-    "print(prediction)"
+        "    print(prediction)\n",
+        "else:\n",
+        "    raise ValueError(\"Service deployment isn't healthy, can't call the service\")"
       ]
     },
     {
@@ -399,11 +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."
       ]
@@ -423,12 +421,31 @@
       "source": [
         "aks_service.update(collect_model_data=False)"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Clean up"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "aks_service.delete()\n",
+        "image.delete()\n",
+        "model.delete()"
+      ]
     }
   ],
   "metadata": {
     "authors": [
       {
-    "name": "marthalc"
+        "name": "jocier"
       }
     ],
     "kernelspec": {
@@ -446,7 +463,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+      "version": "3.6.3"
     }
   },
   "nbformat": 4,

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

@@ -1,14 +1,20 @@
-# ONNX on Azure Machine Learning
+# ONNX on Azure Machine Learning
 
-These tutorials show how to create and deploy [ONNX](http://onnx.ai) models in Azure Machine Learning environments using [ONNX Runtime](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-build-deploy-onnx) for inference. Once deployed as a web service, you can ping the model with your own set of images to be analyzed!
+These tutorials show how to create and deploy Open Neural Network eXchange ([ONNX](http://onnx.ai)) models in Azure Machine Learning environments using [ONNX Runtime](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-build-deploy-onnx) for inference. Once deployed as a web service, you can ping the model with your own set of images to be analyzed!
 
 ## Tutorials
-- [Obtain ONNX model from ONNX Model Zoo and deploy with ONNX Runtime inference - Handwritten Digit Classification (MNIST)](https://github.com/Azure/MachineLearningNotebooks/blob/master/onnx/onnx-inference-mnist-deploy.ipynb)
-- [Obtain ONNX model from ONNX Model Zoo and deploy with ONNX Runtime inference - Facial Expression Recognition (Emotion FER+)](https://github.com/Azure/MachineLearningNotebooks/blob/master/onnx/onnx-inference-facial-emotion-recognition-deploy.ipynb)
-- [Obtain ONNX model from ONNX Model Zoo and deploy with ONNX Runtime inference - Image Recognition (ResNet50)](https://github.com/Azure/MachineLearningNotebooks/blob/master/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb)
-- [Convert ONNX model from CoreML and deploy - TinyYOLO](https://github.com/Azure/MachineLearningNotebooks/blob/master/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb)
-- [Train ONNX model in PyTorch and deploy - MNIST](https://github.com/Azure/MachineLearningNotebooks/blob/master/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb)
 
+0. [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)
+2. [Facial Expression Recognition (Emotion FER+)](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb)
+
+#### Demo Notebooks from Microsoft Ignite 2018
+Note that the following notebooks do not have evaluation sections for the models since they were deployed as part of a live demo. You can find the respective pre-processing and post-processing code linked from the ONNX Model Zoo Github pages ([ResNet](https://github.com/onnx/models/tree/master/models/image_classification/resnet), [TinyYoloV2](https://github.com/onnx/models/tree/master/tiny_yolov2)), or experiment with the ONNX models by [running them in the browser](https://microsoft.github.io/onnxjs-demo/#/).
+
+3. [Image Recognition (ResNet50)](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb)
+4. [Convert Core ML Model to ONNX and deploy - Real Time Object Detection (TinyYOLO)](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb) 
 
 ## Documentation
 - [ONNX Runtime Python API Documentation](http://aka.ms/onnxruntime-python)
@@ -19,7 +25,6 @@ These tutorials show how to create and deploy [ONNX](http://onnx.ai) models in A
 - [Azure AI – Making AI Real for Business](https://aka.ms/aml-blog-overview) 
 - [What’s new in Azure Machine Learning](https://aka.ms/aml-blog-whats-new)
 
-
 ## License
 Copyright (c) Microsoft Corporation. All rights reserved.  
 Licensed under the MIT License.

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",
@@ -255,25 +255,36 @@
         "    input_name = session.get_inputs()[0].name\n",
         "    output_name = session.get_outputs()[0].name \n",
         "    \n",
+        "\n",
+        "def preprocess(input_data_json):\n",
+        "    # convert the JSON data into the tensor input\n",
+        "    return np.array(json.loads(input_data_json)['data']).astype('float32')\n",
+        "\n",
+        "def postprocess(result):\n",
+        "    # We use argmax to pick the highest confidence label\n",
+        "    return int(np.argmax(np.array(result).squeeze(), axis=0))\n",
+        "    \n",
         "def run(input_data):\n",
-    "    '''Purpose: evaluate test input in Azure Cloud using onnxruntime.\n",
-    "        We will call the run function later from our Jupyter Notebook \n",
-    "        so our azure service can evaluate our model input in the cloud. '''\n",
         "\n",
         "    try:\n",
         "        # load in our data, convert to readable format\n",
-    "        data = np.array(json.loads(input_data)['data']).astype('float32')\n",
+        "        data = preprocess(input_data)\n",
         "        \n",
+        "        # start timer\n",
         "        start = time.time()\n",
-    "        r = session.run([output_name], {input_name: data})[0]\n",
+        "        \n",
+        "        r = session.run([output_name], {input_name: data})\n",
+        "        \n",
+        "        #end timer\n",
         "        end = time.time()\n",
-    "        result = choose_class(r[0])\n",
-    "        result_dict = {\"result\": [result],\n",
-    "                      \"time_in_sec\": [end - start]}\n",
+        "        \n",
+        "        result = postprocess(r)\n",
+        "        result_dict = {\"result\": result,\n",
+        "                      \"time_in_sec\": end - start}\n",
         "    except Exception as e:\n",
         "        result_dict = {\"error\": str(e)}\n",
         "    \n",
-    "    return json.dumps(result_dict)\n",
+        "    return result_dict\n",
         "\n",
         "def choose_class(result_prob):\n",
         "    \"\"\"We use argmax to determine the right label to choose from our output\"\"\"\n",
@@ -423,7 +434,16 @@
         "\n",
         "If you've made it this far, you've deployed a working VM with a handwritten digit classifier running in the cloud using Azure ML. Congratulations!\n",
         "\n",
-    "Let's see how well our model deals with our test images."
+        "You can get the URL for the webservice with the code below. Let's now see how well our model deals with our test images."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(aci_service.scoring_uri)"
       ]
     },
     {
@@ -544,14 +564,14 @@
         "    input_data = json.dumps({'data': test_inputs[i].tolist()})\n",
         "    \n",
         "    # predict using the deployed model\n",
-    "    r = json.loads(aci_service.run(input_data))\n",
+        "    r = aci_service.run(input_data)\n",
         "    \n",
         "    if \"error\" in r:\n",
         "        print(r['error'])\n",
         "        break\n",
         "        \n",
-    "    result = r['result'][0]\n",
-    "    time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n",
+        "    result = r['result']\n",
+        "    time_ms = np.round(r['time_in_sec'] * 1000, 2)\n",
         "    \n",
         "    ground_truth = int(np.argmax(test_outputs[i]))\n",
         "    \n",
@@ -601,19 +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"
       ]
@@ -658,10 +678,10 @@
         "    input_data = json.dumps({'data': img.tolist()})\n",
         "\n",
         "    try:\n",
-    "        r = json.loads(aci_service.run(input_data))\n",
-    "        result = r['result'][0]\n",
-    "        time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n",
-    "    except Exception as e:\n",
+        "        r = aci_service.run(input_data)\n",
+        "        result = r['result']\n",
+        "        time_ms = np.round(r['time_in_sec'] * 1000, 2)\n",
+        "    except KeyError as e:\n",
         "        print(str(e))\n",
         "\n",
         "    plt.figure(figsize = (16, 6))\n",
@@ -783,7 +803,7 @@
       "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-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/README.md

@@ -8,6 +8,8 @@ The Python-based Azure Machine Learning Pipeline SDK provides interfaces to work
 
 Data management and reuse across pipelines and pipeline runs is simplified using named and strictly versioned data sources and named inputs and outputs for processing tasks. Pipelines enable collaboration across teams of data scientists by recording all intermediate tasks and data.
 
+Learn more about how to [create your first machine learning pipeline](https://docs.microsoft.com/azure/machine-learning/service/how-to-create-your-first-pipeline).
+
 ### Why build pipelines?
 
 With pipelines, you can optimize your workflow with simplicity, speed, portability, and reuse. When building pipelines with Azure Machine Learning, you can focus on what you know best — machine learning — rather than infrastructure.

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",
@@ -98,12 +94,12 @@
       "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",
-    "store_name=os.getenv(\"ADL_STORENAME_62\", \"<my-datastore-name>\"), # ADLS account name\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",
+        "store_name=os.getenv(\"ADL_STORENAME_62\", \"<my-datastore-name>\") # ADLS account name\n",
         "tenant_id=os.getenv(\"ADL_TENANT_62\", \"<my-tenant-id>\") # tenant id of service principal\n",
         "client_id=os.getenv(\"ADL_CLIENTID_62\", \"<my-client-id>\") # client id of service principal\n",
         "client_secret=os.getenv(\"ADL_CLIENT_SECRET_62\", \"<my-client-secret>\") # the secret of service principal\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",
@@ -201,7 +197,7 @@
         "            print('Data factory not found, creating...')\n",
         "            provisioning_config = DataFactoryCompute.provisioning_configuration()\n",
         "            data_factory = ComputeTarget.create(workspace, factory_name, provisioning_config)\n",
-    "            data_factory.wait_for_provisioning()\n",
+        "            data_factory.wait_for_completion()\n",
         "            return data_factory\n",
         "        else:\n",
         "            raise e\n",

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())"
       ]
     },
     {

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())"
       ]
     },
     {

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)"
@@ -91,12 +89,12 @@
       "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",
-    "store_name=os.getenv(\"ADL_STORENAME_62\", \"<my-datastore-name>\"), # ADLS account name\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",
+        "store_name=os.getenv(\"ADL_STORENAME_62\", \"<my-datastore-name>\") # ADLS account name\n",
         "tenant_id=os.getenv(\"ADL_TENANT_62\", \"<my-tenant-id>\") # tenant id of service principal\n",
         "client_id=os.getenv(\"ADL_CLIENTID_62\", \"<my-client-id>\") # client id of service principal\n",
         "client_secret=os.getenv(\"ADL_CLIENT_62_SECRET\", \"<my-client-secret>\") # the secret of service principal\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",
@@ -229,12 +227,10 @@
         "\n",
         "### Remarks\n",
         "\n",
-    "You can use `@@name@@` syntax in your script to refer to inputs, outputs, resources, and params.\n",
+        "You can use `@@name@@` syntax in your script to refer to inputs, outputs, and params.\n",
         "\n",
         "* if `name` is the name of an input or output port binding, any occurences of `@@name@@` in the script\n",
         "are replaced with actual data path of corresponding port binding.\n",
-    "* if `name` is the name of a resource input port binding, any occurences of `@@name@@` in the script\n",
-    "are replaced with local path of resource after it's downloaded to script directory on a worker node.\n",
         "* if `name` matches any key in `params` dict, any occurences of `@@name@@` will be replaced with\n",
         "corresponding value in dict.\n",
         "\n",
@@ -286,7 +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()"
       ]
     },
@@ -362,7 +358,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+      "version": "3.6.6"
     }
   },
   "nbformat": 4,

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",
@@ -127,7 +127,9 @@
         "\n",
         "- **Resource Group** - The resource group name of your Azure Machine Learning workspace\n",
         "- **Databricks Workspace Name** - The workspace name of your Azure Databricks workspace\n",
-    "- **Databricks Access Token** - The access token you created in ADB"
+        "- **Databricks Access Token** - The access token you created in ADB\n",
+        "\n",
+        "**The Databricks workspace need to be present in the same subscription as your AML workspace**"
       ]
     },
     {
@@ -144,7 +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",
@@ -295,7 +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",
@@ -312,33 +314,74 @@
         "- ***name:** Name of the Module\n",
         "- **inputs:** List of input connections for data consumed by this step. Fetch this inside the notebook using dbutils.widgets.get(\"input\")\n",
         "- **outputs:** List of output port definitions for outputs produced by this step. Fetch this inside the notebook using dbutils.widgets.get(\"output\")\n",
+        "- **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.\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",
+        "- **python_script_path:** The path to the python script in the DBFS or S3. If you are providing this, do not provide python_script_name which is used for uploading script from local machine.\n",
+        "- **python_script_params:** Parameters for the python script (list of str)\n",
+        "- **main_class_name:** The name of the entry point in a JAR module. If you are providing this, do not provide any python script or notebook related parameters.\n",
+        "- **jar_params:** Parameters for the JAR module (list of str)\n",
+        "- **python_script_name:** name of a python script on your local machine (relative to source_directory). If you are providing this do not provide python_script_path which is used to execute a remote python script; or any of the JAR or notebook related parameters.\n",
+        "- **source_directory:** folder that contains the script and other files\n",
+        "- **hash_paths:** list of paths to hash to detect a change in source_directory (script file is always hashed)\n",
         "- **run_name:** Name in databricks for this run\n",
         "- **timeout_seconds:** Timeout for the databricks run\n",
+        "- **runconfig:** Runconfig to use. Either pass runconfig or each library type as a separate parameter but do not mix the two\n",
         "- **maven_libraries:** maven libraries for the databricks run\n",
         "- **pypi_libraries:** pypi libraries for the databricks run\n",
         "- **egg_libraries:** egg libraries for the databricks run\n",
         "- **jar_libraries:** jar libraries for the databricks run\n",
         "- **rcran_libraries:** rcran libraries for the databricks run\n",
-    "- **databricks_compute:** Azure Databricks compute\n",
-    "- **databricks_compute_name:** Name of Azure Databricks compute\n",
+        "- **compute_target:** Azure Databricks compute\n",
+        "- **allow_reuse:** Whether the step should reuse previous results when run with the same settings/inputs\n",
+        "- **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 databricks_compute or databricks_compute_name parameters*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id='notebook_howto'></a>"
+        "*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",
+        "You can use a runconfig to specify the library dependencies for your cluster in Databricks. The runconfig will contain a databricks section as follows:\n",
+        "```yaml\n",
+        "environment:\n",
+        "# Databricks details\n",
+        "  databricks:\n",
+        "# List of maven libraries.\n",
+        "    mavenLibraries:\n",
+        "    - coordinates: org.jsoup:jsoup:1.7.1\n",
+        "      repo: ''\n",
+        "      exclusions:\n",
+        "      - slf4j:slf4j\n",
+        "      - '*:hadoop-client'\n",
+        "# List of PyPi libraries\n",
+        "    pypiLibraries:\n",
+        "    - package: beautifulsoup4\n",
+        "      repo: ''\n",
+        "# List of RCran libraries\n",
+        "    rcranLibraries:\n",
+        "    - package: ada\n",
+        "      repo: http://cran.us.r-project.org\n",
+        "# List of JAR libraries\n",
+        "    jarLibraries:\n",
+        "    - library: dbfs:/mnt/libraries/library.jar\n",
+        "# List of Egg libraries\n",
+        "    eggLibraries:\n",
+        "    - library: dbfs:/mnt/libraries/library.egg\n",
+        "```\n",
+        "\n",
+        "You can then create a RunConfiguration object using this file and pass it as the runconfig parameter to DatabricksStep.\n",
+        "```python\n",
+        "from azureml.core.runconfig import RunConfiguration\n",
+        "\n",
+        "runconfig = RunConfiguration()\n",
+        "runconfig.load(path='<directory_where_runconfig_is_stored>', name='<runconfig_file_name>')\n",
+        "```"
       ]
     },
     {
@@ -346,7 +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:"
       ]
     },
     {
@@ -647,7 +690,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-   "version": "3.6.7"
+      "version": "3.6.2"
     }
   },
   "nbformat": 4,

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())"
       ]
     },
     {

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",

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()"

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

@@ -1,6 +1,15 @@
 ## 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 Pytorch Estimator and tune hyperparameters using Hyperdrive](train-hyperparameter-tune-deploy-with-pytorch)
+4. [Distributed training using TensorFlow and Parameter Server](distributed-tensorflow-with-parameter-server)
+5. [Distributed training using TensorFlow and Horovod](distributed-tensorflow-with-horovod)
+6. [Distributed training using Pytorch and Horovod](distributed-pytorch-with-horovod)
+7. [Distributed training using CNTK and custom Docker image](distributed-cntk-with-custom-docker)
+8. [Export run history records to Tensorboard](export-run-history-to-tensorboard)
+9. [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`."
       ]
     },
     {
@@ -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",

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"
       ]
     },
     {
@@ -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())"
       ]
     },
     {
@@ -251,26 +251,6 @@
         "The above code specifies that we will run our training script on `2` nodes, with one worker per node. In order to execute a distributed run using MPI/Horovod, you must provide the argument `distributed_backend='mpi'`. Using this estimator with these settings, PyTorch, Horovod and their dependencies will be installed for you. However, if your script also uses other packages, make sure to install them via the `PyTorch` constructor's `pip_packages` or `conda_packages` parameters."
       ]
     },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To use the latest version of PyTorch 1.0, run the following cell:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "estimator.conda_dependencies.remove_conda_package('pytorch=0.4.0')\n",
-    "estimator.conda_dependencies.remove_pip_package('horovod==0.13.11')\n",
-    "estimator.conda_dependencies.add_conda_package('pytorch-nightly')\n",
-    "estimator.conda_dependencies.add_channel('pytorch')\n",
-    "estimator.conda_dependencies.add_pip_package('horovod==0.15.2')"
-   ]
-  },
     {
       "cell_type": "markdown",
       "metadata": {},

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

@@ -1,3 +1,8 @@
+# Copyright (c) 2017, PyTorch contributors
+# Modifications copyright (C) Microsoft Corporation
+# Licensed under the BSD license
+# Adapted from https://github.com/uber/horovod/blob/master/examples/pytorch_mnist.py
+
 from __future__ import print_function
 import argparse
 import torch.nn as nn
@@ -11,6 +16,8 @@ from azureml.core.run import Run
 # get the Azure ML run object
 run = Run.get_context()
 
+print("Torch version:", torch.__version__)
+
 # Training settings
 parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
 parser.add_argument('--batch-size', type=int, default=64, metavar='N',
@@ -43,7 +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"
       ]
     },
     {
@@ -91,10 +91,12 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Create a remote compute target\n",
-    "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an `AmlCompute` cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n",
+        "## Create or Attach existing AmlCompute\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
         "\n",
-    "**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process."
+        "**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/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
       ]
     },
     {
@@ -122,8 +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())"
       ]
     },
     {
@@ -236,8 +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"
@@ -91,10 +91,12 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Create a remote compute target\n",
-    "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an `AmlCompute` cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n",
+        "## Create or Attach existing AmlCompute\n",
+        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
         "\n",
-    "**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process."
+        "**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."
       ]
     },
     {
@@ -122,8 +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`)"
       ]
@@ -74,8 +74,7 @@
       "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",
@@ -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`)"
       ]
@@ -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",
@@ -276,9 +275,9 @@
         "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 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 `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",
         "```shell\n",
         "# create a DSVM in your resource group\n",
@@ -294,19 +293,26 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "from azureml.core.compute import DsvmCompute\n",
+        "from azureml.core.compute import RemoteCompute\n",
         "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
-    "compute_target_name = 'cpudsvm'\n",
+        "username = os.getenv('AZUREML_DSVM_USERNAME', default='<my_username>')\n",
+        "address = os.getenv('AZUREML_DSVM_ADDRESS', default='<ip_address_or_fqdn>')\n",
         "\n",
+        "compute_target_name = 'cpudsvm'\n",
+        "# if you want to connect using SSH key instead of username/password you can provide parameters private_key_file and private_key_passphrase \n",
         "try:\n",
-    "    compute_target = DsvmCompute(workspace=ws, name=compute_target_name)\n",
-    "    print('found existing:', compute_target.name)\n",
+        "    attached_dsvm_compute = RemoteCompute(workspace=ws, name=compute_target_name)\n",
+        "    print('found existing:', attached_dsvm_compute.name)\n",
         "except ComputeTargetException:\n",
-    "    print('creating new.')\n",
-    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size=\"Standard_D2_v2\")\n",
-    "    compute_target = DsvmCompute.create(ws, name=compute_target_name, provisioning_configuration=dsvm_config)\n",
-    "compute_target.wait_for_completion(show_output=True)"
+        "    attached_dsvm_compute = RemoteCompute.attach(workspace=ws,\n",
+        "                                                 name=compute_target_name,\n",
+        "                                                 username=username,\n",
+        "                                                 address=address,\n",
+        "                                                 ssh_port=22,\n",
+        "                                                 private_key_file='./.ssh/id_rsa')\n",
+        "    \n",
+        "    attached_dsvm_compute.wait_for_completion(show_output=True)"
       ]
     },
     {
@@ -332,7 +338,7 @@
         "# script_params[\"--max_steps\"] = \"5000\"\n",
         "\n",
         "tf_estimator = TensorFlow(source_directory=exp_dir,\n",
-    "                          compute_target=compute_target,\n",
+        "                          compute_target=attached_dsvm_compute,\n",
         "                          entry_script='mnist_with_summaries.py',\n",
         "                          script_params=script_params)\n",
         "\n",
@@ -397,7 +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",
@@ -415,8 +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-pytorch/pytorch_train.py

@@ -182,6 +182,8 @@ def download_data():
 
 
 def main():
+    print("Torch version:", torch.__version__)
+
     # get command-line arguments
     parser = argparse.ArgumentParser()
     parser.add_argument('--num_epochs', type=int, default=25,

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

@@ -15,7 +15,7 @@
       "source": [
         "# Train, hyperparameter tune, and deploy with PyTorch\n",
         "\n",
-    "In this tutorial, you will train, hyperparameter tune, and deploy a PyTorch model using the Azure Machine Learning (AML) Python SDK.\n",
+        "In this tutorial, you will train, hyperparameter tune, and deploy a PyTorch model using the Azure Machine Learning (Azure ML) Python SDK.\n",
         "\n",
         "This tutorial will train an image classification model using transfer learning, based on PyTorch's [Transfer Learning tutorial](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html). The model is trained to classify ants and bees by first using a pretrained ResNet18 model that has been trained on the [ImageNet](http://image-net.org/index) dataset."
       ]
@@ -25,10 +25,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-    "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
-    "* Go through the [00.configuration.ipynb](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) notebook to:\n",
-    "    * install the AML SDK\n",
-    "    * create a workspace and its configuration file (`config.json`)"
+        "* Go through the [Configuration](../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`"
       ]
     },
     {
@@ -93,10 +90,12 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Create a remote compute target\n",
-    "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an `AmlCompute` cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n",
+        "## 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, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource.\n",
         "\n",
-    "**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process."
+        "**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."
       ]
     },
     {
@@ -124,8 +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())"
       ]
     },
     {
@@ -176,11 +175,11 @@
       "metadata": {},
       "source": [
         "### Prepare training script\n",
-    "Now you will need to create your training script. In this tutorial, the training script is already provided for you at `pytorch_train.py`. In practice, you should be able to take any custom training script as is and run it with AML without having to modify your code.\n",
+        "Now you will need to create your training script. In this tutorial, the training script is already provided for you at `pytorch_train.py`. In practice, you should be able to take any custom training script as is and run it with Azure ML without having to modify your code.\n",
         "\n",
-    "However, if you would like to use AML's [tracking and metrics](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#metrics) capabilities, you will have to add a small amount of AML code inside your training script. \n",
+        "However, if you would like to use Azure ML's [tracking and metrics](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#metrics) capabilities, you will have to add a small amount of Azure ML code inside your training script. \n",
         "\n",
-    "In `pytorch_train.py`, we will log some metrics to our AML run. To do so, we will access the AML run object within the script:\n",
+        "In `pytorch_train.py`, we will log some metrics to our Azure ML run. To do so, we will access the Azure ML `Run` object within the script:\n",
         "```Python\n",
         "from azureml.core.run import Run\n",
         "run = Run.get_context()\n",
@@ -238,7 +237,7 @@
       "metadata": {},
       "source": [
         "### Create a PyTorch estimator\n",
-    "The AML SDK's PyTorch estimator enables you to easily submit PyTorch training jobs for both single-node and distributed runs. For more information on the PyTorch estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-pytorch). The following code will define a single-node PyTorch job."
+        "The Azure ML SDK's PyTorch estimator enables you to easily submit PyTorch training jobs for both single-node and distributed runs. For more information on the PyTorch estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-pytorch). The following code will define a single-node PyTorch job."
       ]
     },
     {
@@ -267,7 +266,7 @@
       "source": [
         "The `script_params` parameter is a dictionary containing the command-line arguments to your training script `entry_script`. Please note the following:\n",
         "- We passed our training data reference `ds_data` to our script's `--data_dir` argument. This will 1) mount our datastore on the remote compute and 2) provide the path to the training data `hymenoptera_data` on our datastore.\n",
-    "- We specified the output directory as `./outputs`. The `outputs` directory is specially treated by AML in that all the content in this directory gets uploaded to your workspace as part of your run history. The files written to this directory are therefore accessible even once your remote run is over. In this tutorial, we will save our trained model to this output directory.\n",
+        "- We specified the output directory as `./outputs`. The `outputs` directory is specially treated by Azure ML in that all the content in this directory gets uploaded to your workspace as part of your run history. The files written to this directory are therefore accessible even once your remote run is over. In this tutorial, we will save our trained model to this output directory.\n",
         "\n",
         "To leverage the Azure VM's GPU for training, we set `use_gpu=True`."
       ]
@@ -360,7 +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",
@@ -410,8 +409,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "from azureml.widgets import RunDetails\n",
-    "\n",
         "RunDetails(hyperdrive_run).show()"
       ]
     },
@@ -506,7 +503,7 @@
       "metadata": {},
       "source": [
         "### Create environment file\n",
-    "Then, we will need to create an environment file (`myenv.yml`) that specifies all of the scoring script's package dependencies. This file is used to ensure that all of those dependencies are installed in the Docker image by AML. In this case, we need to specify `azureml-core`, `torch` and `torchvision`."
+        "Then, we will need to create an environment file (`myenv.yml`) that specifies all of the scoring script's package dependencies. This file is used to ensure that all of those dependencies are installed in the Docker image by Azure ML. In this case, we need to specify `azureml-core`, `torch` and `torchvision`."
       ]
     },
     {
@@ -650,7 +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",
@@ -258,15 +254,15 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Create a remote compute target\n",
-    "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an `AmlCompute` cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace."
+        "## 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 in the previous cell, 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",
+        "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"
@@ -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

@@ -4,5 +4,7 @@ Follow these sample notebooks to learn:
 
 1. [Train within notebook](train-within-notebook): train a simple scikit-learn model using the Jupyter kernel and deploy the model to Azure Container Service.
 2. [Train on local](train-on-local): train a model using local computer as compute target.
-3. [Train on remove VM](train-on-remote-vm): train a model using a remote Azure VM as compute target.
-4. [Logging API](logging-api): experiment with various logging functions to create runs and automatically generate graphs.
+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. [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 [Configuration](../../../configuration.ipynb) Notebook first if you haven't. Also make sure you have tqdm and matplotlib installed in the current kernel.\n",
+        "Make sure you go through the [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

@@ -0,0 +1,522 @@
+{
+  "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": [
+        "#  Train using Azure Machine Learning Compute\n",
+        "\n",
+        "* Initialize a Workspace\n",
+        "* Create an Experiment\n",
+        "* Introduction to AmlCompute\n",
+        "* Submit an AmlCompute run in a few different ways\n",
+        "    - Provision as a run based compute target \n",
+        "    - Provision as a persistent compute target (Basic)\n",
+        "    - Provision as a persistent compute target (Advanced)\n",
+        "* Additional operations to perform on AmlCompute\n",
+        "* Find the best model in the run"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prerequisites\n",
+        "Make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Check core SDK version number\n",
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize a Workspace\n",
+        "\n",
+        "Initialize a workspace object from persisted configuration"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "create workspace"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create An Experiment\n",
+        "\n",
+        "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "experiment_name = 'train-on-amlcompute'\n",
+        "experiment = Experiment(workspace = ws, name = experiment_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Introduction to AmlCompute\n",
+        "\n",
+        "Azure Machine Learning Compute is managed compute infrastructure that allows the user to easily create single to multi-node compute of the appropriate VM Family. It is created **within your workspace region** and is a resource that can be used by other users in your workspace. It autoscales by default to the max_nodes, when a job is submitted, and executes in a containerized environment packaging the dependencies as specified by the user. \n",
+        "\n",
+        "Since it is managed compute, job scheduling and cluster management are handled internally by Azure Machine Learning service. \n",
+        "\n",
+        "For more information on Azure Machine Learning Compute, please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)\n",
+        "\n",
+        "If you are an existing BatchAI customer who is migrating to Azure Machine Learning, please read [this article](https://aka.ms/batchai-retirement)\n",
+        "\n",
+        "**Note**: As with other Azure services, there are limits on certain resources (for eg. AmlCompute quota) 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.\n",
+        "\n",
+        "\n",
+        "The training script `train.py` is already created for you. Let's have a look."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Submit an AmlCompute run in a few different ways\n",
+        "\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 [configuration notebook](../../../configuration.ipynb)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "\n",
+        "AmlCompute.supported_vmsizes(workspace = ws)\n",
+        "#AmlCompute.supported_vmsizes(workspace = ws, location='southcentralus')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create project directory\n",
+        "\n",
+        "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import shutil\n",
+        "\n",
+        "project_folder = './train-on-amlcompute'\n",
+        "os.makedirs(project_folder, exist_ok=True)\n",
+        "shutil.copy('train.py', project_folder)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Provision as a run based compute target\n",
+        "\n",
+        "You can provision AmlCompute as a compute target at run-time. In this case, the compute is auto-created for your run, scales up to max_nodes that you specify, and then **deleted automatically** after the run completes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.runconfig import RunConfiguration\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "from azureml.core.runconfig import DEFAULT_CPU_IMAGE\n",
+        "\n",
+        "# create a new runconfig object\n",
+        "run_config = RunConfiguration()\n",
+        "\n",
+        "# signal that you want to use AmlCompute to execute script.\n",
+        "run_config.target = \"amlcompute\"\n",
+        "\n",
+        "# AmlCompute will be created in the same region as workspace\n",
+        "# Set vm size for AmlCompute\n",
+        "run_config.amlcompute.vm_size = 'STANDARD_D2_V2'\n",
+        "\n",
+        "# enable Docker \n",
+        "run_config.environment.docker.enabled = True\n",
+        "\n",
+        "# set Docker base image to the default CPU-based image\n",
+        "run_config.environment.docker.base_image = DEFAULT_CPU_IMAGE\n",
+        "\n",
+        "# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
+        "run_config.environment.python.user_managed_dependencies = False\n",
+        "\n",
+        "# auto-prepare the Docker image when used for execution (if it is not already prepared)\n",
+        "run_config.auto_prepare_environment = True\n",
+        "\n",
+        "# specify CondaDependencies obj\n",
+        "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
+        "\n",
+        "# Now submit a run on AmlCompute\n",
+        "from azureml.core.script_run_config import ScriptRunConfig\n",
+        "\n",
+        "script_run_config = ScriptRunConfig(source_directory=project_folder,\n",
+        "                                    script='train.py',\n",
+        "                                    run_config=run_config)\n",
+        "\n",
+        "run = experiment.submit(script_run_config)\n",
+        "\n",
+        "# Show run details\n",
+        "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,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "# Shows output of the run on stdout.\n",
+        "run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.get_metrics()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Provision as a persistent compute target (Basic)\n",
+        "\n",
+        "You can provision a persistent AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n",
+        "\n",
+        "* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n",
+        "* `max_nodes`: Maximum nodes to autoscale to while running a job on AmlCompute"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# Choose a name for your CPU cluster\n",
+        "cpu_cluster_name = \"cpucluster\"\n",
+        "\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
+        "    print('Found existing cluster, use it.')\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
+        "                                                           max_nodes=4)\n",
+        "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
+        "\n",
+        "cpu_cluster.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Configure & Run"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.runconfig import RunConfiguration\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "\n",
+        "# create a new RunConfig object\n",
+        "run_config = RunConfiguration(framework=\"python\")\n",
+        "\n",
+        "# Set compute target to AmlCompute target created in previous step\n",
+        "run_config.target = cpu_cluster.name\n",
+        "\n",
+        "# enable Docker \n",
+        "run_config.environment.docker.enabled = True\n",
+        "\n",
+        "# specify CondaDependencies obj\n",
+        "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
+        "\n",
+        "from azureml.core import Run\n",
+        "from azureml.core import ScriptRunConfig\n",
+        "\n",
+        "src = ScriptRunConfig(source_directory=project_folder, \n",
+        "                      script='train.py', \n",
+        "                      run_config=run_config) \n",
+        "run = experiment.submit(config=src)\n",
+        "run"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "# Shows output of the run on stdout.\n",
+        "run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.get_metrics()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Provision as a persistent compute target (Advanced)\n",
+        "\n",
+        "You can also specify additional properties or change defaults while provisioning AmlCompute using a more advanced configuration. This is useful when you want a dedicated cluster of 4 nodes (for example you can set the min_nodes and max_nodes to 4), or want the compute to be within an existing VNet in your subscription.\n",
+        "\n",
+        "In addition to `vm_size` and `max_nodes`, you can specify:\n",
+        "* `min_nodes`: Minimum nodes (default 0 nodes) to downscale to while running a job on AmlCompute\n",
+        "* `vm_priority`: Choose between 'dedicated' (default) and 'lowpriority' VMs when provisioning AmlCompute. Low Priority VMs use Azure's excess capacity and are thus cheaper but risk your run being pre-empted\n",
+        "* `idle_seconds_before_scaledown`: Idle time (default 120 seconds) to wait after run completion before auto-scaling to min_nodes\n",
+        "* `vnet_resourcegroup_name`: Resource group of the **existing** VNet within which AmlCompute should be provisioned\n",
+        "* `vnet_name`: Name of VNet\n",
+        "* `subnet_name`: Name of SubNet within the VNet"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# Choose a name for your CPU cluster\n",
+        "cpu_cluster_name = \"cpucluster\"\n",
+        "\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
+        "    print('Found existing cluster, use it.')\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
+        "                                                           vm_priority='lowpriority',\n",
+        "                                                           min_nodes=2,\n",
+        "                                                           max_nodes=4,\n",
+        "                                                           idle_seconds_before_scaledown='300',\n",
+        "                                                           vnet_resourcegroup_name='<my-resource-group>',\n",
+        "                                                           vnet_name='<my-vnet-name>',\n",
+        "                                                           subnet_name='<my-subnet-name>')\n",
+        "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
+        "\n",
+        "cpu_cluster.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Configure & Run"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.runconfig import RunConfiguration\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "\n",
+        "# create a new RunConfig object\n",
+        "run_config = RunConfiguration(framework=\"python\")\n",
+        "\n",
+        "# Set compute target to AmlCompute target created in previous step\n",
+        "run_config.target = cpu_cluster.name\n",
+        "\n",
+        "# enable Docker \n",
+        "run_config.environment.docker.enabled = True\n",
+        "\n",
+        "# specify CondaDependencies obj\n",
+        "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
+        "\n",
+        "from azureml.core import Run\n",
+        "from azureml.core import ScriptRunConfig\n",
+        "\n",
+        "src = ScriptRunConfig(source_directory=project_folder, \n",
+        "                      script='train.py', \n",
+        "                      run_config=run_config) \n",
+        "run = experiment.submit(config=src)\n",
+        "run"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "# Shows output of the run on stdout.\n",
+        "run.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.get_metrics()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Additional operations to perform on AmlCompute\n",
+        "\n",
+        "You can perform more operations on AmlCompute such as updating the node counts or deleting the compute. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#Get_status () gets the latest status of the AmlCompute target\n",
+        "cpu_cluster.get_status().serialize()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#Update () takes in the min_nodes, max_nodes and idle_seconds_before_scaledown and updates the AmlCompute target\n",
+        "#cpu_cluster.update(min_nodes=1)\n",
+        "#cpu_cluster.update(max_nodes=10)\n",
+        "cpu_cluster.update(idle_seconds_before_scaledown=300)\n",
+        "#cpu_cluster.update(min_nodes=2, max_nodes=4, idle_seconds_before_scaledown=600)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#Delete () is used to deprovision and delete the AmlCompute target. Useful if you want to re-use the compute name \n",
+        "#'cpucluster' in this case but use a different VM family for instance.\n",
+        "\n",
+        "#cpu_cluster.delete()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Success!\n",
+        "Great, you are ready to move on to the remaining notebooks."
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "nigup"
+      }
+    ],
+    "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/training/train-on-amlcompute/train.py

@@ -0,0 +1,44 @@
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+from sklearn.datasets import load_diabetes
+from sklearn.linear_model import Ridge
+from sklearn.metrics import mean_squared_error
+from sklearn.model_selection import train_test_split
+from azureml.core.run import Run
+from sklearn.externals import joblib
+import os
+import numpy as np
+
+os.makedirs('./outputs', exist_ok=True)
+
+X, y = load_diabetes(return_X_y=True)
+
+run = Run.get_context()
+
+X_train, X_test, y_train, y_test = train_test_split(X, y,
+                                                    test_size=0.2,
+                                                    random_state=0)
+data = {"train": {"X": X_train, "y": y_train},
+        "test": {"X": X_test, "y": y_test}}
+
+# list of numbers from 0.0 to 1.0 with a 0.05 interval
+alphas = np.arange(0.0, 1.0, 0.05)
+
+for alpha in alphas:
+    # Use Ridge algorithm to create a regression model
+    reg = Ridge(alpha=alpha)
+    reg.fit(data["train"]["X"], data["train"]["y"])
+
+    preds = reg.predict(data["test"]["X"])
+    mse = mean_squared_error(preds, data["test"]["y"])
+    run.log('alpha', alpha)
+    run.log('mse', mse)
+
+    model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)
+    # save model in the outputs folder so it automatically get uploaded
+    with open(model_file_name, "wb") as file:
+        joblib.dump(value=reg, filename=os.path.join('./outputs/',
+                                                     model_file_name))
+
+    print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))

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

@@ -29,7 +29,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-    "Make sure you go through the [Configuration](../../../configuration.ipynb) Notebook first if you haven't."
+        "Make sure you go through the [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

@@ -16,7 +16,7 @@
         "# 04. Train in a remote Linux VM\n",
         "* Create Workspace\n",
         "* Create `train.py` file\n",
-    "* Create (or attach) DSVM as compute resource.\n",
+        "* Create and Attach a Remote VM (eg. DSVM) as compute resource.\n",
         "* Upoad data files into default datastore\n",
         "* Configure & execute a run in a few different ways\n",
         "    - Use system-built conda\n",
@@ -30,7 +30,7 @@
       "metadata": {},
       "source": [
         "## Prerequisites\n",
-    "Make sure you go through the [Configuration](../../../configuration.ipynb) Notebook first if you haven't."
+        "Make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't."
       ]
     },
     {
@@ -188,9 +188,17 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "## Create Linux DSVM as a compute target\n",
+        "## Create and Attach a DSVM as a compute target\n",
         "\n",
-    "**Note**: If creation fails with a message about Marketplace purchase eligibilty, go to portal.azure.com, start creating DSVM there, and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled it, you can exit without actually creating VM.\n",
+        "**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",
+        "# note you need to be at least a contributor to the resource group in order to execute this command successfully\n",
+        "(myenv) $ az vm create --resource-group <resource_group_name> --name <some_vm_name> --image microsoft-dsvm:linux-data-science-vm-ubuntu:linuxdsvmubuntu:latest --admin-username <username> --admin-password <password> --generate-ssh-keys --authentication-type password\n",
+        "```\n",
+        "\n",
+        "**Note**: You can also use [this url](https://portal.azure.com/#create/microsoft-dsvm.linux-data-science-vm-ubuntulinuxdsvmubuntu) to create the VM using the Azure Portal\n",
         "\n",
         "**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you switch to a different port (such as 5022), you can specify the port number in the provisioning configuration object."
       ]
@@ -201,52 +209,26 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "from azureml.core.compute import DsvmCompute\n",
+        "from azureml.core.compute import ComputeTarget, RemoteCompute\n",
         "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
+        "username = os.getenv('AZUREML_DSVM_USERNAME', default='<my_username>')\n",
+        "address = os.getenv('AZUREML_DSVM_ADDRESS', default='<ip_address_or_fqdn>')\n",
+        "\n",
         "compute_target_name = 'cpudsvm'\n",
-    "\n",
+        "# if you want to connect using SSH key instead of username/password you can provide parameters private_key_file and private_key_passphrase \n",
         "try:\n",
-    "    dsvm_compute = DsvmCompute(workspace=ws, name=compute_target_name)\n",
-    "    print('found existing:', dsvm_compute.name)\n",
+        "    attached_dsvm_compute = RemoteCompute(workspace=ws, name=compute_target_name)\n",
+        "    print('found existing:', attached_dsvm_compute.name)\n",
         "except ComputeTargetException:\n",
-    "    print('creating new.')\n",
-    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size=\"Standard_D2_v2\")\n",
-    "    dsvm_compute = DsvmCompute.create(ws, name=compute_target_name, provisioning_configuration=dsvm_config)\n",
-    "    dsvm_compute.wait_for_completion(show_output=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Attach an existing Linux DSVM\n",
-    "You can also attach an existing Linux VM as a compute target. To create one, you can use Azure CLI command:\n",
-    "\n",
-    "```\n",
-    "az vm create -n cpudsvm -l eastus2 -g <my-resource-group> --size Standard_D2_v2 --image microsoft-dsvm:linux-data-science-vm-ubuntu:linuxdsvmubuntu:latest --generate-ssh-keys\n",
-    "```\n",
-    "\n",
-    "The ```--generate-ssh-keys``` automatically places the ssh keys to standard location, typically to ~/.ssh folder. The default port is 22."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "'''\n",
-    "from azureml.core.compute import ComputeTarget, RemoteCompute \n",
-    "attach_config = RemoteCompute.attach_configuration(username='<my_username>',\n",
-    "                                                   address='<ip_adress_or_fqdn>',\n",
+        "    attach_config = RemoteCompute.attach_configuration(address=address,\n",
         "                                                        ssh_port=22,\n",
+        "                                                        username=username,\n",
         "                                                        private_key_file='./.ssh/id_rsa')\n",
         "    attached_dsvm_compute = ComputeTarget.attach(workspace=ws,\n",
-    "                                             name='attached_vm',\n",
-    "                                             attach_configuration=attach_config)\n",
-    "attached_dsvm_compute.wait_for_completion(show_output=True)\n",
-    "'''\n"
+        "                                                 name=compute_target_name,\n",
+        "                                                 attach_config=attach_config)\n",
+        "    attached_dsvm_compute.wait_for_completion(show_output=True)"
       ]
     },
     {
@@ -298,7 +280,7 @@
         "conda_run_config = RunConfiguration(framework=\"python\")\n",
         "\n",
         "# Set compute target to the Linux DSVM\n",
-    "conda_run_config.target = dsvm_compute.name\n",
+        "conda_run_config.target = attached_dsvm_compute.name\n",
         "\n",
         "# set the data reference of the run configuration\n",
         "conda_run_config.data_references = {ds.name: dr}\n",
@@ -313,7 +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",
@@ -325,6 +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,
@@ -368,7 +356,7 @@
         "vm_run_config = RunConfiguration(framework=\"python\")\n",
         "\n",
         "# Set compute target to the Linux DSVM\n",
-    "vm_run_config.target = dsvm_compute.name\n",
+        "vm_run_config.target = attached_dsvm_compute.name\n",
         "\n",
         "# set the data reference of the run coonfiguration\n",
         "conda_run_config.data_references = {ds.name: dr}\n",
@@ -403,7 +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."
       ]
     },
     {
@@ -412,11 +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())"
       ]
     },
     {
@@ -469,15 +457,11 @@
       "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",
         "# Set compute target to the Linux DSVM\n",
-    "docker_run_config.target = dsvm_compute.name\n",
+        "docker_run_config.target = attached_dsvm_compute.name\n",
         "\n",
         "# Use Docker in the remote VM\n",
         "docker_run_config.environment.docker.enabled = True\n",
@@ -524,6 +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.ipynb) notebook to set up your Azure Machine Learning workspace and ensure other common prerequisites are met.  From the configuration, the important sections are the workspace configuration and ACI regristration.\n",
+        "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/README.md

@@ -0,0 +1,11 @@
+## Azure Machine Learning service Tutorial
+
+Complete these tutorials to learn how to train and deploy models using Azure Machine Learning services and Python SDK. These Notebooks accompany the [tutorial articles starting here]([https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-train-models-with-aml]).
+
+As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) notebook first to set up your Azure ML Workspace. Then, run the notebooks in following recommended order.
+
+ * [Tutorial #1](img-classification-part1-training.ipynb): Train an image classification model with Azure Machine Learning
+ * [Tutorial #2](img-classification-part2-deploy.ipynb):  Deploy an image classification model from first tutorial in Azure Container Instance (ACI)
+ * [Tutorial #3](regression-part1-data-prep.ipynb): Use data preparation.
+
+ Also find quickstarts and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/).

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-part1-data-prep.ipynb

@@ -13,7 +13,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "# Tutorial #1: Prepare data for regression modeling"
+        "# Tutorial (part 1): Prepare data for regression modeling"
       ]
     },
     {
@@ -101,7 +101,7 @@
         "all_columns = dprep.ColumnSelector(term=\".*\", use_regex=True)\n",
         "drop_if_all_null = [all_columns, dprep.ColumnRelationship(dprep.ColumnRelationship.ALL)]\n",
         "useful_columns = [\n",
-    "    \"cost\", \"distance\"\"distance\", \"dropoff_datetime\", \"dropoff_latitude\", \"dropoff_longitude\",\n",
+        "    \"cost\", \"distance\", \"dropoff_datetime\", \"dropoff_latitude\", \"dropoff_longitude\",\n",
         "    \"passengers\", \"pickup_datetime\", \"pickup_latitude\", \"pickup_longitude\", \"store_forward\", \"vendor\"\n",
         "]"
       ]
@@ -337,6 +337,23 @@
         "combined_df = combined_df.replace(columns=\"store_forward\", find=\"0\", replace_with=\"N\").fill_nulls(\"store_forward\", \"N\")"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Execute another `replace` function, this time on the `distance` field. This reformats distance values that are incorrectly labeled as `.00`, and fills any nulls with zeros. Convert the `distance` field to numerical format."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "combined_df = combined_df.replace(columns=\"distance\", find=\".00\", replace_with=0).fill_nulls(\"distance\", 0)\n",
+        "combined_df = combined_df.to_number([\"distance\"])"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -507,6 +524,23 @@
         "tmp_df.get_profile()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Before packaging the dataflow, perform two final filters on the data set. To eliminate incorrect data points, filter the dataflow on records where both the `cost` and `distance` are greater than zero."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "tmp_df = tmp_df.filter(dprep.col(\"distance\") > 0)\n",
+        "tmp_df = tmp_df.filter(dprep.col(\"cost\") > 0)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -520,9 +554,12 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "import os\n",
+        "file_path = os.path.join(os.getcwd(), \"dflows.dprep\")\n",
+        "\n",
         "dflow_prepared = tmp_df\n",
         "package = dprep.Package([dflow_prepared])\n",
-    "package.save(\".\\dflows\")"
+        "package.save(file_path)"
       ]
     },
     {
@@ -536,7 +573,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "Delete the file `dflows` (whether you are running locally or in Azure Notebooks) in your current directory if you do not wish to continue with part two of the tutorial. If you continue on to part two, you will need the `dflows` file in the current directory."
+        "Delete the file `dflows.dprep` (whether you are running locally or in Azure Notebooks) in your current directory if you do not wish to continue with part two of the tutorial. If you continue on to part two, you will need the `dflows.dprep` file in the current directory."
       ]
     },
     {
@@ -571,9 +608,9 @@
       }
     ],
     "kernelspec": {
-   "display_name": "Python 3",
+      "display_name": "Python 3.6",
       "language": "python",
-   "name": "python3"
+      "name": "python36"
     },
     "language_info": {
       "codemirror_mode": {

tutorials/regression-part2-automated-ml.ipynb

@@ -11,7 +11,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "# Tutorial #2: Train a regression model with automated machine learning\n",
+        "# Tutorial (part 2): Use automated machine learning to build your regression model \n",
         "\n",
         "This tutorial is **part two of a two-part tutorial series**. In the previous tutorial, you [prepared the NYC taxi data for regression modeling](regression-part1-data-prep.ipynb).\n",
         "\n",
@@ -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 +111,10 @@
       "outputs": [],
       "source": [
         "import azureml.dataprep as dprep\n",
-    "package_saved = dprep.Package.open(\".\\dflow\")\n",
+        "\n",
+        "file_path = os.path.join(os.getcwd(), \"dflows.dprep\")\n",
+        "\n",
+        "package_saved = dprep.Package.open(file_path)\n",
         "dflow_prepared = package_saved.dataflows[0]\n",
         "dflow_prepared.get_profile()"
       ]
@@ -130,7 +132,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "dflow_X = dflow_prepared.keep_columns(['pickup_weekday', 'dropoff_latitude', 'dropoff_longitude','pickup_hour','pickup_longitude','pickup_latitude','passengers'])\n",
+        "dflow_X = dflow_prepared.keep_columns(['pickup_weekday','pickup_hour', 'distance','passengers', 'vendor'])\n",
         "dflow_y = dflow_prepared.keep_columns('cost')"
       ]
     },
@@ -155,7 +157,7 @@
         "x_df = dflow_X.to_pandas_dataframe()\n",
         "y_df = dflow_y.to_pandas_dataframe()\n",
         "\n",
-    "x_train, x_test, y_train, y_test = train_test_split(x_df, y_df, test_size=0.2, random_state=123)\n",
+        "x_train, x_test, y_train, y_test = train_test_split(x_df, y_df, test_size=0.2, random_state=223)\n",
         "# flatten y_train to 1d array\n",
         "y_train.values.flatten()"
       ]
@@ -304,7 +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"
       ]
@@ -347,7 +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"
       ]
     },
     {
@@ -373,7 +374,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-    "Compare the predicted cost values with the actual cost values. Use the `y_test` dataframe, and convert it to a list to compare to the predicted values. The function `mean_absolute_error` takes two arrays of values, and calculates the average absolute value error between them. In this example, a mean absolute error of 3.5 would mean that on average, the model predicts the cost within plus or minus 3.5 of the actual value."
+        "Create a scatter plot to visualize the predicted cost values compared to the actual cost values. The following code uses the `distance` feature as the x-axis, and trip `cost` as the y-axis. The first 100 predicted and actual cost values are created as separate series, in order to compare the variance of predicted cost at each trip distance value. Examining the plot shows that the distance/cost relationship is nearly linear, and the predicted cost values are in most cases very close to the actual cost values for the same trip distance."
       ]
     },
     {
@@ -382,10 +383,44 @@
       "metadata": {},
       "outputs": [],
       "source": [
-    "from sklearn.metrics import mean_absolute_error\n",
+        "import matplotlib.pyplot as plt\n",
         "\n",
+        "fig = plt.figure(figsize=(14, 10))\n",
+        "ax1 = fig.add_subplot(111)\n",
+        "\n",
+        "distance_vals = [x[4] for x in x_test.values]\n",
         "y_actual = y_test.values.flatten().tolist()\n",
-    "mean_absolute_error(y_actual, y_predict)"
+        "\n",
+        "ax1.scatter(distance_vals[:100], y_predict[:100], s=18, c='b', marker=\"s\", label='Predicted')\n",
+        "ax1.scatter(distance_vals[:100], y_actual[:100], s=18, c='r', marker=\"o\", label='Actual')\n",
+        "\n",
+        "ax1.set_xlabel('distance (mi)')\n",
+        "ax1.set_title('Predicted and Actual Cost/Distance')\n",
+        "ax1.set_ylabel('Cost ($)')\n",
+        "\n",
+        "plt.legend(loc='upper left', prop={'size': 12})\n",
+        "plt.rcParams.update({'font.size': 14})\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Calculate the `root mean squared error` of the results. Use the `y_test` dataframe, and convert it to a list to compare to the predicted values. The function `mean_squared_error` takes two arrays of values, and calculates the average squared error between them. Taking the square root of the result gives an error in the same units as the y variable (cost), and indicates roughly how far your predictions are from the actual value. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.metrics import mean_squared_error\n",
+        "from math import sqrt\n",
+        "\n",
+        "rmse = sqrt(mean_squared_error(y_actual, y_predict))\n",
+        "rmse"
       ]
     },
     {
@@ -433,7 +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)."
       ]
     }
   ],
@@ -444,9 +479,9 @@
       }
     ],
     "kernelspec": {
-   "display_name": "Python 3",
+      "display_name": "Python 3.6",
       "language": "python",
-   "name": "python3"
+      "name": "python36"
     },
     "language_info": {
       "codemirror_mode": {