update samples from Release-129 as a part of 1.40.0 SDK stable release

update samples from Release-128 as a part of  SDK release

00.configuration.ipynb

@@ -1,223 +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": [
-    "# 00. Installation and configuration\n",
-    "\n",
-    "## Prerequisites:\n",
-    "\n",
-    "### 1. Install Azure ML SDK\n",
-    "Follow [SDK installation instructions](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment).\n",
-    "\n",
-    "### 2. Install some additional packages\n",
-    "This Notebook requires some additional libraries. In the conda environment, run below commands: \n",
-    "```shell\n",
-    "(myenv) $ conda install -y matplotlib tqdm scikit-learn\n",
-    "```\n",
-    "\n",
-    "### 3. Make sure your subscription is registered to use ACI.\n",
-    "This Notebook makes use of Azure Container Instance (ACI). You need to ensure your subscription has been registered to use ACI in order be able to deploy a dev/test web service.\n",
-    "```shell\n",
-    "# check to see if ACI is already registered\n",
-    "(myenv) $ az provider show -n Microsoft.ContainerInstance -o table\n",
-    "\n",
-    "# if ACI is not registered, run this command.\n",
-    "# note you need to be the subscription owner in order to execute this command successfully.\n",
-    "(myenv) $ az provider register -n Microsoft.ContainerInstance\n",
-    "```\n",
-    "\n",
-    "In this example you will optionally create an Azure Machine Learning Workspace and initialize your notebook directory to easily use this workspace.  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",
-    "\n",
-    "This notebook also contains optional cells to install and update the require Azure Machine Learning libraries."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "install"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# Check core SDK version number for debugging purposes\n",
-    "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 AML Workspace is an Azure resource that organaizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an AML Workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, operationalization, and the monitoring of operationalized models.\n",
-    "\n",
-    "### What do I need\n",
-    "\n",
-    "In order to use an AML Workspace, first you need access to an Azure Subscription.  You can [create your own](https://azure.microsoft.com/en-us/free/) or get your existing subscription information from the [Azure portal](https://portal.azure.com).  Inside your subscription, you will need access to a _resource group_, which organizes Azure resources and provides a default region for the resources in a group.  You can see what resource groups to which you have access, or create a new one in the [Azure portal](https://portal.azure.com)\n",
-    "\n",
-    "You can also easily create a new resource group using azure-cli.\n",
-    "\n",
-    "```sh\n",
-    "(myenv) $ az group create -n my_resource_group -l eastus2\n",
-    "```\n",
-    "\n",
-    "To create or access an Azure ML Workspace, you will need to import the AML library and the following information:\n",
-    "* A name for your workspace\n",
-    "* Your subscription id\n",
-    "* The resource group name\n",
-    "\n",
-    "**Note**: As with other Azure services, there are limits on certain resources (for eg. BatchAI cluster size) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Supported Azure Regions\n",
-    "Please specify the Azure subscription Id, resource group name, workspace name, and the region in which you want to create the workspace, for example \"eastus2\". "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "subscription_id = os.environ.get(\"SUBSCRIPTION_ID\", \"<my-subscription-id>\")\n",
-    "resource_group = os.environ.get(\"RESOURCE_GROUP\", \"<my-rg>\")\n",
-    "workspace_name = os.environ.get(\"WORKSPACE_NAME\", \"<my-workspace>\")\n",
-    "workspace_region = os.environ.get(\"WORKSPACE_REGION\", \"eastus2\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Creating a workspace\n",
-    "If you already have access to an AML Workspace you want to use, you can skip this cell.  Otherwise, this cell will create an AML workspace for you in a subscription provided you have the correct permissions.\n",
-    "\n",
-    "This will fail when:\n",
-    "1. You do not have permission to create a workspace in the resource group\n",
-    "2. You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription\n",
-    "\n",
-    "If workspace creation fails, please work with your IT admin to provide you with the appropriate permissions or to provision the required resources."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "create workspace"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# import the Workspace class and check the azureml 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": {
-    "tags": [
-     "create workspace"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "ws = Workspace(workspace_name = workspace_name,\n",
-    "               subscription_id = subscription_id,\n",
-    "               resource_group = resource_group)\n",
-    "\n",
-    "# persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
-    "ws.write_config()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can then load the workspace from this config file from any notebook in the current directory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "create workspace"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# load workspace configuratio from ./aml_config/config.json file.\n",
-    "my_workspace = Workspace.from_config()\n",
-    "my_workspace.get_details()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Success!\n",
-    "Great, you are ready to move on to the rest of the sample notebooks."
-   ]
-  }
- ],
- "metadata": {
-  "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": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

01.getting-started/01.train-within-notebook/01.train-within-notebook.ipynb

@@ -1,810 +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": [
-    "# 01. Train in the Notebook & Deploy Model to ACI\n",
-    "\n",
-    "* Load workspace\n",
-    "* Train a simple regression model directly in the Notebook python kernel\n",
-    "* Record run history\n",
-    "* Find the best model in run history and download it.\n",
-    "* Deploy the model as an Azure Container Instance (ACI)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prerequisites\n",
-    "1. Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't. \n",
-    "\n",
-    "2. Install following pre-requisite libraries to your conda environment and restart notebook.\n",
-    "```shell\n",
-    "(myenv) $ conda install -y matplotlib tqdm scikit-learn\n",
-    "```\n",
-    "\n",
-    "3. Check that ACI is registered for your Azure Subscription.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!az provider show -n Microsoft.ContainerInstance -o table"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If ACI is not registered, run following command to register it. Note that you have to be a subscription owner, or this command will fail."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!az provider register -n Microsoft.ContainerInstance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Validate Azure ML SDK installation and get version number for debugging purposes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "install"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "from azureml.core import Experiment, Run, Workspace\n",
-    "import azureml.core\n",
-    "\n",
-    "# Check core SDK version number\n",
-    "print(\"SDK version:\", azureml.core.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialize Workspace\n",
-    "\n",
-    "Initialize a workspace object from persisted configuration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "create workspace"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "ws = Workspace.from_config()\n",
-    "print('Workspace name: ' + ws.name, \n",
-    "      'Azure region: ' + ws.location, \n",
-    "      'Subscription id: ' + ws.subscription_id, \n",
-    "      'Resource group: ' + ws.resource_group, sep='\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Set experiment name\n",
-    "Choose a name for experiment."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "experiment_name = 'train-in-notebook'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Start a training run in local Notebook"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# load diabetes dataset, a well-known small dataset that comes with scikit-learn\n",
-    "from sklearn.datasets import load_diabetes\n",
-    "from sklearn.linear_model import Ridge\n",
-    "from sklearn.metrics import mean_squared_error\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.externals import joblib\n",
-    "\n",
-    "X, y = load_diabetes(return_X_y = True)\n",
-    "columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
-    "data = {\n",
-    "    \"train\":{\"X\": X_train, \"y\": y_train},        \n",
-    "    \"test\":{\"X\": X_test, \"y\": y_test}\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Train a simple Ridge model\n",
-    "Train a very simple Ridge regression model in scikit-learn, and save it as a pickle file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "reg = Ridge(alpha = 0.03)\n",
-    "reg.fit(X=data['train']['X'], y=data['train']['y'])\n",
-    "preds = reg.predict(data['test']['X'])\n",
-    "print('Mean Squared Error is', mean_squared_error(data['test']['y'], preds))\n",
-    "joblib.dump(value=reg, filename='model.pkl');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Add experiment tracking\n",
-    "Now, let's add Azure ML experiment logging, and upload persisted model into run record as well."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "local run",
-     "outputs upload"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "experiment = Experiment(workspace=ws, name=experiment_name)\n",
-    "run = experiment.start_logging()\n",
-    "\n",
-    "run.tag(\"Description\",\"My first run!\")\n",
-    "run.log('alpha', 0.03)\n",
-    "reg = Ridge(alpha=0.03)\n",
-    "reg.fit(data['train']['X'], data['train']['y'])\n",
-    "preds = reg.predict(data['test']['X'])\n",
-    "run.log('mse', mean_squared_error(data['test']['y'], preds))\n",
-    "joblib.dump(value=reg, filename='model.pkl')\n",
-    "run.upload_file(name='outputs/model.pkl', path_or_stream='./model.pkl')\n",
-    "\n",
-    "run.complete()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can browse to the recorded run. Please make sure you use Chrome to navigate the run history page."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Simple parameter sweep\n",
-    "Sweep over alpha values of a sklearn ridge model, and capture metrics and trained model in the Azure ML experiment."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import os\n",
-    "from tqdm import tqdm\n",
-    "\n",
-    "model_name = \"model.pkl\"\n",
-    "\n",
-    "# list of numbers from 0 to 1.0 with a 0.05 interval\n",
-    "alphas = np.arange(0.0, 1.0, 0.05)\n",
-    "\n",
-    "# try a bunch of alpha values in a Linear Regression (Ridge) model\n",
-    "for alpha in tqdm(alphas):\n",
-    "    # create a bunch of runs, each train a model with a different alpha value\n",
-    "    with experiment.start_logging() as run:\n",
-    "        # Use Ridge algorithm to build a regression model\n",
-    "        reg = Ridge(alpha=alpha)\n",
-    "        reg.fit(X=data[\"train\"][\"X\"], y=data[\"train\"][\"y\"])\n",
-    "        preds = reg.predict(X=data[\"test\"][\"X\"])\n",
-    "        mse = mean_squared_error(y_true=data[\"test\"][\"y\"], y_pred=preds)\n",
-    "\n",
-    "        # log alpha, mean_squared_error and feature names in run history\n",
-    "        run.log(name=\"alpha\", value=alpha)\n",
-    "        run.log(name=\"mse\", value=mse)\n",
-    "        run.log_list(name=\"columns\", value=columns)\n",
-    "\n",
-    "        with open(model_name, \"wb\") as file:\n",
-    "            joblib.dump(value=reg, filename=file)\n",
-    "        \n",
-    "        # upload the serialized model into run history record\n",
-    "        run.upload_file(name=\"outputs/\" + model_name, path_or_stream=model_name)\n",
-    "\n",
-    "        # now delete the serialized model from local folder since it is already uploaded to run history \n",
-    "        os.remove(path=model_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# now let's take a look at the experiment in Azure portal.\n",
-    "experiment"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Select best model from the experiment\n",
-    "Load all experiment run metrics recursively from the experiment into a dictionary object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "runs = {}\n",
-    "run_metrics = {}\n",
-    "\n",
-    "for r in tqdm(experiment.get_runs()):\n",
-    "    metrics = r.get_metrics()\n",
-    "    if 'mse' in metrics.keys():\n",
-    "        runs[r.id] = r\n",
-    "        run_metrics[r.id] = metrics"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now find the run with the lowest Mean Squared Error value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_run_id = min(run_metrics, key = lambda k: run_metrics[k]['mse'])\n",
-    "best_run = runs[best_run_id]\n",
-    "print('Best run is:', best_run_id)\n",
-    "print('Metrics:', run_metrics[best_run_id])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can add tags to your runs to make them easier to catalog"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "query history"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "best_run.tag(key=\"Description\", value=\"The best one\")\n",
-    "best_run.get_tags()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot MSE over alpha\n",
-    "\n",
-    "Let's observe the best model visually by plotting the MSE values over alpha values:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "best_alpha = run_metrics[best_run_id]['alpha']\n",
-    "min_mse = run_metrics[best_run_id]['mse']\n",
-    "\n",
-    "alpha_mse = np.array([(run_metrics[k]['alpha'], run_metrics[k]['mse']) for k in run_metrics.keys()])\n",
-    "sorted_alpha_mse = alpha_mse[alpha_mse[:,0].argsort()]\n",
-    "\n",
-    "plt.plot(sorted_alpha_mse[:,0], sorted_alpha_mse[:,1], 'r--')\n",
-    "plt.plot(sorted_alpha_mse[:,0], sorted_alpha_mse[:,1], 'bo')\n",
-    "\n",
-    "plt.xlabel('alpha', fontsize = 14)\n",
-    "plt.ylabel('mean squared error', fontsize = 14)\n",
-    "plt.title('MSE over alpha', fontsize = 16)\n",
-    "\n",
-    "# plot arrow\n",
-    "plt.arrow(x = best_alpha, y = min_mse + 39, dx = 0, dy = -26, ls = '-', lw = 0.4,\n",
-    "          width = 0, head_width = .03, head_length = 8)\n",
-    "\n",
-    "# plot \"best run\" text\n",
-    "plt.text(x = best_alpha - 0.08, y = min_mse + 50, s = 'Best Run', fontsize = 14)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Register the best model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Find the model file saved in the run record of best run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "query history"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "for f in best_run.get_file_names():\n",
-    "    print(f)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can register this model in the model registry of the workspace"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "register model from history"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "model = best_run.register_model(model_name='best_model', model_path='outputs/model.pkl')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Verify that the model has been registered properly. If you have done this several times you'd see the version number auto-increases each time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "register model from history"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "models = ws.models(name='best_model')\n",
-    "for m in models:\n",
-    "    print(m.name, m.version)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also download the registered model. Afterwards, you should see a `model.pkl` file in the current directory. You can then use it for local testing if you'd like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "download file"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# remove the model file if it is already on disk\n",
-    "if os.path.isfile('model.pkl'): \n",
-    "    os.remove('model.pkl')\n",
-    "# download the model\n",
-    "model.download(target_dir=\"./\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Scoring script\n",
-    "\n",
-    "Now we are ready to build a Docker image and deploy the model in it as a web service. The first step is creating the scoring script. For convenience, we have created the scoring script for you. It is printed below as text, but you can also run `%pfile ./score.py` in a cell to show the file.\n",
-    "\n",
-    "Tbe scoring script consists of two functions: `init` that is used to load the model to memory when starting the container, and `run` that makes the prediction when web service is called. Please pay special attention to how the model is loaded in the `init()` function. When Docker image is built for this model, the actual model file is downloaded and placed on disk, and `get_model_path` function returns the local path where the model is placed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('./score.py', 'r') as scoring_script:\n",
-    "    print(scoring_script.read())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create environment dependency file\n",
-    "\n",
-    "We need a environment dependency file `myenv.yml` to specify which libraries are needed by the scoring script when building the Docker image for web service deployment. We can manually create this file, or we can use the `CondaDependencies` API to automatically create this file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.conda_dependencies import CondaDependencies \n",
-    "\n",
-    "myenv = CondaDependencies()\n",
-    "myenv.add_conda_package(\"scikit-learn\")\n",
-    "print(myenv.serialize_to_string())\n",
-    "\n",
-    "with open(\"myenv.yml\",\"w\") as f:\n",
-    "    f.write(myenv.serialize_to_string())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Deploy web service into an Azure Container Instance\n",
-    "The deployment process takes the registered model and your scoring scrip, and builds a Docker image. It then deploys the Docker image into Azure Container Instance as a running container with an HTTP endpoint readying for scoring calls. Read more about [Azure Container Instance](https://azure.microsoft.com/en-us/services/container-instances/).\n",
-    "\n",
-    "Note ACI is great for quick and cost-effective dev/test deployment scenarios. For production workloads, please use [Azure Kubernentes Service (AKS)](https://azure.microsoft.com/en-us/services/kubernetes-service/) instead. Please follow in struction in [this notebook](11.production-deploy-to-aks.ipynb) to see how that can be done from Azure ML.\n",
-    " \n",
-    "** Note: ** The web service creation can take 6-7 minutes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "deploy service",
-     "aci"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "from azureml.core.webservice import AciWebservice, Webservice\n",
-    "\n",
-    "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
-    "                                               memory_gb=1, \n",
-    "                                               tags={'sample name': 'AML 101'}, \n",
-    "                                               description='This is a great example.')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note the below `WebService.deploy_from_model()` function takes a model object registered under the workspace. It then bakes the model file in the Docker image so it can be looked-up using the `Model.get_model_path()` function in `score.py`. \n",
-    "\n",
-    "If you have a local model file instead of a registered model object, you can also use the `WebService.deploy()` function which would register the model and then deploy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "deploy service",
-     "aci"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "from azureml.core.image import ContainerImage\n",
-    "image_config = ContainerImage.image_configuration(execution_script=\"score.py\", \n",
-    "                                    runtime=\"python\", \n",
-    "                                    conda_file=\"myenv.yml\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "deploy service",
-     "aci"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "# this will take 5-10 minutes to finish\n",
-    "# you can also use \"az container list\" command to find the ACI being deployed\n",
-    "service = Webservice.deploy_from_model(name='my-aci-svc',\n",
-    "                                       deployment_config=aciconfig,\n",
-    "                                       models=[model],\n",
-    "                                       image_config=image_config,\n",
-    "                                       workspace=ws)\n",
-    "\n",
-    "service.wait_for_deployment(show_output=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Test web service"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "deploy service",
-     "aci"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "print('web service is hosted in ACI:', service.scoring_uri)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use the `run` API to call the web service with one row of data to get a prediction."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "deploy service",
-     "aci"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "# score the first row from the test set.\n",
-    "test_samples = json.dumps({\"data\": X_test[0:1, :].tolist()})\n",
-    "service.run(input_data = test_samples)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Feed the entire test set and calculate the errors (residual values)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "deploy service",
-     "aci"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# score the entire test set.\n",
-    "test_samples = json.dumps({'data': X_test.tolist()})\n",
-    "\n",
-    "result = json.loads(service.run(input_data = test_samples))['result']\n",
-    "residual = result - y_test"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also send raw HTTP request to test the web service."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "deploy service",
-     "aci"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "import json\n",
-    "\n",
-    "# 2 rows of input data, each with 10 made-up numerical features\n",
-    "input_data = \"{\\\"data\\\": [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10], [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]]}\"\n",
-    "\n",
-    "headers = {'Content-Type':'application/json'}\n",
-    "\n",
-    "# for AKS deployment you'd need to the service key in the header as well\n",
-    "# api_key = service.get_key()\n",
-    "# headers = {'Content-Type':'application/json',  'Authorization':('Bearer '+ api_key)} \n",
-    "\n",
-    "resp = requests.post(service.scoring_uri, input_data, headers = headers)\n",
-    "print(resp.text)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Residual graph\n",
-    "Plot a residual value graph to chart the errors on the entire test set. Observe the nice bell curve."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios':[3, 1], 'wspace':0, 'hspace': 0})\n",
-    "f.suptitle('Residual Values', fontsize = 18)\n",
-    "\n",
-    "f.set_figheight(6)\n",
-    "f.set_figwidth(14)\n",
-    "\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.set_yticklabels([])\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Delete ACI to clean up"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Deleting ACI is super fast!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "deploy service",
-     "aci"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "service.delete()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "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": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

01.getting-started/01.train-within-notebook/score.py

@@ -1,27 +0,0 @@
-import pickle
-import json
-import numpy as np
-from sklearn.externals import joblib
-from sklearn.linear_model import Ridge
-from azureml.core.model import Model
-
-
-def init():
-    global model
-    # note here "best_model" is the name of the model registered under the workspace
-    # this call should return the path to the model.pkl file on the local disk.
-    model_path = Model.get_model_path(model_name='best_model')
-    # deserialize the model file back into a sklearn model
-    model = joblib.load(model_path)
-
-
-# note you can pass in multiple rows for scoring
-def run(raw_data):
-    try:
-        data = json.loads(raw_data)['data']
-        data = np.array(data)
-        result = model.predict(data)
-        return json.dumps({"result": result.tolist()})
-    except Exception as e:
-        result = str(e)
-        return json.dumps({"error": result})

01.getting-started/02.train-on-local/02.train-on-local.ipynb

@@ -1,465 +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": [
-    "# 02. Train locally\n",
-    "* Create or load workspace.\n",
-    "* Create scripts locally.\n",
-    "* Create `train.py` in a folder, along with a `my.lib` file.\n",
-    "* Configure & execute a local run in a user-managed Python environment.\n",
-    "* Configure & execute a local run in a system-managed Python environment.\n",
-    "* Configure & execute a local run in a Docker environment.\n",
-    "* Query run metrics to find the best model\n",
-    "* Register model for operationalization."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prerequisites\n",
-    "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Check core SDK version number\n",
-    "import azureml.core\n",
-    "\n",
-    "print(\"SDK version:\", azureml.core.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialize Workspace\n",
-    "\n",
-    "Initialize a workspace object from persisted configuration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.workspace import Workspace\n",
-    "\n",
-    "ws = Workspace.from_config()\n",
-    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create An Experiment\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-local'\n",
-    "exp = Experiment(workspace=ws, name=experiment_name)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## View `train.py`\n",
-    "\n",
-    "`train.py` is already created for you."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('./train.py', 'r') as f:\n",
-    "    print(f.read())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note `train.py` also references a `mylib.py` file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('./mylib.py', 'r') as f:\n",
-    "    print(f.read())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Configure & Run\n",
-    "### User-managed environment\n",
-    "Below, we use a user-managed run, which means you are responsible to ensure all the necessary packages are available in the Python environment you choose to run the script."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.runconfig import RunConfiguration\n",
-    "\n",
-    "# Editing a run configuration property on-fly.\n",
-    "run_config_user_managed = RunConfiguration()\n",
-    "\n",
-    "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'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Submit script to run in the user-managed environment\n",
-    "Note whole script folder is submitted for execution, including the `mylib.py` file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core import ScriptRunConfig\n",
-    "\n",
-    "src = ScriptRunConfig(source_directory='./', script='train.py', run_config=run_config_user_managed)\n",
-    "run = exp.submit(src)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Get run history details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Block to wait till run finishes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run.wait_for_completion(show_output=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### System-managed environment\n",
-    "You can also ask the system to build a new conda environment and execute your scripts in it. The environment is built once and will be reused in subsequent executions as long as the conda dependencies remain unchanged. "
-   ]
-  },
-  {
-   "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",
-    "run_config_system_managed = RunConfiguration()\n",
-    "\n",
-    "run_config_system_managed.environment.python.user_managed_dependencies = False\n",
-    "run_config_system_managed.prepare_environment = True\n",
-    "\n",
-    "# Specify conda dependencies with scikit-learn\n",
-    "cd = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
-    "run_config_system_managed.environment.python.conda_dependencies = cd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 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."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "src = ScriptRunConfig(source_directory=\"./\", script='train.py', run_config=run_config_system_managed)\n",
-    "run = exp.submit(src)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Get run history details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Block and wait till run finishes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run.wait_for_completion(show_output = True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "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",
-    "You can also ask the system to pull down a Docker image and execute your scripts in it."
-   ]
-  },
-  {
-   "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",
-    "run_config_docker = RunConfiguration()\n",
-    "\n",
-    "run_config_docker.environment.python.user_managed_dependencies = False\n",
-    "run_config_docker.prepare_environment = True\n",
-    "run_config_docker.environment.docker.enabled = True\n",
-    "run_config_docker.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
-    "\n",
-    "# Specify conda dependencies with scikit-learn\n",
-    "cd = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
-    "run_config_docker.environment.python.conda_dependencies = cd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "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"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "src = ScriptRunConfig(source_directory=\"./\", script='train.py', run_config=run_config_docker)\n",
-    "run = exp.submit(src)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Get run history details\n",
-    "run"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run.wait_for_completion(show_output=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Query run metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "query history",
-     "get metrics"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# get all metris logged in the run\n",
-    "run.get_metrics()\n",
-    "metrics = run.get_metrics()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's find the model that has the lowest MSE value logged."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "best_alpha = metrics['alpha'][np.argmin(metrics['mse'])]\n",
-    "\n",
-    "print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n",
-    "    min(metrics['mse']), \n",
-    "    best_alpha\n",
-    "))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also list all the files that are associated with this run record"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run.get_file_names()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We know the model `ridge_0.40.pkl` is the best performing model from the eariler queries. So let's register it with the workspace."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# supply a model name, and the full path to the serialized model file.\n",
-    "model = run.register_model(model_name='best_ridge_model', model_path='./outputs/ridge_0.40.pkl')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(model.name, model.version, model.url)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now you can deploy this model following the example in the 01 notebook."
-   ]
-  }
- ],
- "metadata": {
-  "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
-}

01.getting-started/03.train-on-aci/03.train-on-aci.ipynb

@@ -1,284 +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": [
-    "# 03. Train on Azure Container Instance\n",
-    "\n",
-    "* Create Workspace\n",
-    "* Create `train.py` in the project folder.\n",
-    "* Configure an ACI (Azure Container Instance) run\n",
-    "* Execute in ACI"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prerequisites\n",
-    "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Check core SDK version number\n",
-    "import azureml.core\n",
-    "\n",
-    "print(\"SDK version:\", azureml.core.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialize Workspace\n",
-    "\n",
-    "Initialize a workspace object from persisted configuration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "create workspace"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "from azureml.core import Workspace\n",
-    "\n",
-    "ws = Workspace.from_config()\n",
-    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create 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-aci'\n",
-    "experiment = Experiment(workspace = ws, name = experiment_name)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Remote execution on ACI\n",
-    "\n",
-    "The training script `train.py` is already created for you. Let's have a look."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('./train.py', 'r') as f:\n",
-    "    print(f.read())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Configure for using ACI\n",
-    "Linux-based ACI is available in `West US`, `East US`, `West Europe`, `North Europe`, `West US 2`, `Southeast Asia`, `Australia East`, `East US 2`, and `Central US` regions. See details [here](https://docs.microsoft.com/en-us/azure/container-instances/container-instances-quotas#region-availability)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "configure run"
-    ]
-   },
-   "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()\n",
-    "\n",
-    "# signal that you want to use ACI to execute script.\n",
-    "run_config.target = \"containerinstance\"\n",
-    "\n",
-    "# ACI container group is only supported in certain regions, which can be different than the region the Workspace is in.\n",
-    "run_config.container_instance.region = 'eastus2'\n",
-    "\n",
-    "# set the ACI CPU and Memory \n",
-    "run_config.container_instance.cpu_cores = 1\n",
-    "run_config.container_instance.memory_gb = 2\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 = azureml.core.runconfig.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'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Submit the Experiment\n",
-    "Finally, run the training job on the ACI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remote run",
-     "aci"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "%%time \n",
-    "from azureml.core.script_run_config import ScriptRunConfig\n",
-    "\n",
-    "script_run_config = ScriptRunConfig(source_directory='./',\n",
-    "                                    script='train.py',\n",
-    "                                    run_config=run_config)\n",
-    "\n",
-    "run = experiment.submit(script_run_config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "query history"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# Show run details\n",
-    "run"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "remote run",
-     "aci"
-    ]
-   },
-   "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": {
-    "tags": [
-     "get metrics"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# get all metris logged in the run\n",
-    "run.get_metrics()\n",
-    "metrics = run.get_metrics()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n",
-    "    min(metrics['mse']), \n",
-    "    metrics['alpha'][np.argmin(metrics['mse'])]\n",
-    "))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# show all the files stored within the run record\n",
-    "run.get_file_names()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now you can take a model produced here, register it and then deploy as a web service."
-   ]
-  }
- ],
- "metadata": {
-  "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
-}

01.getting-started/04.train-on-remote-vm/04.train-on-remote-vm.ipynb

@@ -1,321 +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": [
-    "# 04. Train in a remote VM (MLC managed DSVM)\n",
-    "* Create Workspace\n",
-    "* Create Project\n",
-    "* Create `train.py` file\n",
-    "* Create DSVM as Machine Learning Compute (MLC) resource\n",
-    "* Configure & execute a run in a conda environment in the default miniconda Docker container on DSVM"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prerequisites\n",
-    "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Check core SDK version number\n",
-    "import azureml.core\n",
-    "\n",
-    "print(\"SDK version:\", azureml.core.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialize Workspace\n",
-    "\n",
-    "Initialize a workspace object from persisted configuration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core import Workspace\n",
-    "\n",
-    "ws = Workspace.from_config()\n",
-    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create 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": [
-    "experiment_name = 'train-on-remote-vm'\n",
-    "\n",
-    "from azureml.core import Experiment\n",
-    "\n",
-    "exp = Experiment(workspace = ws, name = experiment_name)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## View `train.py`\n",
-    "\n",
-    "For convenience, we created a training script for you. It is printed below as a text, but you can also run `%pfile ./train.py` in a cell to show the file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('./train.py', 'r') as training_script:\n",
-    "    print(training_script.read())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Linux 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",
-    " \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 append the port number to the address like the example below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.compute import DsvmCompute\n",
-    "from azureml.core.compute_target import ComputeTargetException\n",
-    "\n",
-    "compute_target_name = 'mydsvm'\n",
-    "\n",
-    "try:\n",
-    "    dsvm_compute = DsvmCompute(workspace = ws, name = compute_target_name)\n",
-    "    print('found existing:', 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 as a compute target\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "'''\n",
-    " from azureml.core.compute import RemoteCompute \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",
-    " dsvm_compute = RemoteCompute.attach(ws,name=\"attach-from-sdk6\",username=<username>,address=<ipaddress>,ssh_port=22,password=<password>)\n",
-    "'''"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Configure & Run"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Configure a Docker run with new conda environment on the VM\n",
-    "You can execute in a Docker container in the VM. If you choose this route, you don't need to install anything on the VM yourself. Azure ML execution service will take care of it for you."
-   ]
-  },
-  {
-   "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",
-    "\n",
-    "# Load the \"cpu-dsvm.runconfig\" file (created by the above attach operation) in memory\n",
-    "run_config = RunConfiguration(framework = \"python\")\n",
-    "\n",
-    "# Set compute target to the Linux DSVM\n",
-    "run_config.target = compute_target_name\n",
-    "\n",
-    "# Use Docker in the remote VM\n",
-    "run_config.environment.docker.enabled = True\n",
-    "\n",
-    "# Use CPU base image from DockerHub\n",
-    "run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
-    "print('Base Docker image is:', run_config.environment.docker.base_image)\n",
-    "\n",
-    "# Ask system to provision a new one based on the conda_dependencies.yml file\n",
-    "run_config.environment.python.user_managed_dependencies = False\n",
-    "\n",
-    "# Prepare the Docker and conda environment automatically when executingfor the first time.\n",
-    "run_config.prepare_environment = True\n",
-    "\n",
-    "# specify CondaDependencies obj\n",
-    "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Submit the Experiment\n",
-    "Submit script to run in the Docker image in the remote VM. If you run this for the first time, the system will download the base image, layer in packages specified in the `conda_dependencies.yml` file on top of the base image, create a container and then execute the script in the container."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core import Run\n",
-    "from azureml.core import ScriptRunConfig\n",
-    "\n",
-    "src = ScriptRunConfig(source_directory = '.', script = 'train.py', run_config = run_config)\n",
-    "run = exp.submit(src)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### View run history details"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run.wait_for_completion(show_output = True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Find the best run"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# get all metris logged in the run\n",
-    "run.get_metrics()\n",
-    "metrics = run.get_metrics()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n",
-    "    min(metrics['mse']), \n",
-    "    metrics['alpha'][np.argmin(metrics['mse'])]\n",
-    "))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Clean up compute resource"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dsvm_compute.delete()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.6",
-   "language": "python",
-   "name": "python36"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

01.getting-started/05.train-in-spark/05.train-in-spark.ipynb

@@ -1,257 +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": [
-    "# 05. Train in Spark\n",
-    "* Create Workspace\n",
-    "* Create Experiment\n",
-    "* Copy relevant files to the script folder\n",
-    "* Configure and Run"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prerequisites\n",
-    "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Check core SDK version number\n",
-    "import azureml.core\n",
-    "\n",
-    "print(\"SDK version:\", azureml.core.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialize Workspace\n",
-    "\n",
-    "Initialize a workspace object from persisted configuration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core import Workspace\n",
-    "\n",
-    "ws = Workspace.from_config()\n",
-    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Experiment\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "experiment_name = 'train-on-spark'\n",
-    "\n",
-    "from azureml.core import Experiment\n",
-    "\n",
-    "exp = Experiment(workspace = ws, name = experiment_name)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## View `train-spark.py`\n",
-    "\n",
-    "For convenience, we created a training script for you. It is printed below as a text, but you can also run `%pfile ./train-spark.py` in a cell to show the file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('train-spark.py', 'r') as training_script:\n",
-    "    print(training_script.read())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Configure & Run"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Attach an HDI cluster\n",
-    "To use HDI commpute target:\n",
-    " 1. Create an Spark for HDI cluster in Azure. Here is some [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.\n",
-    " 2. Enter the IP address, username and password below"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.compute import HDInsightCompute\n",
-    "\n",
-    "try:\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",
-    "    hdi_compute_new = HDInsightCompute.attach(ws, \n",
-    "                             name=\"hdi-attach\", \n",
-    "                             address=\"hdi-ignite-demo-ssh.azurehdinsight.net\", \n",
-    "                             ssh_port=22, \n",
-    "                             username='<username>', \n",
-    "                             password='<password>')\n",
-    "\n",
-    "except UserErrorException as e:\n",
-    "    print(\"Caught = {}\".format(e.message))\n",
-    "    print(\"Compute config already attached.\")\n",
-    "    \n",
-    "    \n",
-    "hdi_compute_new.wait_for_completion(show_output=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Configure HDI 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",
-    "\n",
-    "# Load the \"cpu-dsvm.runconfig\" file (created by the above attach operation) in memory\n",
-    "run_config = RunConfiguration(framework = \"python\")\n",
-    "\n",
-    "# Set compute target to the Linux DSVM\n",
-    "run_config.target = hdi_compute.name\n",
-    "\n",
-    "# Use Docker in the remote VM\n",
-    "# run_config.environment.docker.enabled = True\n",
-    "\n",
-    "# Use CPU base image from DockerHub\n",
-    "# run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
-    "# print('Base Docker image is:', run_config.environment.docker.base_image)\n",
-    "\n",
-    "# Ask system to provision a new one based on the conda_dependencies.yml file\n",
-    "run_config.environment.python.user_managed_dependencies = False\n",
-    "\n",
-    "# Prepare the Docker and conda environment automatically when executingfor the first time.\n",
-    "# run_config.prepare_environment = True\n",
-    "\n",
-    "# specify CondaDependencies obj\n",
-    "# run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
-    "# load the runconfig object from the \"myhdi.runconfig\" file generated by the attach operaton above."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Submit the script to HDI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "script_run_config = ScriptRunConfig(source_directory = '.',\n",
-    "                                    script= 'train-spark.py',\n",
-    "                                    run_config = run_config)\n",
-    "run = experiment.submit(script_run_config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# get the URL of the run history web page\n",
-    "run"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run.wait_for_completion(show_output = True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# get all metris logged in the run\n",
-    "metrics = run.get_metrics()\n",
-    "print(metrics)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.6",
-   "language": "python",
-   "name": "python36"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

01.getting-started/10.register-model-create-image-deploy-service/10.register-model-create-image-deploy-service.ipynb

@@ -1,420 +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": [
-    "## 10. 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",
-    " 1. Register model\n",
-    " 2. Query versions of models and select one to deploy\n",
-    " 3. Create Docker image\n",
-    " 4. Query versions of images\n",
-    " 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",
-    " \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.  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prerequisites\n",
-    "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Check core SDK version number\n",
-    "import azureml.core\n",
-    "\n",
-    "print(\"SDK version:\", azureml.core.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialize Workspace\n",
-    "\n",
-    "Initialize a workspace object from persisted configuration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "create workspace"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "from azureml.core import Workspace\n",
-    "\n",
-    "ws = Workspace.from_config()\n",
-    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Register Model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can add tags and descriptions to your models. Note you need to have a `sklearn_linreg_model.pkl` file in the current directory. This file is generated by the 01 notebook. The below call registers that file as a model with the same name `sklearn_linreg_model.pkl` in the workspace.\n",
-    "\n",
-    "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "register model from file"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "from azureml.core.model import Model\n",
-    "import sklearn\n",
-    "\n",
-    "library_version = \"sklearn\"+sklearn.__version__.replace(\".\",\"x\")\n",
-    "\n",
-    "model = Model.register(model_path = \"sklearn_regression_model.pkl\",\n",
-    "                       model_name = \"sklearn_regression_model.pkl\",\n",
-    "                       tags = {'area': \"diabetes\", 'type': \"regression\", 'version': library_version},\n",
-    "                       description = \"Ridge regression model to predict diabetes\",\n",
-    "                       workspace = ws)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can explore the registered models within your workspace and query by tag. Models are versioned. If you call the register_model command many times with same model name, you will get multiple versions of the model with increasing version numbers."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "register model from file"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "regression_models = ws.models(tags=['area'])\n",
-    "for name, m in regression_models.items():\n",
-    "    print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can pick a specific model to deploy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(model.name, model.description, model.version, sep = '\\t')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create Docker Image"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Show `score.py`. Note that the `sklearn_regression_model.pkl` in the `get_model_path` call is referring to a model named `sklearn_linreg_model.pkl` registered under the workspace. It is NOT referenceing the local file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%writefile score.py\n",
-    "import pickle\n",
-    "import json\n",
-    "import numpy\n",
-    "from sklearn.externals import joblib\n",
-    "from sklearn.linear_model import Ridge\n",
-    "from azureml.core.model import Model\n",
-    "\n",
-    "def init():\n",
-    "    global model\n",
-    "    # note here \"sklearn_regression_model.pkl\" is the name of the model registered under\n",
-    "    # this is a different behavior than before when the code is run locally, even though the code is the same.\n",
-    "    model_path = Model.get_model_path('sklearn_regression_model.pkl')\n",
-    "    # deserialize the model file back into a sklearn model\n",
-    "    model = joblib.load(model_path)\n",
-    "\n",
-    "# note you can pass in multiple rows for scoring\n",
-    "def run(raw_data):\n",
-    "    try:\n",
-    "        data = json.loads(raw_data)['data']\n",
-    "        data = numpy.array(data)\n",
-    "        result = model.predict(data)\n",
-    "    except Exception as e:\n",
-    "        result = str(e)\n",
-    "    return json.dumps({\"result\": result.tolist()})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.conda_dependencies import CondaDependencies \n",
-    "\n",
-    "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
-    "\n",
-    "with open(\"myenv.yml\",\"w\") as f:\n",
-    "    f.write(myenv.serialize_to_string())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that following command can take few minutes. \n",
-    "\n",
-    "You can add tags and descriptions to images. Also, an image can contain multiple models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "create image"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "from azureml.core.image import Image, ContainerImage\n",
-    "\n",
-    "image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
-    "                                 execution_script=\"score.py\",\n",
-    "                                 conda_file=\"myenv.yml\",\n",
-    "                                 tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
-    "                                 description = \"Image with ridge regression model\")\n",
-    "\n",
-    "image = Image.create(name = \"myimage1\",\n",
-    "                     # this is the model object \n",
-    "                     models = [model],\n",
-    "                     image_config = image_config, \n",
-    "                     workspace = ws)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "create image"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "image.wait_for_creation(show_output = True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "List images by tag and find out the detailed build log for debugging."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "create image"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "for i in Image.list(workspace = ws,tags = [\"area\"]):\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": "markdown",
-   "metadata": {},
-   "source": [
-    "### Deploy image as web service on Azure Container Instance\n",
-    "\n",
-    "Note that the service creation can take few minutes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "deploy service",
-     "aci"
-    ]
-   },
-   "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')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "deploy service",
-     "aci"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "from azureml.core.webservice import Webservice\n",
-    "\n",
-    "aci_service_name = 'my-aci-service-2'\n",
-    "print(aci_service_name)\n",
-    "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
-    "                                           image = image,\n",
-    "                                           name = aci_service_name,\n",
-    "                                           workspace = ws)\n",
-    "aci_service.wait_for_deployment(True)\n",
-    "print(aci_service.state)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Test web service"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Call the web service with some dummy input data to get a prediction."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "deploy service",
-     "aci"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "\n",
-    "test_sample = json.dumps({'data': [\n",
-    "    [1,2,3,4,5,6,7,8,9,10], \n",
-    "    [10,9,8,7,6,5,4,3,2,1]\n",
-    "]})\n",
-    "test_sample = bytes(test_sample,encoding = 'utf8')\n",
-    "\n",
-    "prediction = aci_service.run(input_data = test_sample)\n",
-    "print(prediction)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Delete ACI to clean up"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": [
-     "deploy service",
-     "aci"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "aci_service.delete()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.6",
-   "language": "python",
-   "name": "python36"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

01.getting-started/12.enable-data-collection-for-models-in-aks/12.enable-data-collection-for-models-in-aks.ipynb

@@ -1,447 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Enabling Data Collection for Models in Production\n",
-    "With this notebook, you can learn how to collect input model data from your Azure Machine Learning service in an Azure Blob storage. Once enabled, this data collected gives you the opportunity:\n",
-    "\n",
-    "* Monitor data drifts as production data enters your model\n",
-    "* Make better decisions on when to retrain or optimize your model\n",
-    "* Retrain your model with the data collected\n",
-    "\n",
-    "## What data is collected?\n",
-    "* Model input data (voice, images, and video are not supported) from services deployed in Azure Kubernetes Cluster (AKS)\n",
-    "* Model predictions using production input data.\n",
-    "\n",
-    "**Note:** pre-aggregation or pre-calculations on this data are done by user and not included in this version of the product.\n",
-    "\n",
-    "## What is different compared to standard production deployment process?\n",
-    "1. Update scoring file.\n",
-    "2. Update yml file with new dependency.\n",
-    "3. Update aks configuration.\n",
-    "4. Build new image and deploy it. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Import your dependencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core import Workspace, Run\n",
-    "from azureml.core.compute import AksCompute, ComputeTarget\n",
-    "from azureml.core.webservice import Webservice, AksWebservice\n",
-    "from azureml.core.image import Image\n",
-    "from azureml.core.model import Model\n",
-    "\n",
-    "import azureml.core\n",
-    "print(azureml.core.VERSION)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Set up your configuration and create a workspace\n",
-    "Follow Notebook 00 instructions to do this.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ws = Workspace.from_config()\n",
-    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Register Model\n",
-    "Register an existing trained model, add descirption and tags."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Register the model\n",
-    "from azureml.core.model import Model\n",
-    "model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n",
-    "                       model_name = \"sklearn_regression_model.pkl\", # this is the name the model is registered as\n",
-    "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
-    "                       description = \"Ridge regression model to predict diabetes\",\n",
-    "                       workspace = ws)\n",
-    "\n",
-    "print(model.name, model.description, model.version)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4. *Update your scoring file with Data Collection*\n",
-    "The file below, compared to the file used in notebook 11, has the following changes:\n",
-    "### a. Import the module\n",
-    "```python \n",
-    "from azureml.monitoring import ModelDataCollector```\n",
-    "### b. In your init function add:\n",
-    "```python \n",
-    "global inputs_dc, prediction_d\n",
-    "inputs_dc = ModelDataCollector(\"best_model\", identifier=\"inputs\", feature_names=[\"feat1\", \"feat2\", \"feat3\". \"feat4\", \"feat5\", \"Feat6\"])\n",
-    "prediction_dc = ModelDataCollector(\"best_model\", identifier=\"predictions\", feature_names=[\"prediction1\", \"prediction2\"])```\n",
-    "    \n",
-    "* Identifier: Identifier is later used for building the folder structure in your Blob, it can be used to divide \"raw\" data versus \"processed\".\n",
-    "* CorrelationId: is an optional parameter, you do not need to set it up if your model doesn't require it. Having a correlationId in place does help you for easier mapping with other data. (Examples include: LoanNumber, CustomerId, etc.)\n",
-    "* Feature Names: These need to be set up in the order of your features in order for them to have column names when the .csv is created.\n",
-    "\n",
-    "### c. In your run function add:\n",
-    "```python\n",
-    "inputs_dc.collect(data)\n",
-    "prediction_dc.collect(result)```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%writefile score.py\n",
-    "import pickle\n",
-    "import json\n",
-    "import numpy \n",
-    "from sklearn.externals import joblib\n",
-    "from sklearn.linear_model import Ridge\n",
-    "from azureml.core.model import Model\n",
-    "from azureml.monitoring import ModelDataCollector\n",
-    "import time\n",
-    "\n",
-    "def init():\n",
-    "    global model\n",
-    "    print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))\n",
-    "    # note here \"sklearn_regression_model.pkl\" is the name of the model registered under the workspace\n",
-    "    # this call should return the path to the model.pkl file on the local disk.\n",
-    "    model_path = Model.get_model_path(model_name = 'sklearn_regression_model.pkl')\n",
-    "    # deserialize the model file back into a sklearn model\n",
-    "    model = joblib.load(model_path)\n",
-    "    global inputs_dc, prediction_dc\n",
-    "    # this setup will help us save our inputs under the \"inputs\" path in our Azure Blob\n",
-    "    inputs_dc = ModelDataCollector(model_name=\"sklearn_regression_model\", identifier=\"inputs\", feature_names=[\"feat1\", \"feat2\"]) \n",
-    "    # this setup will help us save our ipredictions under the \"predictions\" path in our Azure Blob\n",
-    "    prediction_dc = ModelDataCollector(\"sklearn_regression_model\", identifier=\"predictions\", feature_names=[\"prediction1\", \"prediction2\"]) \n",
-    "  \n",
-    "# note you can pass in multiple rows for scoring\n",
-    "def run(raw_data):\n",
-    "    global inputs_dc, prediction_dc\n",
-    "    try:\n",
-    "        data = json.loads(raw_data)['data']\n",
-    "        data = numpy.array(data)\n",
-    "        result = model.predict(data)\n",
-    "        print (\"saving input data\" + time.strftime(\"%H:%M:%S\"))\n",
-    "        inputs_dc.collect(data) #this call is saving our input data into our blob\n",
-    "        prediction_dc.collect(result)#this call is saving our prediction data into our blob\n",
-    "        print (\"saving prediction data\" + time.strftime(\"%H:%M:%S\"))\n",
-    "        return json.dumps({\"result\": result.tolist()})\n",
-    "    except Exception as e:\n",
-    "        result = str(e)\n",
-    "        print (result + time.strftime(\"%H:%M:%S\"))\n",
-    "        return json.dumps({\"error\": result})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5. *Update your myenv.yml file with the required module*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.conda_dependencies import CondaDependencies \n",
-    "\n",
-    "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
-    "myenv.add_pip_package(\"azureml-monitoring\")\n",
-    "\n",
-    "with open(\"myenv.yml\",\"w\") as f:\n",
-    "    f.write(myenv.serialize_to_string())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 6. Create your new Image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.image import ContainerImage\n",
-    "\n",
-    "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
-    "                                                  runtime = \"python\",\n",
-    "                                                  conda_file = \"myenv.yml\",\n",
-    "                                                  description = \"Image with ridge regression model\",\n",
-    "                                                  tags = {'area': \"diabetes\", 'type': \"regression\"}\n",
-    "                                                 )\n",
-    "\n",
-    "image = ContainerImage.create(name = \"myimage1\",\n",
-    "                              # this is the model object\n",
-    "                              models = [model],\n",
-    "                              image_config = image_config,\n",
-    "                              workspace = ws)\n",
-    "\n",
-    "image.wait_for_creation(show_output = True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(model.name, model.description, model.version)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 7. Deploy to AKS service"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create AKS compute if you haven't done so (Notebook 11)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Use the default configuration (can also provide parameters to customize)\n",
-    "prov_config = AksCompute.provisioning_configuration()\n",
-    "\n",
-    "aks_name = 'my-aks-test1' \n",
-    "# Create the cluster\n",
-    "aks_target = ComputeTarget.create(workspace = ws, \n",
-    "                                  name = aks_name, \n",
-    "                                  provisioning_configuration = prov_config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "aks_target.wait_for_completion(show_output = True)\n",
-    "print(aks_target.provisioning_state)\n",
-    "print(aks_target.provisioning_errors)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you already have a cluster you can attach the service to it:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "scrolled": true
-   },
-   "source": [
-    "```python \n",
-    "    %%time\n",
-    "    resource_id = '/subscriptions/<subscriptionid>/resourcegroups/<resourcegroupname>/providers/Microsoft.ContainerService/managedClusters/<aksservername>'\n",
-    "    create_name= 'myaks4'\n",
-    "    aks_target = AksCompute.attach(workspace = ws, \n",
-    "                                      name = create_name, \n",
-    "                                      #esource_id=resource_id)\n",
-    "    ## Wait for the operation to complete\n",
-    "    aks_target.wait_for_provisioning(True)```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### a. *Activate Data Collection and App Insights through updating AKS Webservice configuration*\n",
-    "In order to enable Data Collection and App Insights in your service you will need to update your AKS configuration file:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Set the web service configuration\n",
-    "aks_config = AksWebservice.deploy_configuration(collect_model_data=True, enable_app_insights=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### b. Deploy your service"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "aks_service_name ='aks-w-dc2'\n",
-    "\n",
-    "aks_service = Webservice.deploy_from_image(workspace = ws, \n",
-    "                                           name = aks_service_name,\n",
-    "                                           image = image,\n",
-    "                                           deployment_config = aks_config,\n",
-    "                                           deployment_target = aks_target\n",
-    "                                           )\n",
-    "aks_service.wait_for_deployment(show_output = True)\n",
-    "print(aks_service.state)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 8. Test your service and send some data\n",
-    "**Note**: It will take around 15 mins for your data to appear in your blob.\n",
-    "The data will appear in your Azure Blob following this format:\n",
-    "\n",
-    "/modeldata/subscriptionid/resourcegroupname/workspacename/webservicename/modelname/modelversion/identifier/year/month/day/data.csv "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%time\n",
-    "import json\n",
-    "\n",
-    "test_sample = json.dumps({'data': [\n",
-    "    [1,2,3,4,54,6,7,8,88,10], \n",
-    "    [10,9,8,37,36,45,4,33,2,1]\n",
-    "]})\n",
-    "test_sample = bytes(test_sample,encoding = 'utf8')\n",
-    "\n",
-    "prediction = aks_service.run(input_data = test_sample)\n",
-    "print(prediction)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 9. Validate you data and analyze it\n",
-    "You can look into your data following this path format in your Azure Blob (it takes up to 15 minutes for the data to appear):\n",
-    "\n",
-    "/modeldata/**subscriptionid>**/**resourcegroupname>**/**workspacename>**/**webservicename>**/**modelname>**/**modelversion>>**/**identifier>**/*year/month/day*/data.csv \n",
-    "\n",
-    "For doing further analysis you have multiple options:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### a. Create DataBricks cluter and connect it to your blob\n",
-    "https://docs.microsoft.com/en-us/azure/azure-databricks/quickstart-create-databricks-workspace-portal or in your databricks workspace you can look for the template \"Azure Blob Storage Import Example Notebook\".\n",
-    "\n",
-    "\n",
-    "Here is an example for setting up the file location to extract the relevant data:\n",
-    "\n",
-    "<code> file_location = \"wasbs://mycontainer@storageaccountname.blob.core.windows.net/unknown/unknown/unknown-bigdataset-unknown/my_iterate_parking_inputs/2018/&deg;/&deg;/data.csv\" \n",
-    "file_type = \"csv\"</code>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### b. Connect Blob to Power Bi (Small Data only)\n",
-    "1. Download and Open PowerBi Desktop\n",
-    "2. Select “Get Data” and click on “Azure Blob Storage” >> Connect\n",
-    "3. Add your storage account and enter your storage key.\n",
-    "4. Select the container where your Data Collection is stored and click on Edit. \n",
-    "5. In the query editor, click under “Name” column and add your Storage account Model path into the filter. Note: if you want to only look into files from a specific year or month, just expand the filter path. For example, just look into March data: /modeldata/subscriptionid>/resourcegroupname>/workspacename>/webservicename>/modelname>/modelversion>/identifier>/year>/3\n",
-    "6. Click on the double arrow aside the “Content” column to combine the files. \n",
-    "7. Click OK and the data will preload.\n",
-    "8. You can now click Close and Apply and start building your custom reports on your Model Input data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Disable Data Collection"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "aks_service.update(collect_model_data=False)"
-   ]
-  }
- ],
- "metadata": {
-  "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
-}

Dockerfiles/1.0.10/Dockerfile

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

Dockerfiles/1.0.15/Dockerfile

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

Dockerfiles/1.0.17/Dockerfile

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

Dockerfiles/1.0.18/Dockerfile

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

Dockerfiles/1.0.2/Dockerfile

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

Dockerfiles/1.0.21/Dockerfile

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

Dockerfiles/1.0.23/Dockerfile

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

Dockerfiles/1.0.30/Dockerfile

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

Dockerfiles/1.0.33/Dockerfile

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

Dockerfiles/1.0.41/Dockerfile

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

Dockerfiles/1.0.43/Dockerfile

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

Dockerfiles/1.0.6/Dockerfile

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

Dockerfiles/1.0.8/Dockerfile

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

LICENSE

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

README.md

@@ -1,45 +0,0 @@
-# Sample notebooks for Azure Machine Learning service
-
-To run the notebooks in this repository use one of these methods:
-
-## Use Azure Notebooks - Jupyter based notebooks in the Azure cloud
-
-1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
-[Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks if they are not already there.
-1.  Create a workspace and its configuration file (**config.json**) using [these instructions](https://aka.ms/aml-how-to-configure-environment).
-1. Select `+New` in the Azure Notebook toolbar to add your **config.json** file to the imported folder.
-    ![upload config file to notebook folder](images/additems.png)
-1. Open the notebook.
-    
-    **Make sure the Azure Notebook kernal is set to `Python 3.6`** when you open a notebook. 
-    
-    ![set kernal to Python 3.6](images/python36.png)
-
-
-## **Use your own notebook server**
-
-1. Use [these instructions](https://aka.ms/aml-how-to-configure-environment) to:
-    * Create a workspace and its configuration file (**config.json**).
-    * Configure your notebook server.
-1. Clone [this repository](https://aka.ms/aml-notebooks).
-1. Add your **config.json** file to the cloned folder
-1. You may need to install other packages for specific notebooks
-1. Start your notebook server.
-1. Open the notebook you want to run.
-
-> Note: **Looking for automated machine learning samples?**
-> For your convenience, you can use an installation script instead of the steps below for the automated ML notebooks. Go to the [automl folder README](automl/README.md) and follow the instructions.  The script installs all  packages needed for notebooks in that folder.
-
-# Contributing
-
-This project welcomes contributions and suggestions.  Most contributions require you to agree to a
-Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
-the rights to use your contribution. For details, visit https://cla.microsoft.com.
-
-When you submit a pull request, a CLA-bot will automatically determine whether you need to provide
-a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions
-provided by the bot. You will only need to do this once across all repos using our CLA.
-
-This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
-For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
-contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

automl/00.configuration.ipynb

@@ -1,265 +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 00. configuration\n",
-    "\n",
-    "In this example you will create an Azure Machine Learning Workspace and initialize your notebook directory to easily use this workspace.  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",
-    "\n",
-    "\n",
-    "## Prerequisites:\n",
-    "\n",
-    "Before running this notebook, run the automl_setup script described in README.md.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Connect to your Azure Subscription\n",
-    "\n",
-    "In order to use an AML Workspace, first you need access to an Azure Subscription.  You can [create your own](https://azure.microsoft.com/en-us/free/) or get your existing subscription information from the [Azure portal](https://portal.azure.com).\n",
-    "\n",
-    "First login to azure and follow prompts to authenticate. Then check that your subscription is correct"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!az login"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!az account show"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you have multiple subscriptions and need to change the active one, you can use a command\n",
-    "```shell\n",
-    "az account set -s <subscription-id>\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Register Machine Learning Services Resource Provider\n",
-    "\n",
-    "This step is required to use the Azure ML services backing the SDK."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# register the new RP\n",
-    "!az provider register -n Microsoft.MachineLearningServices\n",
-    "\n",
-    "# check the registration status\n",
-    "!az provider show -n Microsoft.MachineLearningServices"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Check core SDK version number for validate your installation and for debugging purposes"
-   ]
-  },
-  {
-   "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 AML Workspace is an Azure resource that organaizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an AML 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 AML library and specify following information:\n",
-    "* A name for your workspace. You can choose one.\n",
-    "* Your subscription id. Use *id* value from *az account show* output above. \n",
-    "* The resource group name. Resource group organizes Azure resources and provides default region for the resources in the group. You can either specify a new one, in which case it gets created for your Workspace, or use an existing one or create a new one from [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 AML Workspace you want to use, you can skip this cell.  Otherwise, this cell will create an AML workspace for you in a 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 admin to provide you with the appropriate permissions or to provision the required resources.\n",
-    "\n",
-    "**Note** The workspace creation can take several minutes."
-   ]
-  },
-  {
-   "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.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()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can then load the workspace from this config file from any notebook in the current directory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# load workspace configuratio from ./aml_config/config.json file.\n",
-    "my_workspace = Workspace.from_config()\n",
-    "my_workspace.get_details()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create a folder to host all sample projects\n",
-    "Lastly, 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": [
-    "## Success!\n",
-    "Great, you are ready to move on to the rest of the sample notebooks."
-   ]
-  }
- ],
- "metadata": {
-  "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
-}

automl/01.auto-ml-classification.ipynb

@@ -1,399 +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 01: Classification with local compute\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.\n",
-    "\n",
-    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
-    "\n",
-    "In this notebook you would see\n",
-    "1. Creating an Experiment in an existing Workspace\n",
-    "2. Instantiating AutoMLConfig\n",
-    "3. Training the Model using local compute\n",
-    "4. Exploring the results\n",
-    "5. Testing the fitted model\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Experiment\n",
-    "\n",
-    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import logging\n",
-    "import os\n",
-    "import random\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from sklearn import datasets\n",
-    "\n",
-    "import azureml.core\n",
-    "from azureml.core.experiment import Experiment\n",
-    "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ws = Workspace.from_config()\n",
-    "\n",
-    "# choose a name for experiment\n",
-    "experiment_name = 'automl-local-classification'\n",
-    "# project folder\n",
-    "project_folder = './sample_projects/automl-local-classification'\n",
-    "\n",
-    "experiment=Experiment(ws, experiment_name)\n",
-    "\n",
-    "output = {}\n",
-    "output['SDK version'] = azureml.core.VERSION\n",
-    "output['Subscription ID'] = ws.subscription_id\n",
-    "output['Workspace 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 Digits Dataset"
-   ]
-  },
-  {
-   "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_digits = digits.data[100:,:]\n",
-    "y_digits = digits.target[100:]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instantiate Auto ML Config\n",
-    "\n",
-    "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
-    "\n",
-    "|Property|Description|\n",
-    "|-|-|\n",
-    "|**task**|classification or regression|\n",
-    "|**primary_metric**|This is the metric that you want to optimize.<br> 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",
-    "|**max_time_sec**|Time limit in seconds for each iteration|\n",
-    "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data |\n",
-    "|**n_cross_validations**|Number of cross validation splits|\n",
-    "|**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",
-    "                             max_time_sec = 3600,\n",
-    "                             iterations = 50,\n",
-    "                             n_cross_validations = 3,\n",
-    "                             verbosity = logging.INFO,\n",
-    "                             X = X_digits, \n",
-    "                             y = y_digits,\n",
-    "                             path=project_folder)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Training the Model\n",
-    "\n",
-    "You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
-    "You will see the currently running iterations printing to the console."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "local_run = experiment.submit(automl_config, show_output=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Optionally, you can continue an interrupted local run by calling continue_experiment without the <b>iterations</b> parameter, or run more iterations to a completed run by specifying the <b>iterations</b> parameter:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "local_run"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "local_run = local_run.continue_experiment(X = X_digits, \n",
-    "                                          y = y_digits, \n",
-    "                                          show_output = True,\n",
-    "                                          iterations = 5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exploring the results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Widget for monitoring runs\n",
-    "\n",
-    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
-    "\n",
-    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.train.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 on automl_classifier returns the best run and the fitted model for the last *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_run, fitted_model = local_run.get_output()\n",
-    "print(best_run)\n",
-    "print(fitted_model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Best Model based on any other metric\n",
-    "Give me the run and the model that has the smallest `log_loss`:"
-   ]
-  },
-  {
-   "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",
-    "Give me the run and the model from the 3rd 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": [
-    "### Testing the Fitted Model \n",
-    "\n",
-    "#### Load Test Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "digits = datasets.load_digits()\n",
-    "X_digits = digits.data[:10, :]\n",
-    "y_digits = digits.target[:10]\n",
-    "images = digits.images[:10]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Testing our best pipeline\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_digits), 2):\n",
-    "    print(index)\n",
-    "    predicted = fitted_model.predict(X_digits[index:index + 1])[0]\n",
-    "    label = y_digits[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": {
-  "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
-}

automl/02.auto-ml-regression.ipynb

@@ -1,409 +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 02: Regression with local compute\n",
-    "\n",
-    "In this example we use the scikit learn's [diabetes dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html) 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 would see\n",
-    "1. Creating an Experiment using an existing Workspace\n",
-    "2. Instantiating AutoMLConfig\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 logging\n",
-    "import os\n",
-    "import random\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from sklearn import datasets\n",
-    "\n",
-    "import azureml.core\n",
-    "from azureml.core.experiment import Experiment\n",
-    "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ws = Workspace.from_config()\n",
-    "\n",
-    "# choose a name for the experiment\n",
-    "experiment_name = 'automl-local-regression'\n",
-    "# project folder\n",
-    "project_folder = './sample_projects/automl-local-regression'\n",
-    "\n",
-    "experiment = Experiment(ws, experiment_name)\n",
-    "\n",
-    "output = {}\n",
-    "output['SDK version'] = azureml.core.VERSION\n",
-    "output['Subscription ID'] = ws.subscription_id\n",
-    "output['Workspace Name'] = ws.name\n",
-    "output['Resource Group'] = ws.resource_group\n",
-    "output['Location'] = ws.location\n",
-    "output['Project Directory'] = project_folder\n",
-    "output['Experiment Name'] = experiment.name\n",
-    "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data = output, index = ['']).T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnostics\n",
-    "\n",
-    "Opt-in diagnostics for better experience, quality, and security of future releases"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.telemetry import set_diagnostics_collection\n",
-    "set_diagnostics_collection(send_diagnostics=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Read Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# load diabetes dataset, a well-known built-in small dataset that comes with scikit-learn\n",
-    "from sklearn.datasets import load_diabetes\n",
-    "from sklearn.linear_model import Ridge\n",
-    "from sklearn.metrics import mean_squared_error\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "X, y = load_diabetes(return_X_y = True)\n",
-    "\n",
-    "columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
-    "\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instantiate Auto ML Config\n",
-    "\n",
-    "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
-    "\n",
-    "|Property|Description|\n",
-    "|-|-|\n",
-    "|**task**|classification or regression|\n",
-    "|**primary_metric**|This is the metric that you want to optimize.<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>|\n",
-    "|**max_time_sec**|Time limit in seconds for each iteration|\n",
-    "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data|\n",
-    "|**n_cross_validations**|Number of cross validation splits|\n",
-    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
-    "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "automl_config = AutoMLConfig(task='regression',\n",
-    "                             max_time_sec = 600,\n",
-    "                             iterations = 10,\n",
-    "                             primary_metric = 'spearman_correlation', \n",
-    "                             n_cross_validations = 5,\n",
-    "                             debug_log = 'automl.log',\n",
-    "                             verbosity = logging.INFO,\n",
-    "                             X = X_train, \n",
-    "                             y = y_train,\n",
-    "                             path=project_folder)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 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": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "local_run"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exploring the results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Widget for monitoring runs\n",
-    "\n",
-    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
-    "\n",
-    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.train.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 on automl_classifier returns the best run and the fitted model for the last *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_run, fitted_model = local_run.get_output()\n",
-    "print(best_run)\n",
-    "print(fitted_model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Best Model based on any other metric\n",
-    "Show the run and model that has the smallest `root_mean_squared_error` (which turned out to be the same as the one with largest `spearman_correlation` value):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lookup_metric = \"root_mean_squared_error\"\n",
-    "best_run, fitted_model = local_run.get_output(metric=lookup_metric)\n",
-    "print(best_run)\n",
-    "print(fitted_model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Model from a specific iteration\n",
-    "\n",
-    "Simply show the run and model from the 3rd 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": [
-    "### Testing the Fitted Model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Predict on training and test set, and calculate residual values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_pred_train = fitted_model.predict(X_train)\n",
-    "y_residual_train = y_train - y_pred_train\n",
-    "\n",
-    "y_pred_test = fitted_model.predict(X_test)\n",
-    "y_residual_test = y_test - y_pred_test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "from sklearn import datasets\n",
-    "from sklearn.metrics import mean_squared_error, r2_score\n",
-    "\n",
-    "# set up a multi-plot chart\n",
-    "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[1, 1], 'wspace':0, 'hspace': 0})\n",
-    "f.suptitle('Regression Residual Values', fontsize = 18)\n",
-    "f.set_figheight(6)\n",
-    "f.set_figwidth(16)\n",
-    "\n",
-    "# plot residual values of training set\n",
-    "a0.axis([0, 360, -200, 200])\n",
-    "a0.plot(y_residual_train, 'bo', alpha = 0.5)\n",
-    "a0.plot([-10,360],[0,0], 'r-', lw = 3)\n",
-    "a0.text(16,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_train, y_pred_train))), fontsize = 12)\n",
-    "a0.text(16,140,'R2 score = {0:.2f}'.format(r2_score(y_train, y_pred_train)), fontsize = 12)\n",
-    "a0.set_xlabel('Training samples', fontsize = 12)\n",
-    "a0.set_ylabel('Residual Values', fontsize = 12)\n",
-    "# plot histogram\n",
-    "a0.hist(y_residual_train, orientation = 'horizontal', color = 'b', bins = 10, histtype = 'step');\n",
-    "a0.hist(y_residual_train, orientation = 'horizontal', color = 'b', alpha = 0.2, bins = 10);\n",
-    "\n",
-    "# plot residual values of test set\n",
-    "a1.axis([0, 90, -200, 200])\n",
-    "a1.plot(y_residual_test, 'bo', alpha = 0.5)\n",
-    "a1.plot([-10,360],[0,0], 'r-', lw = 3)\n",
-    "a1.text(5,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_test, y_pred_test))), fontsize = 12)\n",
-    "a1.text(5,140,'R2 score = {0:.2f}'.format(r2_score(y_test, y_pred_test)), fontsize = 12)\n",
-    "a1.set_xlabel('Test samples', fontsize = 12)\n",
-    "a1.set_yticklabels([])\n",
-    "# plot histogram\n",
-    "a1.hist(y_residual_test, orientation = 'horizontal', color = 'b', bins = 10, histtype = 'step');\n",
-    "a1.hist(y_residual_test, orientation = 'horizontal', color = 'b', alpha = 0.2, bins = 10);\n",
-    "\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "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
-}

automl/03.auto-ml-remote-execution.ipynb

@@ -1,471 +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 03:  Remote Execution using DSVM (Ubuntu)\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.\n",
-    "\n",
-    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
-    "\n",
-    "In this notebook you would see\n",
-    "1. Creating an Experiment using an existing Workspace\n",
-    "2. Attaching an existing DSVM to a workspace\n",
-    "3. Instantiating AutoMLConfig \n",
-    "4. Training the Model using the DSVM\n",
-    "5. Exploring the results\n",
-    "6. Testing the fitted model\n",
-    "\n",
-    "In addition this notebook showcases the following features\n",
-    "- **Parallel** Executions for iterations\n",
-    "- Asyncronous tracking of progress\n",
-    "- **Cancelling** individual iterations or the entire run\n",
-    "- Retrieving models for any iteration or logged metric\n",
-    "- specify automl settings as **kwargs**\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Experiment\n",
-    "\n",
-    "As part of the setup you have already created a workspace. For AutoML you would need to create a <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import logging\n",
-    "import os\n",
-    "import random\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from sklearn import datasets\n",
-    "\n",
-    "import azureml.core\n",
-    "from azureml.core.experiment import Experiment\n",
-    "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ws = Workspace.from_config()\n",
-    "\n",
-    "# choose a name for the run history container in the workspace\n",
-    "experiment_name = 'automl-remote-dsvm4'\n",
-    "# project folder\n",
-    "project_folder = './sample_projects/automl-remote-dsvm4'\n",
-    "\n",
-    "experiment=Experiment(ws, experiment_name)\n",
-    "\n",
-    "output = {}\n",
-    "output['SDK version'] = azureml.core.VERSION\n",
-    "output['Subscription ID'] = ws.subscription_id\n",
-    "output['Workspace Name'] = ws.name\n",
-    "output['Resource Group'] = ws.resource_group\n",
-    "output['Location'] = ws.location\n",
-    "output['Project Directory'] = project_folder\n",
-    "output['Experiment Name'] = experiment.name\n",
-    "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data = output, index = ['']).T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnostics\n",
-    "\n",
-    "Opt-in diagnostics for better experience, quality, and security of future releases"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.telemetry import set_diagnostics_collection\n",
-    "set_diagnostics_collection(send_diagnostics=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create a Remote Linux DSVM\n",
-    "Note: If creation fails with a message about Marketplace purchase eligibilty, go to portal.azure.com, start creating DSVM there, and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled it, you can exit without actually creating VM.\n",
-    "\n",
-    "**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you can switch to a different port (such as 5022), you can append the port number to the address. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.compute import DsvmCompute\n",
-    "\n",
-    "dsvm_name = 'mydsvm'\n",
-    "try:\n",
-    "    dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
-    "    print('found existing dsvm.')\n",
-    "except:\n",
-    "    print('creating new dsvm.')\n",
-    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
-    "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
-    "    dsvm_compute.wait_for_completion(show_output = True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Get Data File\n",
-    "For remote executions you should author a get_data.py file containing a get_data() function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if not os.path.exists(project_folder):\n",
-    "    os.makedirs(project_folder)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%writefile $project_folder/get_data.py\n",
-    "\n",
-    "from sklearn import datasets\n",
-    "from scipy import sparse\n",
-    "import numpy as np\n",
-    "\n",
-    "def get_data():\n",
-    "    \n",
-    "    digits = datasets.load_digits()\n",
-    "    X_digits = digits.data[100:,:]\n",
-    "    y_digits = digits.target[100:]\n",
-    "\n",
-    "    return { \"X\" : X_digits, \"y\" : y_digits }"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
-    "\n",
-    "You can specify automl_settings as **kwargs** as well. Also note that you can use the get_data() symantic for local excutions too. \n",
-    "\n",
-    "<i>Note: For Remote DSVM and Batch AI you cannot pass Numpy arrays directly to the fit method.</i>\n",
-    "\n",
-    "|Property|Description|\n",
-    "|-|-|\n",
-    "|**primary_metric**|This is the metric that you want to optimize.<br> 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",
-    "|**max_time_sec**|Time limit in seconds for each iteration|\n",
-    "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data|\n",
-    "|**n_cross_validations**|Number of cross validation splits|\n",
-    "|**concurrent_iterations**|Max number of iterations that would be executed in parallel.  This should be less than the number of cores on the DSVM."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "automl_settings = {\n",
-    "    \"max_time_sec\": 600,\n",
-    "    \"iterations\": 20,\n",
-    "    \"n_cross_validations\": 5,\n",
-    "    \"primary_metric\": 'AUC_weighted',\n",
-    "    \"preprocess\": False,\n",
-    "    \"concurrent_iterations\": 2,\n",
-    "    \"verbosity\": logging.INFO\n",
-    "}\n",
-    "\n",
-    "automl_config = AutoMLConfig(task = 'classification',\n",
-    "                             debug_log = 'automl_errors.log',\n",
-    "                             path=project_folder, \n",
-    "                             compute_target = dsvm_compute,\n",
-    "                             data_script = project_folder + \"/get_data.py\",\n",
-    "                             **automl_settings\n",
-    "                            )\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<b>Note</b> that the first run on a new DSVM may take a several minutes to preparing the environment."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "remote_run = experiment.submit(automl_config, show_output=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exploring the Results\n",
-    "\n",
-    "#### Loading executed runs\n",
-    "In case you need to load a previously executed run given a run id please enable the below cell"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "remote_run = AutoMLRun(experiment=experiment, run_id='AutoML_480d3ed6-fc94-44aa-8f4e-0b945db9d3ef')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Widget for monitoring runs\n",
-    "\n",
-    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
-    "\n",
-    "You can click on a pipeline to see run properties and output logs.  Logs are also available on the DSVM under /tmp/azureml_run/{iterationid}/azureml-logs\n",
-    "\n",
-    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.train.widgets import RunDetails\n",
-    "RunDetails(remote_run).show() "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wait till the run finishes\n",
-    "remote_run.wait_for_completion(show_output = True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "#### Retrieve All Child Runs\n",
-    "You can also use sdk methods to fetch all the child runs and see individual metrics that we log. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "children = list(remote_run.get_children())\n",
-    "metricslist = {}\n",
-    "for run in children:\n",
-    "    properties = run.get_properties()\n",
-    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
-    "    metricslist[int(properties['iteration'])] = metrics\n",
-    "\n",
-    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
-    "rundata"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Canceling runs\n",
-    "\n",
-    "You can cancel ongoing remote runs using the *cancel()* and *cancel_iteration()* functions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Cancel the ongoing experiment and stop scheduling new iterations\n",
-    "# remote_run.cancel()\n",
-    "\n",
-    "# Cancel iteration 1 and move onto iteration 2\n",
-    "# remote_run.cancel_iteration(1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Retrieve the Best Model\n",
-    "\n",
-    "Below we select the best pipeline from our iterations. The *get_output* method 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 = remote_run.get_output()\n",
-    "print(best_run)\n",
-    "print(fitted_model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Best Model based on any other metric\n",
-    "Show the run/model which has the smallest `log_loss` value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lookup_metric = \"log_loss\"\n",
-    "best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
-    "print(best_run)\n",
-    "print(fitted_model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Model from a specific iteration\n",
-    "Show the run and model from the 3rd iteration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iteration = 3\n",
-    "third_run, third_model = remote_run.get_output(iteration=iteration)\n",
-    "print(third_run)\n",
-    "print(third_model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n",
-    "\n",
-    "#### Load Test Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "digits = datasets.load_digits()\n",
-    "X_digits = digits.data[:10, :]\n",
-    "y_digits = digits.target[:10]\n",
-    "images = digits.images[:10]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Testing our best pipeline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Randomly select digits and test\n",
-    "for index in np.random.choice(len(y_digits), 2):\n",
-    "    print(index)\n",
-    "    predicted = fitted_model.predict(X_digits[index:index + 1])[0]\n",
-    "    label = y_digits[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": {
-  "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
-}

automl/03b.auto-ml-remote-batchai.ipynb

@@ -1,522 +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 03:  Remote Execution using Batch AI\n",
-    "\n",
-    "In this example we use the scikit learn's [diabetes dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html) to showcase how you can use AutoML for a simple classification problem.\n",
-    "\n",
-    "Make sure you have executed the [setup](setup.ipynb) before running this notebook.\n",
-    "\n",
-    "In this notebook you would see\n",
-    "1. Creating an Experiment using an existing Workspace\n",
-    "2. Attaching an existing Batch AI compute to a workspace\n",
-    "3. Instantiating AutoMLConfig \n",
-    "4. Training the Model using the Batch AI\n",
-    "5. Exploring the results\n",
-    "6. Testing the fitted model\n",
-    "\n",
-    "In addition this notebook showcases the following features\n",
-    "- **Parallel** Executions for iterations\n",
-    "- Asyncronous tracking of progress\n",
-    "- **Cancelling** individual iterations or the entire run\n",
-    "- Retrieving models for any iteration or logged metric\n",
-    "- specify automl settings as **kwargs**\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Experiment\n",
-    "\n",
-    "As part of the setup you have already created a workspace. For AutoML you would need to create a <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import logging\n",
-    "import os\n",
-    "import random\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from sklearn import datasets\n",
-    "\n",
-    "import azureml.core\n",
-    "from azureml.core.experiment import Experiment\n",
-    "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ws = Workspace.from_config()\n",
-    "\n",
-    "# choose a name for the run history container in the workspace\n",
-    "experiment_name = 'automl-remote-batchai'\n",
-    "# project folder\n",
-    "project_folder = './sample_projects/automl-remote-batchai'\n",
-    "\n",
-    "experiment=Experiment(ws, experiment_name)\n",
-    "\n",
-    "output = {}\n",
-    "output['SDK version'] = azureml.core.VERSION\n",
-    "output['Subscription ID'] = ws.subscription_id\n",
-    "output['Workspace Name'] = ws.name\n",
-    "output['Resource Group'] = ws.resource_group\n",
-    "output['Location'] = ws.location\n",
-    "output['Project Directory'] = project_folder\n",
-    "output['Experiment Name'] = experiment.name\n",
-    "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data = output, index = ['']).T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnostics\n",
-    "\n",
-    "Opt-in diagnostics for better experience, quality, and security of future releases"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.telemetry import set_diagnostics_collection\n",
-    "set_diagnostics_collection(send_diagnostics=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Batch AI Cluster\n",
-    "The cluster is created as Machine Learning Compute and will appear under your workspace.\n",
-    "\n",
-    "<b>Note</b>: The cluster creation can take over 10 minutes, please be patient.\n",
-    "\n",
-    "As with other Azure services, there are limits on certain resources (for eg. BatchAI cluster size) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.compute import BatchAiCompute\n",
-    "from azureml.core.compute import ComputeTarget\n",
-    "\n",
-    "# choose a name for your cluster\n",
-    "batchai_cluster_name = ws.name + \"cpu\"\n",
-    "\n",
-    "found = False\n",
-    "# see if this compute target already exists in the workspace\n",
-    "for ct in ws.compute_targets():\n",
-    "    print(ct.name, ct.type)\n",
-    "    if (ct.name == batchai_cluster_name and ct.type == 'BatchAI'):\n",
-    "        found = True\n",
-    "        print('found compute target. just use it.')\n",
-    "        compute_target = ct\n",
-    "        break\n",
-    "        \n",
-    "if not found:\n",
-    "    print('creating a new compute target...')\n",
-    "    provisioning_config = BatchAiCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-    "                                                                #vm_priority = 'lowpriority', # optional\n",
-    "                                                                autoscale_enabled = True,\n",
-    "                                                                cluster_min_nodes = 1, \n",
-    "                                                                cluster_max_nodes = 4)\n",
-    "\n",
-    "    # create the cluster\n",
-    "    compute_target = ComputeTarget.create(ws,batchai_cluster_name, provisioning_config)\n",
-    "    \n",
-    "    # can poll for a minimum number of nodes and for a specific timeout. \n",
-    "    # if no min node count is provided it will use the scale settings for the cluster\n",
-    "    compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
-    "    \n",
-    "     # For a more detailed view of current BatchAI cluster status, use the 'status' property    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Get Data File\n",
-    "For remote executions you should author a get_data.py file containing a get_data() function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if not os.path.exists(project_folder):\n",
-    "    os.makedirs(project_folder)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%writefile $project_folder/get_data.py\n",
-    "\n",
-    "from sklearn import datasets\n",
-    "from scipy import sparse\n",
-    "import numpy as np\n",
-    "\n",
-    "def get_data():\n",
-    "    \n",
-    "    digits = datasets.load_digits()\n",
-    "    X_digits = digits.data\n",
-    "    y_digits = digits.target\n",
-    "\n",
-    "    return { \"X\" : X_digits, \"y\" : y_digits }"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
-    "\n",
-    "You can specify automl_settings as **kwargs** as well. Also note that you can use the get_data() symantic for local excutions too. \n",
-    "\n",
-    "<i>Note: For Remote DSVM and Batch AI you cannot pass Numpy arrays directly to the fit method.</i>\n",
-    "\n",
-    "|Property|Description|\n",
-    "|-|-|\n",
-    "|**primary_metric**|This is the metric that you want to optimize.<br> 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",
-    "|**max_time_sec**|Time limit in seconds for each iteration|\n",
-    "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data|\n",
-    "|**n_cross_validations**|Number of cross validation splits|\n",
-    "|**concurrent_iterations**|Max number of iterations that would be executed in parallel.  This should be less than the number of cores on the DSVM."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "automl_settings = {\n",
-    "    \"max_time_sec\": 120,\n",
-    "    \"iterations\": 20,\n",
-    "    \"n_cross_validations\": 5,\n",
-    "    \"primary_metric\": 'AUC_weighted',\n",
-    "    \"preprocess\": False,\n",
-    "    \"concurrent_iterations\": 5,\n",
-    "    \"verbosity\": logging.INFO\n",
-    "}\n",
-    "\n",
-    "automl_config = AutoMLConfig(task = 'classification',\n",
-    "                             debug_log = 'automl_errors.log',\n",
-    "                             path=project_folder,\n",
-    "                             compute_target = compute_target,\n",
-    "                             data_script = project_folder + \"/get_data.py\",\n",
-    "                             **automl_settings\n",
-    "                            )\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "remote_run = experiment.submit(automl_config, show_output=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exploring the Results\n",
-    "\n",
-    "#### Loading executed runs\n",
-    "In case you need to load a previously executed run given a run id please enable the below cell"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "remote_run = AutoMLRun(experiment=experiment, run_id='AutoML_5db13491-c92a-4f1d-b622-8ab8d973a058')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Widget for monitoring runs\n",
-    "\n",
-    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
-    "\n",
-    "You can click on a pipeline to see run properties and output logs.  Logs are also available on the DSVM under /tmp/azureml_run/{iterationid}/azureml-logs\n",
-    "\n",
-    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "remote_run"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.train.widgets import RunDetails\n",
-    "RunDetails(remote_run).show() "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# wait till the run finishes\n",
-    "remote_run.wait_for_completion(show_output = True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "#### Retrieve All Child Runs\n",
-    "You can also use sdk methods to fetch all the child runs and see individual metrics that we log. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "children = list(remote_run.get_children())\n",
-    "metricslist = {}\n",
-    "for run in children:\n",
-    "    properties = run.get_properties()\n",
-    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
-    "    metricslist[int(properties['iteration'])] = metrics\n",
-    "\n",
-    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
-    "rundata"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Canceling runs\n",
-    "\n",
-    "You can cancel ongoing remote runs using the *cancel()* and *cancel_iteration()* functions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Cancel the ongoing experiment and stop scheduling new iterations\n",
-    "# remote_run.cancel()\n",
-    "\n",
-    "# Cancel iteration 1 and move onto iteration 2\n",
-    "# remote_run.cancel_iteration(1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Retrieve the Best Model\n",
-    "\n",
-    "Below we select the best pipeline from our iterations. The *get_output* method 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 = remote_run.get_output()\n",
-    "print(best_run)\n",
-    "print(fitted_model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Best Model based on any other metric\n",
-    "Show the run/model which has the smallest `log_loss` value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lookup_metric = \"log_loss\"\n",
-    "best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
-    "print(best_run)\n",
-    "print(fitted_model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Model from a specific iteration\n",
-    "Show the run and model from the 3rd iteration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iteration = 3\n",
-    "third_run, third_model = remote_run.get_output(iteration=iteration)\n",
-    "print(third_run)\n",
-    "print(third_model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Register fitted model for deployment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "description = 'AutoML Model'\n",
-    "tags = None\n",
-    "remote_run.register_model(description=description, tags=tags)\n",
-    "remote_run.model_id # Use this id to deploy the model as a web service in Azure"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n",
-    "\n",
-    "#### Load Test Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "digits = datasets.load_digits()\n",
-    "X_digits = digits.data[:10, :]\n",
-    "y_digits = digits.target[:10]\n",
-    "images = digits.images[:10]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Testing our best pipeline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Randomly select digits and test\n",
-    "for index in np.random.choice(len(y_digits), 2):\n",
-    "    print(index)\n",
-    "    predicted = fitted_model.predict(X_digits[index:index + 1])[0]\n",
-    "    label = y_digits[index]\n",
-    "    title = \"Label value = %d  Predicted value = %d \" % ( label,predicted)\n",
-    "    fig = plt.figure(1, figsize=(3,3))\n",
-    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
-    "    ax1.set_title(title)\n",
-    "    plt.imshow(images[index], cmap=plt.cm.gray_r, interpolation='nearest')\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "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
-}

automl/04.auto-ml-remote-execution-text-data-blob-store.ipynb

@@ -1,495 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-    "\n",
-    "Licensed under the MIT License."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Auto ML : Remote Execution with Text data from Blobstorage\n",
-    "\n",
-    "In this example we use the [Burning Man 2016 dataset](https://innovate.burningman.org/datasets-page/) to showcase how you can use AutoML to handle text data from a Azure blobstorage.\n",
-    "\n",
-    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
-    "\n",
-    "In this notebook you would see\n",
-    "1. Creating an Experiment using an existing Workspace\n",
-    "2. Attaching an existing DSVM to a workspace\n",
-    "3. Instantiating AutoMLConfig \n",
-    "4. Training the Model using the DSVM\n",
-    "5. Exploring the results\n",
-    "6. Testing the fitted model\n",
-    "\n",
-    "In addition this notebook showcases the following features\n",
-    "- **Parallel** Executions for iterations\n",
-    "- Asyncronous tracking of progress\n",
-    "- **Cancelling** individual iterations or the entire run\n",
-    "- Retrieving models for any iteration or logged metric\n",
-    "- specify automl settings as **kwargs**\n",
-    "- handling **text** data with **preprocess** flag\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Experiment\n",
-    "\n",
-    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import logging\n",
-    "import os\n",
-    "import random\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from sklearn import datasets\n",
-    "\n",
-    "import azureml.core\n",
-    "from azureml.core.experiment import Experiment\n",
-    "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ws = Workspace.from_config()\n",
-    "\n",
-    "# choose a name for the run history container in the workspace\n",
-    "experiment_name = 'automl-remote-dsvm-blobstore'\n",
-    "# project folder\n",
-    "project_folder = './sample_projects/automl-remote-dsvm-blobstore'\n",
-    "\n",
-    "experiment = Experiment(ws, experiment_name)\n",
-    "\n",
-    "output = {}\n",
-    "output['SDK version'] = azureml.core.VERSION\n",
-    "output['Subscription ID'] = ws.subscription_id\n",
-    "output['Workspace'] = ws.name\n",
-    "output['Resource Group'] = ws.resource_group\n",
-    "output['Location'] = ws.location\n",
-    "output['Project Directory'] = project_folder\n",
-    "output['Experiment Name'] = experiment.name\n",
-    "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data=output, index=['']).T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnostics\n",
-    "\n",
-    "Opt-in diagnostics for better experience, quality, and security of future releases"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.telemetry import set_diagnostics_collection\n",
-    "set_diagnostics_collection(send_diagnostics=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Attach a Remote Linux DSVM\n",
-    "To use remote docker commpute target:\n",
-    "1. Create a Linux DSVM in Azure. Here is some [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_runs. 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, username and password below\n",
-    "\n",
-    "**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you can switch to a different port (such as 5022), you can append the port number to the address. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.compute import RemoteCompute\n",
-    "\n",
-    "# Add your VM information below\n",
-    "dsvm_name     = 'mydsvm1'\n",
-    "dsvm_ip_addr  = '<<ip_addr>>'\n",
-    "dsvm_username = '<<username>>'\n",
-    "dsvm_password = '<<password>>'\n",
-    "\n",
-    "dsvm_compute = RemoteCompute.attach(workspace=ws, name=dsvm_name, address=dsvm_ip_addr, username=dsvm_username, password=dsvm_password, ssh_port=22)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Get Data File\n",
-    "For remote executions you should author a get_data.py file containing a get_data() function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
-    "\n",
-    "The *get_data()* function returns a [dictionary](README.md#getdata)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if not os.path.exists(project_folder):\n",
-    "    os.makedirs(project_folder)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%writefile $project_folder/get_data.py\n",
-    "\n",
-    "import pandas as pd\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "\n",
-    "def get_data():\n",
-    "    # Burning man 2016 data\n",
-    "    df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
-    "                     delimiter=\"\\t\", quotechar='\"')\n",
-    "    # get integer labels\n",
-    "    le = LabelEncoder()\n",
-    "    le.fit(df[\"Label\"].values)\n",
-    "    y = le.transform(df[\"Label\"].values)\n",
-    "    df = df.drop([\"Label\"], axis=1)\n",
-    "\n",
-    "    df_train, _, y_train, _ = train_test_split(df, y, test_size=0.1, random_state=42)\n",
-    "\n",
-    "    return { \"X\" : df, \"y\" : y }"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### View data\n",
-    "\n",
-    "You can execute the *get_data()* function locally to view the *train* data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%run  $project_folder/get_data.py\n",
-    "data_dict = get_data()\n",
-    "df = data_dict[\"X\"]\n",
-    "y = data_dict[\"y\"]\n",
-    "pd.set_option('display.max_colwidth', 15)\n",
-    "df['Label'] = pd.Series(y, index=df.index)\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
-    "\n",
-    "You can specify automl_settings as **kwargs** as well. Also note that you can use the get_data() symantic for local excutions too. \n",
-    "\n",
-    "<i>Note: For Remote DSVM and Batch AI you cannot pass Numpy arrays directly to the fit method.</i>\n",
-    "\n",
-    "|Property|Description|\n",
-    "|-|-|\n",
-    "|**primary_metric**|This is the metric that you want to optimize.<br> 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",
-    "|**max_time_sec**|Time limit in seconds for each iteration|\n",
-    "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data|\n",
-    "|**n_cross_validations**|Number of cross validation splits|\n",
-    "|**concurrent_iterations**|Max number of iterations that would be executed in parallel.  This should be less than the number of cores on the DSVM\n",
-    "|**preprocess**| *True/False* <br>Setting this to *True* enables AutoML to perform preprocessing <br>on the input to handle *missing data*, and perform some common *feature extraction*|\n",
-    "|**max_cores_per_iteration**| Indicates how many cores on the compute target would be used to train a single pipeline.<br> Default is *1*, you can set it to *-1* to use all cores|"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "automl_settings = {\n",
-    "    \"max_time_sec\": 3600,\n",
-    "    \"iterations\": 10,\n",
-    "    \"n_cross_validations\": 5,\n",
-    "    \"primary_metric\": 'AUC_weighted',\n",
-    "    \"preprocess\": True,\n",
-    "    \"max_cores_per_iteration\": 2\n",
-    "}\n",
-    "\n",
-    "automl_config = AutoMLConfig(task = 'classification',\n",
-    "                             path=project_folder,\n",
-    "                             compute_target = dsvm_compute,\n",
-    "                             data_script = project_folder + \"/get_data.py\",\n",
-    "                             **automl_settings\n",
-    "                            )\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Training the Model <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
-    "\n",
-    "For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets/models even when the experiment is running to retreive the best model up to that point. Once you are satisfied with the model you can cancel a particular iteration or the whole run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "remote_run = experiment.submit(automl_config)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
-    "#### Widget for monitoring runs\n",
-    "\n",
-    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
-    "\n",
-    "You can click on a pipeline to see run properties and output logs. Logs are also available on the DSVM under /tmp/azureml_run/{iterationid}/azureml-logs\n",
-    "\n",
-    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.train.widgets import RunDetails\n",
-    "RunDetails(remote_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(remote_run.get_children())\n",
-    "metricslist = {}\n",
-    "for run in children:\n",
-    "    properties = run.get_properties()\n",
-    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
-    "    metricslist[int(properties['iteration'])] = metrics\n",
-    "\n",
-    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
-    "rundata"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Canceling runs\n",
-    "You can cancel ongoing remote runs using the *cancel()* and *cancel_iteration()* functions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Cancel the ongoing experiment and stop scheduling new iterations\n",
-    "remote_run.cancel()\n",
-    "\n",
-    "# Cancel iteration 1 and move onto iteration 2\n",
-    "# remote_run.cancel_iteration(1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Retrieve the Best Model\n",
-    "\n",
-    "Below we select the best pipeline from our iterations. The *get_output* method on automl_classifier returns the best run and the fitted model for the last *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_run, fitted_model = remote_run.get_output()\n",
-    "print(best_run)\n",
-    "print(fitted_model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Best Model based on any other metric"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# lookup_metric = \"accuracy\"\n",
-    "# best_run, fitted_model = remote_run.get_output(metric=lookup_metric)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Model from a specific iteration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iteration = 0\n",
-    "zero_run, zero_model = remote_run.get_output(iteration=iteration)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Register fitted model for deployment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "description = 'AutoML Model'\n",
-    "tags = None\n",
-    "remote_run.register_model(description=description, tags=tags)\n",
-    "remote_run.model_id # Use this id to deploy the model as a web service in Azure"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sklearn\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "from pandas_ml import ConfusionMatrix\n",
-    "\n",
-    "df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
-    "                     delimiter=\"\\t\", quotechar='\"')\n",
-    "\n",
-    "# get integer labels\n",
-    "le = LabelEncoder()\n",
-    "le.fit(df[\"Label\"].values)\n",
-    "y = le.transform(df[\"Label\"].values)\n",
-    "df = df.drop([\"Label\"], axis=1)\n",
-    "\n",
-    "_, df_test, _, y_test = train_test_split(df, y, test_size=0.1, random_state=42)\n",
-    "\n",
-    "\n",
-    "ypred = fitted_model.predict(df_test.values)\n",
-    "\n",
-    "\n",
-    "ypred_strings = le.inverse_transform(ypred)\n",
-    "ytest_strings = le.inverse_transform(y_test)\n",
-    "\n",
-    "cm = ConfusionMatrix(ytest_strings, ypred_strings)\n",
-    "\n",
-    "print(cm)\n",
-    "\n",
-    "cm.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "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
-}

automl/05.auto-ml-missing-data-Blacklist-Early-Termination.ipynb

@@ -1,396 +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 05 : Blacklisting models, Early termination and handling missing data\n",
-    "\n",
-    "In this example we use the scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) 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 metric 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 black list of algos that AutoML will ignore for this run.\n",
-    "\n",
-    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
-    "\n",
-    "In this notebook you would see\n",
-    "1. Creating an Experiment using an existing Workspace\n",
-    "2. Instantiating AutoMLConfig\n",
-    "4. Training the Model\n",
-    "5. Exploring the results\n",
-    "6. Testing the fitted model\n",
-    "\n",
-    "In addition this notebook showcases the following features\n",
-    "- **Blacklist** certain pipelines\n",
-    "- Specify a **target metrics** to indicate stopping criteria\n",
-    "- Handling **Missing Data** in the input\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Create Experiment\n",
-    "\n",
-    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import logging\n",
-    "import os\n",
-    "import random\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from sklearn import datasets\n",
-    "\n",
-    "import azureml.core\n",
-    "from azureml.core.experiment import Experiment\n",
-    "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ws = Workspace.from_config()\n",
-    "\n",
-    "# choose a name for the experiment\n",
-    "experiment_name = 'automl-local-missing-data'\n",
-    "# project folder\n",
-    "project_folder = './sample_projects/automl-local-missing-data'\n",
-    "\n",
-    "experiment=Experiment(ws, experiment_name)\n",
-    "\n",
-    "output = {}\n",
-    "output['SDK version'] = azureml.core.VERSION\n",
-    "output['Subscription ID'] = ws.subscription_id\n",
-    "output['Workspace'] = ws.name\n",
-    "output['Resource Group'] = ws.resource_group\n",
-    "output['Location'] = ws.location\n",
-    "output['Project Directory'] = project_folder\n",
-    "output['Experiment Name'] = experiment.name\n",
-    "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data=output, index=['']).T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnostics\n",
-    "\n",
-    "Opt-in diagnostics for better experience, quality, and security of future releases"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.telemetry import set_diagnostics_collection\n",
-    "set_diagnostics_collection(send_diagnostics=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating Missing Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy import sparse\n",
-    "\n",
-    "digits = datasets.load_digits()\n",
-    "X_digits = digits.data[10:,:]\n",
-    "y_digits = digits.target[10:]\n",
-    "\n",
-    "# Add missing values in 75% of the lines\n",
-    "missing_rate = 0.75\n",
-    "n_missing_samples = int(np.floor(X_digits.shape[0] * missing_rate))\n",
-    "missing_samples = np.hstack((np.zeros(X_digits.shape[0] - n_missing_samples, dtype=np.bool), np.ones(n_missing_samples, dtype=np.bool)))\n",
-    "rng = np.random.RandomState(0)\n",
-    "rng.shuffle(missing_samples)\n",
-    "missing_features = rng.randint(0, X_digits.shape[1], n_missing_samples)\n",
-    "X_digits[np.where(missing_samples)[0], missing_features] = np.nan"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.DataFrame(data=X_digits)\n",
-    "df['Label'] = pd.Series(y_digits, index=df.index)\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instantiate Auto ML Config\n",
-    "\n",
-    "\n",
-    "This defines the settings and data used to run the experiment.\n",
-    "\n",
-    "|Property|Description|\n",
-    "|-|-|\n",
-    "|**task**|classification or regression|\n",
-    "|**primary_metric**|This is the metric that you want to optimize.<br> 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",
-    "|**max_time_sec**|Time limit in seconds for each iteration|\n",
-    "|**iterations**|Number of iterations. In each iteration Auto ML trains the data with a specific pipeline|\n",
-    "|**n_cross_validations**|Number of cross validation splits|\n",
-    "|**preprocess**| *True/False* <br>Setting this to *True* enables Auto ML to perform preprocessing <br>on the input to handle *missing data*, and perform some common *feature extraction*|\n",
-    "|**exit_score**|*double* value indicating the target for *primary_metric*. <br> Once the target is surpassed the run terminates|\n",
-    "|**blacklist_algos**|*Array* of *strings* indicating pipelines to ignore for Auto ML.<br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGDClassifierWrapper</i><br><i>NBWrapper</i><br><i>BernoulliNB</i><br><i>SVCWrapper</i><br><i>LinearSVMWrapper</i><br><i>KNeighborsClassifier</i><br><i>DecisionTreeClassifier</i><br><i>RandomForestClassifier</i><br><i>ExtraTreesClassifier</i><br><i>LightGBMClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet<i><br><i>GradientBoostingRegressor<i><br><i>DecisionTreeRegressor<i><br><i>KNeighborsRegressor<i><br><i>LassoLars<i><br><i>SGDRegressor<i><br><i>RandomForestRegressor<i><br><i>ExtraTreesRegressor<i>|\n",
-    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
-    "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. |"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "automl_config = AutoMLConfig(task = 'classification',\n",
-    "                             debug_log = 'automl_errors.log',\n",
-    "                             primary_metric = 'AUC_weighted',\n",
-    "                             max_time_sec = 3600,\n",
-    "                             iterations = 20,\n",
-    "                             n_cross_validations = 5,\n",
-    "                             preprocess = True,\n",
-    "                             exit_score = 0.994,\n",
-    "                             blacklist_algos = ['KNeighborsClassifier','LinearSVMWrapper'],\n",
-    "                             verbosity = logging.INFO,\n",
-    "                             X = X_digits, \n",
-    "                             y = y_digits,\n",
-    "                             path=project_folder)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Training the Model\n",
-    "\n",
-    "You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
-    "You will see the currently running iterations printing to the console."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "local_run = experiment.submit(automl_config, show_output=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exploring the results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Widget for monitoring runs\n",
-    "\n",
-    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
-    "\n",
-    "NOTE: The widget will display a link at the bottom. This will not currently work, but will eventually link to a web-ui to explore the individual run details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.train.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. Each pipeline is a tuple of three elements. The first element is the score for the pipeline the second element is the string description of the pipeline and the last element are the pipeline objects used for each fold in the cross-validation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_run, fitted_model = local_run.get_output()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Best Model based on any other metric"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# lookup_metric = \"accuracy\"\n",
-    "# best_run, fitted_model = local_run.get_output(metric=lookup_metric)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Model from a specific iteration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# iteration = 3\n",
-    "# best_run, fitted_model = local_run.get_output(iteration=iteration)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Register fitted model for deployment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "description = 'AutoML Model'\n",
-    "tags = None\n",
-    "local_run.register_model(description=description, tags=tags)\n",
-    "local_run.model_id # Use this id to deploy the model as a web service in Azure"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Testing the Fitted Model "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "digits = datasets.load_digits()\n",
-    "X_digits = digits.data[:10, :]\n",
-    "y_digits = digits.target[:10]\n",
-    "images = digits.images[:10]\n",
-    "\n",
-    "#Randomly select digits and test\n",
-    "for index in np.random.choice(len(y_digits), 2):\n",
-    "    print(index)\n",
-    "    predicted = fitted_model.predict(X_digits[index:index + 1])[0]\n",
-    "    label = y_digits[index]\n",
-    "    title = \"Label value = %d  Predicted value = %d \" % ( label,predicted)\n",
-    "    fig = plt.figure(1, figsize=(3,3))\n",
-    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
-    "    ax1.set_title(title)\n",
-    "    plt.imshow(images[index], cmap=plt.cm.gray_r, interpolation='nearest')\n",
-    "    plt.show()\n"
-   ]
-  }
- ],
- "metadata": {
-  "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
-}

automl/06.auto-ml-sparse-data-custom-cv-split.ipynb

@@ -1,418 +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 06: Custom CV splits, handling sparse data\n",
-    "\n",
-    "In this example we use the scikit learn's [20newsgroup](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 handling sparse data and specify custom cross validation splits.\n",
-    "\n",
-    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
-    "\n",
-    "In this notebook you would see\n",
-    "1. Creating an Experiment using an existing Workspace\n",
-    "2. Instantiating AutoMLConfig\n",
-    "4. Training the Model\n",
-    "5. Exploring the results\n",
-    "6. Testing the fitted model\n",
-    "\n",
-    "In addition this notebook showcases the following features\n",
-    "- **Custom CV** splits \n",
-    "- Handling **Sparse Data** in the input"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Experiment\n",
-    "\n",
-    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import logging\n",
-    "import os\n",
-    "import random\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from sklearn import datasets\n",
-    "\n",
-    "import azureml.core\n",
-    "from azureml.core.experiment import Experiment\n",
-    "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ws = Workspace.from_config()\n",
-    "\n",
-    "# choose a name for the experiment\n",
-    "experiment_name = 'automl-local-missing-data'\n",
-    "# project folder\n",
-    "project_folder = './sample_projects/automl-local-missing-data'\n",
-    "\n",
-    "experiment = Experiment(ws, experiment_name)\n",
-    "\n",
-    "output = {}\n",
-    "output['SDK version'] = azureml.core.VERSION\n",
-    "output['Subscription ID'] = ws.subscription_id\n",
-    "output['Workspace'] = ws.name\n",
-    "output['Resource Group'] = ws.resource_group\n",
-    "output['Location'] = ws.location\n",
-    "output['Project Directory'] = project_folder\n",
-    "output['Experiment Name'] = experiment.name\n",
-    "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data=output, index=['']).T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnostics\n",
-    "\n",
-    "Opt-in diagnostics for better experience, quality, and security of future releases"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.telemetry import set_diagnostics_collection\n",
-    "set_diagnostics_collection(send_diagnostics=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Creating Sparse Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.datasets import fetch_20newsgroups\n",
-    "from sklearn.feature_extraction.text import HashingVectorizer\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "remove = ('headers', 'footers', 'quotes')\n",
-    "categories = [\n",
-    "    'alt.atheism',\n",
-    "    'talk.religion.misc',\n",
-    "    'comp.graphics',\n",
-    "    'sci.space',\n",
-    "]\n",
-    "data_train = fetch_20newsgroups(subset='train', categories=categories,\n",
-    "                                shuffle=True, random_state=42,\n",
-    "                                remove=remove)\n",
-    "\n",
-    "X_train, X_validation, y_train, y_validation = train_test_split(data_train.data, data_train.target, test_size=0.33, random_state=42)\n",
-    "\n",
-    "\n",
-    "vectorizer = HashingVectorizer(stop_words='english', alternate_sign=False,\n",
-    "                               n_features=2**16)\n",
-    "X_train = vectorizer.transform(X_train)\n",
-    "X_validation = vectorizer.transform(X_validation)\n",
-    "\n",
-    "summary_df = pd.DataFrame(index = ['No of Samples', 'No of Features'])\n",
-    "summary_df['Train Set'] = [X_train.shape[0], X_train.shape[1]]\n",
-    "summary_df['Validation Set'] = [X_validation.shape[0], X_validation.shape[1]]\n",
-    "summary_df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instantiate Auto ML Config\n",
-    "\n",
-    "This defines the settings and data used to run the experiment.\n",
-    "\n",
-    "|Property|Description|\n",
-    "|-|-|\n",
-    "|**task**|classification or regression|\n",
-    "|**primary_metric**|This is the metric that you want to optimize.<br> 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",
-    "|**max_time_sec**|Time limit in seconds for each iteration|\n",
-    "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data|\n",
-    "|**preprocess**| *True/False* <br>Setting this to *True* enables Auto ML to perform preprocessing <br>on the input to handle *missing data*, and perform some common *feature extraction*<br>*Note: If input data is Sparse you cannot use preprocess=True*|\n",
-    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
-    "|**X_valid**|(sparse) array-like, shape = [n_samples, n_features] for the custom Validation set|\n",
-    "|**y_valid**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. for the custom Validation set|\n",
-    "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "automl_config = AutoMLConfig(task = 'classification',\n",
-    "                             debug_log='automl_errors.log',\n",
-    "                             primary_metric='AUC_weighted',\n",
-    "                             max_time_sec=3600,\n",
-    "                             iterations=5,\n",
-    "                             preprocess=False,\n",
-    "                             verbosity=logging.INFO,\n",
-    "                             X = X_train, \n",
-    "                             y = y_train,\n",
-    "                             X_valid = X_validation, \n",
-    "                             y_valid = y_validation, \n",
-    "                             path=project_folder)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Training the Model\n",
-    "\n",
-    "You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
-    "You will see the currently running iterations printing to the console."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "local_run = experiment.submit(automl_config, show_output=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exploring the results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Widget for monitoring runs\n",
-    "\n",
-    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
-    "\n",
-    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.train.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": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Retrieve the Best Model\n",
-    "\n",
-    "Below we select the best pipeline from our iterations. The *get_output* method on automl_classifier returns the best run and the fitted model for the last *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_run, fitted_model = local_run.get_output()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Best Model based on any other metric"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# lookup_metric = \"accuracy\"\n",
-    "# best_run, fitted_model = local_run.get_output(metric=lookup_metric)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Model from a specific iteration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# iteration = 3\n",
-    "# best_run, fitted_model = local_run.get_output(iteration=iteration)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Register fitted model for deployment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "description = 'AutoML Model'\n",
-    "tags = None\n",
-    "local_run.register_model(description=description, tags=tags)\n",
-    "local_run.model_id # Use this id to deploy the model as a web service in Azure"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Testing the Fitted Model "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "digits = datasets.load_digits()### Testing the Fitted Model\n",
-    "\n",
-    "#### Load Test Data\n",
-    "import sklearn\n",
-    "from pandas_ml import ConfusionMatrix\n",
-    "\n",
-    "remove = ('headers', 'footers', 'quotes')\n",
-    "categories = [\n",
-    "    'alt.atheism',\n",
-    "    'talk.religion.misc',\n",
-    "    'comp.graphics',\n",
-    "    'sci.space',\n",
-    "]\n",
-    "\n",
-    "\n",
-    "data_test = fetch_20newsgroups(subset='test', categories=categories,\n",
-    "                                shuffle=True, random_state=42,\n",
-    "                                remove=remove)\n",
-    "\n",
-    "vectorizer = HashingVectorizer(stop_words='english', alternate_sign=False,\n",
-    "                               n_features=2**16)\n",
-    "\n",
-    "X_test = vectorizer.transform(data_test.data)\n",
-    "y_test = data_test.target\n",
-    "\n",
-    "#### Testing our best pipeline\n",
-    "\n",
-    "ypred = fitted_model.predict(X_test)\n",
-    "ypred_strings = [categories[i] for i in ypred]\n",
-    "ytest_strings = [categories[i] for i in y_test]\n",
-    "\n",
-    "cm = ConfusionMatrix(ytest_strings, ypred_strings)\n",
-    "print(cm)\n",
-    "cm.plot()"
-   ]
-  }
- ],
- "metadata": {
-  "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
-}

automl/07.auto-ml-exploring-previous-runs.ipynb

@@ -1,326 +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 07: Exploring previous runs\n",
-    "\n",
-    "In this example we present some examples on navigating previously executed runs. We also show how you can download a fitted model for any previous run.\n",
-    "\n",
-    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
-    "\n",
-    "In this notebook you would see\n",
-    "1. List all Experiments for the workspace\n",
-    "2. List AutoML runs for an Experiment\n",
-    "3. Get details for a AutoML Run. (Automl settings, run widget & all metrics)\n",
-    "4. Download fitted pipeline for any iteration\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# List all AutoML Experiments in a Workspace"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import logging\n",
-    "import os\n",
-    "import random\n",
-    "import re\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from sklearn import datasets\n",
-    "\n",
-    "import azureml.core\n",
-    "from azureml.core.experiment import Experiment\n",
-    "from azureml.core.run import Run\n",
-    "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ws = Workspace.from_config()\n",
-    "experiment_list = Experiment.list(workspace=ws)\n",
-    "\n",
-    "summary_df = pd.DataFrame(index = ['No of Runs'])\n",
-    "pattern = re.compile('^AutoML_[^_]*$')\n",
-    "for experiment in experiment_list:\n",
-    "    all_runs = list(experiment.get_runs())\n",
-    "    automl_runs = []\n",
-    "    for run in all_runs:\n",
-    "        if(pattern.match(run.id)):\n",
-    "            automl_runs.append(run)    \n",
-    "    summary_df[experiment.name] = [len(automl_runs)]\n",
-    "    \n",
-    "pd.set_option('display.max_colwidth', -1)\n",
-    "summary_df.T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnostics\n",
-    "\n",
-    "Opt-in diagnostics for better experience, quality, and security of future releases"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.telemetry import set_diagnostics_collection\n",
-    "set_diagnostics_collection(send_diagnostics=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# List AutoML runs for an Experiment\n",
-    "You can set <i>Experiment</i> name with any experiment name from the result of the Experiment.list cell to load the AutoML runs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "experiment_name = 'automl-local-classification' # Replace this with any project name from previous cell\n",
-    "\n",
-    "proj = ws.experiments()[experiment_name]\n",
-    "summary_df = pd.DataFrame(index = ['Type', 'Status', 'Primary Metric', 'Iterations', 'Compute', 'Name'])\n",
-    "pattern = re.compile('^AutoML_[^_]*$')\n",
-    "all_runs = list(proj.get_runs(properties={'azureml.runsource': 'automl'}))\n",
-    "for run in all_runs:\n",
-    "    if(pattern.match(run.id)):\n",
-    "        properties = run.get_properties()\n",
-    "        tags = run.get_tags()\n",
-    "        amlsettings = eval(properties['RawAMLSettingsString'])\n",
-    "        if 'iterations' in tags:\n",
-    "            iterations = tags['iterations']\n",
-    "        else:\n",
-    "            iterations = properties['num_iterations']\n",
-    "        summary_df[run.id] = [amlsettings['task_type'], run.get_details()['status'], properties['primary_metric'], iterations, properties['target'], amlsettings['name']]\n",
-    "    \n",
-    "from IPython.display import HTML\n",
-    "projname_html = HTML(\"<h3>{}</h3>\".format(proj.name))\n",
-    "\n",
-    "from IPython.display import display\n",
-    "display(projname_html)\n",
-    "display(summary_df.T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Get Details for a Auto ML Run\n",
-    "\n",
-    "Copy the project name and run id from the previous cell output to find more details on a particular run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run_id = '' # Filling your own run_id\n",
-    "\n",
-    "from azureml.train.widgets import RunDetails\n",
-    "\n",
-    "experiment = Experiment(ws, experiment_name)\n",
-    "ml_run = AutoMLRun(experiment=experiment, run_id=run_id)\n",
-    "\n",
-    "summary_df = pd.DataFrame(index = ['Type', 'Status', 'Primary Metric', 'Iterations', 'Compute', 'Name', 'Start Time', 'End Time'])\n",
-    "properties = ml_run.get_properties()\n",
-    "tags = ml_run.get_tags()\n",
-    "status = ml_run.get_details()\n",
-    "amlsettings = eval(properties['RawAMLSettingsString'])\n",
-    "if 'iterations' in tags:\n",
-    "    iterations = tags['iterations']\n",
-    "else:\n",
-    "    iterations = properties['num_iterations']\n",
-    "start_time = None\n",
-    "if 'startTimeUtc' in status:\n",
-    "    start_time = status['startTimeUtc']\n",
-    "end_time = None\n",
-    "if 'endTimeUtc' in status:\n",
-    "    end_time = status['endTimeUtc']\n",
-    "summary_df[ml_run.id] = [amlsettings['task_type'], status['status'], properties['primary_metric'], iterations, properties['target'], amlsettings['name'], start_time, end_time]\n",
-    "display(HTML('<h3>Runtime Details</h3>'))\n",
-    "display(summary_df)\n",
-    "\n",
-    "#settings_df = pd.DataFrame(data=amlsettings, index=[''])\n",
-    "display(HTML('<h3>AutoML Settings</h3>'))\n",
-    "display(amlsettings)\n",
-    "\n",
-    "display(HTML('<h3>Iterations</h3>'))\n",
-    "RunDetails(ml_run).show() \n",
-    "\n",
-    "children = list(ml_run.get_children())\n",
-    "metricslist = {}\n",
-    "for run in children:\n",
-    "    properties = run.get_properties()\n",
-    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
-    "    metricslist[int(properties['iteration'])] = metrics\n",
-    "\n",
-    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
-    "display(HTML('<h3>Metrics</h3>'))\n",
-    "display(rundata)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Download fitted models"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Download best model for any given metric"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metric = 'AUC_weighted' # Replace with a metric name\n",
-    "best_run, fitted_model = ml_run.get_output(metric=metric)\n",
-    "fitted_model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Download model for any given iteration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iteration = 4 # Replace with an interation number\n",
-    "best_run, fitted_model = ml_run.get_output(iteration=iteration)\n",
-    "fitted_model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Register fitted model for deployment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "description = 'AutoML Model'\n",
-    "tags = None\n",
-    "ml_run.register_model(description=description, tags=tags)\n",
-    "ml_run.model_id # Use this id to deploy the model as a web service in Azure"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Register best model for any given metric"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "metric = 'AUC_weighted' # Replace with a metric name\n",
-    "description = 'AutoML Model'\n",
-    "tags = None\n",
-    "ml_run.register_model(description=description, tags=tags, metric=metric)\n",
-    "ml_run.model_id # Use this id to deploy the model as a web service in Azure"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Register model for any given iteration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iteration = 4 # Replace with an interation number\n",
-    "description = 'AutoML Model'\n",
-    "tags = None\n",
-    "ml_run.register_model(description=description, tags=tags, iteration=iteration)\n",
-    "ml_run.model_id # Use this id to deploy the model as a web service in Azure"
-   ]
-  }
- ],
- "metadata": {
-  "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
-}

automl/08.auto-ml-remote-execution-with-text-file-on-DSVM.ipynb

@@ -1,480 +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 08: Remote Execution with Text file\n",
-    "\n",
-    "In this sample accesses a data file on a remote DSVM.  This is more efficient than reading the file from Blob storage in the get_data method.\n",
-    "\n",
-    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
-    "\n",
-    "In this notebook you would see\n",
-    "1. Configuring the DSVM to allow files to be access directly by the get_data method.\n",
-    "2. get_data returning data from a local file.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Experiment\n",
-    "\n",
-    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import logging\n",
-    "import os\n",
-    "import random\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from sklearn import datasets\n",
-    "\n",
-    "import azureml.core\n",
-    "from azureml.core.experiment import Experiment\n",
-    "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ws = Workspace.from_config()\n",
-    "\n",
-    "# choose a name for experiment\n",
-    "experiment_name = 'automl-remote-dsvm-file'\n",
-    "# project folder\n",
-    "project_folder = './sample_projects/automl-remote-dsvm-file'\n",
-    "\n",
-    "experiment=Experiment(ws, experiment_name)\n",
-    "\n",
-    "output = {}\n",
-    "output['SDK version'] = azureml.core.VERSION\n",
-    "output['Subscription ID'] = ws.subscription_id\n",
-    "output['Workspace'] = ws.name\n",
-    "output['Resource Group'] = ws.resource_group\n",
-    "output['Location'] = ws.location\n",
-    "output['Project Directory'] = project_folder\n",
-    "output['Experiment Name'] = experiment.name\n",
-    "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data=output, index=['']).T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnostics\n",
-    "\n",
-    "Opt-in diagnostics for better experience, quality, and security of future releases"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.telemetry import set_diagnostics_collection\n",
-    "set_diagnostics_collection(send_diagnostics=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create a Remote Linux DSVM\n",
-    "Note: If creation fails with a message about Marketplace purchase eligibilty, go to portal.azure.com, start creating DSVM there, and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled it, you can exit without actually creating VM.\n",
-    "\n",
-    "**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you can switch to a different port (such as 5022), you can append the port number to the address. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.compute import DsvmCompute\n",
-    "\n",
-    "dsvm_name = 'mydsvm'\n",
-    "try:\n",
-    "    dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
-    "    print('found existing dsvm.')\n",
-    "except:\n",
-    "    print('creating new dsvm.')\n",
-    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
-    "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
-    "    dsvm_compute.wait_for_completion(show_output = True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Copy data file to the DSVM\n",
-    "Download the data file.\n",
-    "Copy the data file to the DSVM under the folder: /tmp/data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
-    "                     delimiter=\"\\t\", quotechar='\"')\n",
-    "df.to_csv(\"data.tsv\", sep=\"\\t\", quotechar='\"', index=False)\n",
-    "\n",
-    "# Now copy the file data.tsv to the folder /tmp/data on the DSVM"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Get Data File\n",
-    "For remote executions you should author a get_data.py file containing a get_data() function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
-    "\n",
-    "The *get_data()* function returns a [dictionary](README.md#getdata)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if not os.path.exists(project_folder):\n",
-    "    os.makedirs(project_folder)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%writefile $project_folder/get_data.py\n",
-    "\n",
-    "import pandas as pd\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "import os\n",
-    "\n",
-    "def get_data():\n",
-    "    # Burning man 2016 data\n",
-    "    df = pd.read_csv('/tmp/data/data.tsv',\n",
-    "                     delimiter=\"\\t\", quotechar='\"')\n",
-    "    # get integer labels\n",
-    "    le = LabelEncoder()\n",
-    "    le.fit(df[\"Label\"].values)\n",
-    "    y = le.transform(df[\"Label\"].values)\n",
-    "    df = df.drop([\"Label\"], axis=1)\n",
-    "\n",
-    "    df_train, _, y_train, _ = train_test_split(df, y, test_size=0.1, random_state=42)\n",
-    "\n",
-    "    return { \"X\" : df.values, \"y\" : y }"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
-    "\n",
-    "You can specify automl_settings as **kwargs** as well. Also note that you can use the get_data() symantic for local excutions too. \n",
-    "\n",
-    "<i>Note: For Remote DSVM and Batch AI you cannot pass Numpy arrays directly to the fit method.</i>\n",
-    "\n",
-    "|Property|Description|\n",
-    "|-|-|\n",
-    "|**primary_metric**|This is the metric that you want to optimize.<br> 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",
-    "|**max_time_sec**|Time limit in seconds for each iteration|\n",
-    "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data|\n",
-    "|**n_cross_validations**|Number of cross validation splits|\n",
-    "|**concurrent_iterations**|Max number of iterations that would be executed in parallel.  This should be less than the number of cores on the DSVM\n",
-    "|**preprocess**| *True/False* <br>Setting this to *True* enables Auto ML to perform preprocessing <br>on the input to handle *missing data*, and perform some common *feature extraction*|\n",
-    "|**max_cores_per_iteration**| Indicates how many cores on the compute target would be used to train a single pipeline.<br> Default is *1*, you can set it to *-1* to use all cores|"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "automl_settings = {\n",
-    "    \"max_time_sec\": 3600,\n",
-    "    \"iterations\": 10,\n",
-    "    \"n_cross_validations\": 5,\n",
-    "    \"primary_metric\": 'AUC_weighted',\n",
-    "    \"preprocess\": True,\n",
-    "    \"max_cores_per_iteration\": 2,\n",
-    "    \"verbosity\": logging.INFO\n",
-    "}\n",
-    "automl_config = AutoMLConfig(task = 'classification',\n",
-    "                             debug_log = 'automl_errors.log',\n",
-    "                             path=project_folder,\n",
-    "                             compute_target = dsvm_compute,\n",
-    "                             data_script = project_folder + \"/get_data.py\",\n",
-    "                             **automl_settings\n",
-    "                            )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Training the Model <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
-    "\n",
-    "For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets/models even when the experiment is running to retreive the best model up to that point. Once you are satisfied with the model you can cancel a particular iteration or the whole run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "remote_run = experiment.submit(automl_config, show_output=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
-    "#### Widget for monitoring runs\n",
-    "\n",
-    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
-    "\n",
-    "You can click on a pipeline to see run properties and output logs. Logs are also available on the DSVM under /tmp/azureml_run/{iterationid}/azureml-logs\n",
-    "\n",
-    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.train.widgets import RunDetails\n",
-    "RunDetails(remote_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(remote_run.get_children())\n",
-    "metricslist = {}\n",
-    "for run in children:\n",
-    "    properties = run.get_properties()\n",
-    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
-    "    metricslist[int(properties['iteration'])] = metrics\n",
-    "\n",
-    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
-    "rundata"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Canceling runs\n",
-    "You can cancel ongoing remote runs using the *cancel()* and *cancel_iteration()* functions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Cancel the ongoing experiment and stop scheduling new iterations\n",
-    "# remote_run.cancel()\n",
-    "\n",
-    "# Cancel iteration 1 and move onto iteration 2\n",
-    "# remote_run.cancel_iteration(1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Retrieve the Best Model\n",
-    "\n",
-    "Below we select the best pipeline from our iterations. The *get_output* method on automl_classifier returns the best run and the fitted model for the last *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_run, fitted_model = remote_run.get_output()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Best Model based on any other metric"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# lookup_metric = \"accuracy\"\n",
-    "# best_run, fitted_model = remote_run.get_output(metric=lookup_metric)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Model from a specific iteration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# iteration = 1\n",
-    "# best_run, fitted_model = remote_run.get_output(iteration=iteration)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Register fitted model for deployment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "description = 'AutoML Model'\n",
-    "tags = None\n",
-    "remote_run.register_model(description=description, tags=tags)\n",
-    "remote_run.model_id # Use this id to deploy the model as a web service in Azure"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sklearn\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "from pandas_ml import ConfusionMatrix\n",
-    "\n",
-    "df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
-    "                     delimiter=\"\\t\", quotechar='\"')\n",
-    "\n",
-    "# get integer labels\n",
-    "le = LabelEncoder()\n",
-    "le.fit(df[\"Label\"].values)\n",
-    "y = le.transform(df[\"Label\"].values)\n",
-    "df = df.drop([\"Label\"], axis=1)\n",
-    "\n",
-    "_, df_test, _, y_test = train_test_split(df, y, test_size=0.1, random_state=42)\n",
-    "\n",
-    "ypred = fitted_model.predict(df_test.values)\n",
-    "\n",
-    "ypred_strings = le.inverse_transform(ypred)\n",
-    "ytest_strings = le.inverse_transform(y_test)\n",
-    "\n",
-    "cm = ConfusionMatrix(ytest_strings, ypred_strings)\n",
-    "\n",
-    "print(cm)\n",
-    "\n",
-    "cm.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "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
-}

automl/09.auto-ml-classification-with-deployment.ipynb

@@ -1,500 +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 09: Classification with deployment\n",
-    "\n",
-    "In this example we use the scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) to showcase how you can use AutoML for a simple classification problem.\n",
-    "\n",
-    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
-    "\n",
-    "In this notebook you would see\n",
-    "1. Creating an Experiment using an existing Workspace\n",
-    "2. Instantiating AutoMLConfig\n",
-    "3. Training the Model using local compute\n",
-    "4. Exploring the results\n",
-    "5. Registering the model\n",
-    "6. Creating Image and creating aci service\n",
-    "7. Testing the aci service\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Experiment\n",
-    "\n",
-    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "import logging\n",
-    "import os\n",
-    "import random\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from sklearn import datasets\n",
-    "\n",
-    "import azureml.core\n",
-    "from azureml.core.experiment import Experiment\n",
-    "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ws = Workspace.from_config()\n",
-    "\n",
-    "# choose a name for experiment\n",
-    "experiment_name =  'automl-local-classification'\n",
-    "# project folder\n",
-    "project_folder = './sample_projects/automl-local-classification'\n",
-    "\n",
-    "experiment=Experiment(ws, experiment_name)\n",
-    "\n",
-    "output = {}\n",
-    "output['SDK version'] = azureml.core.VERSION\n",
-    "output['Subscription ID'] = ws.subscription_id\n",
-    "output['Workspace'] = ws.name\n",
-    "output['Resource Group'] = ws.resource_group\n",
-    "output['Location'] = ws.location\n",
-    "output['Project Directory'] = project_folder\n",
-    "output['Experiment Name'] = experiment.name\n",
-    "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data=output, index=['']).T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnostics\n",
-    "\n",
-    "Opt-in diagnostics for better experience, quality, and security of future releases"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.telemetry import set_diagnostics_collection\n",
-    "set_diagnostics_collection(send_diagnostics=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instantiate Auto ML Config\n",
-    "\n",
-    "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
-    "\n",
-    "|Property|Description|\n",
-    "|-|-|\n",
-    "|**task**|classification or regression|\n",
-    "|**primary_metric**|This is the metric that you want to optimize.<br> 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",
-    "|**max_time_sec**|Time limit in seconds for each iteration|\n",
-    "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data|\n",
-    "|**n_cross_validations**|Number of cross validation splits|\n",
-    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
-    "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. |"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "digits = datasets.load_digits()\n",
-    "X_digits = digits.data[10:,:]\n",
-    "y_digits = digits.target[10:]\n",
-    "\n",
-    "automl_config = AutoMLConfig(task = 'classification',\n",
-    "                             name=experiment_name,\n",
-    "                             debug_log='automl_errors.log',\n",
-    "                             primary_metric='AUC_weighted',\n",
-    "                             max_time_sec=1200,\n",
-    "                             iterations=10,\n",
-    "                             n_cross_validations=2,\n",
-    "                             verbosity=logging.INFO,\n",
-    "                             X = X_digits, \n",
-    "                             y = y_digits,\n",
-    "                             path=project_folder)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Training the Model\n",
-    "\n",
-    "You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
-    "You will see the currently running iterations printing to the console."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "local_run = experiment.submit(automl_config, show_output=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Retrieve the Best Model\n",
-    "\n",
-    "Below we select the best pipeline from our iterations. The *get_output* method on automl_classifier returns the best run and the fitted model for the last *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_run, fitted_model = local_run.get_output()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Register fitted model for deployment"
-   ]
-  },
-  {
-   "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, iteration=8)\n",
-    "local_run.model_id # This will be written to the script file later in the notebook."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create Scoring script ###"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%writefile score.py\n",
-    "import pickle\n",
-    "import json\n",
-    "import numpy\n",
-    "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 yml file for env"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To ensure the consistence the fit results with the training results, the sdk dependence versions need to be the same as the environment that trains the model. Details about retrieving the versions can be found in notebook 12.auto-ml-retrieve-the-training-sdk-versions.ipynb."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "experiment_name = 'automl-local-classification'\n",
-    "\n",
-    "experiment = Experiment(ws, experiment_name)\n",
-    "ml_run = AutoMLRun(experiment=experiment, run_id=local_run.id)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dependencies = ml_run.get_run_sdk_dependencies(iteration=7)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
-    "    print('{}\\t{}'.format(p, dependencies[p]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%writefile myenv.yml\n",
-    "name: myenv\n",
-    "channels:\n",
-    "  - defaults\n",
-    "dependencies:\n",
-    "  - pip:\n",
-    "    - numpy==1.14.2\n",
-    "    - scikit-learn==0.19.2\n",
-    "    - azureml-sdk[notebooks,automl]==<<azureml-version>> "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Substitute the actual version number in the environment file.\n",
-    "\n",
-    "conda_env_file_name = 'myenv.yml'\n",
-    "\n",
-    "with open(conda_env_file_name, 'r') as cefr:\n",
-    "    content = cefr.read()\n",
-    "\n",
-    "with open(conda_env_file_name, 'w') as cefw:\n",
-    "    cefw.write(content.replace('<<azureml-version>>', dependencies['azureml-sdk']))\n",
-    "\n",
-    "# Substitute the actual model id in the script file.\n",
-    "\n",
-    "script_file_name = 'score.py'\n",
-    "\n",
-    "with open(script_file_name, 'r') as cefr:\n",
-    "    content = cefr.read()\n",
-    "\n",
-    "with open(script_file_name, 'w') as cefw:\n",
-    "    cefw.write(content.replace('<<modelid>>', local_run.model_id))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create Image ###"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.image import Image, ContainerImage\n",
-    "\n",
-    "image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
-    "                                 execution_script = script_file_name,\n",
-    "                                 conda_file = conda_env_file_name,\n",
-    "                                 tags = {'area': \"digits\", 'type': \"automl_classification\"},\n",
-    "                                 description = \"Image for automl classification sample\")\n",
-    "\n",
-    "image = Image.create(name = \"automlsampleimage\",\n",
-    "                     # this is the model object \n",
-    "                     models = [model],\n",
-    "                     image_config = image_config, \n",
-    "                     workspace = ws)\n",
-    "\n",
-    "image.wait_for_creation(show_output = True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Deploy Image as web service on Azure Container Instance ###"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.webservice import AciWebservice\n",
-    "\n",
-    "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
-    "                                               memory_gb = 1, \n",
-    "                                               tags = {'area': \"digits\", 'type': \"automl_classification\"}, \n",
-    "                                               description = 'sample service for Automl Classification')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.webservice import Webservice\n",
-    "\n",
-    "aci_service_name = 'automl-sample-01'\n",
-    "print(aci_service_name)\n",
-    "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
-    "                                           image = image,\n",
-    "                                           name = aci_service_name,\n",
-    "                                           workspace = ws)\n",
-    "aci_service.wait_for_deployment(True)\n",
-    "print(aci_service.state)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### To delete a service ##"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#aci_service.delete()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### To get logs from deployed service ###"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#aci_service.get_logs()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Test Web Service ###"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Randomly select digits and test\n",
-    "digits = datasets.load_digits()\n",
-    "X_digits = digits.data[:10, :]\n",
-    "y_digits = digits.target[:10]\n",
-    "images = digits.images[:10]\n",
-    "\n",
-    "for index in np.random.choice(len(y_digits), 3):\n",
-    "    print(index)\n",
-    "    test_sample = json.dumps({'data':X_digits[index:index + 1].tolist()})\n",
-    "    predicted = aci_service.run(input_data = test_sample)\n",
-    "    label = y_digits[index]\n",
-    "    predictedDict = json.loads(predicted)\n",
-    "    title = \"Label value = %d  Predicted value = %s \" % ( label,predictedDict['result'][0])\n",
-    "    fig = plt.figure(1, figsize=(3,3))\n",
-    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
-    "    ax1.set_title(title)\n",
-    "    plt.imshow(images[index], cmap=plt.cm.gray_r, interpolation='nearest')\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "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
-}

automl/10.auto-ml-multi-output-example.ipynb

@@ -1,292 +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 10: Multi output Example for AutoML"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook shows an example to use AutoML to train the multi output problems by leveraging the correlation between the outputs using indicator vectors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import logging\n",
-    "import os\n",
-    "import random\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from sklearn import datasets\n",
-    "\n",
-    "import azureml.core\n",
-    "from azureml.core.experiment import Experiment\n",
-    "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnostics\n",
-    "\n",
-    "Opt-in diagnostics for better experience, quality, and security of future releases"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.telemetry import set_diagnostics_collection\n",
-    "set_diagnostics_collection(send_diagnostics=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Transformer functions\n",
-    "The transformation of the input are happening for input X and Y as following, e.g. Y = {y_1, y_2}, then X becomes\n",
-    "    \n",
-    "X 1 0\n",
-    "     \n",
-    "X 0 1\n",
-    "\n",
-    "and Y becomes,\n",
-    "\n",
-    "y_1\n",
-    "\n",
-    "y_2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy import sparse\n",
-    "from scipy import linalg\n",
-    "\n",
-    "#Transformer functions\n",
-    "def multi_output_transform_x_y(X, Y):\n",
-    "    X_new = multi_output_transformer_x(X, Y.shape[1])\n",
-    "    y_new = multi_output_transform_y(Y)\n",
-    "    return X_new, y_new\n",
-    "\n",
-    "def multi_output_transformer_x(X, number_of_columns_Y):\n",
-    "    indicator_vecs = linalg.block_diag(*([np.ones((X.shape[0], 1))] * number_of_columns_Y))\n",
-    "    if sparse.issparse(X):\n",
-    "        X_new = sparse.vstack(np.tile(X, number_of_columns_Y))\n",
-    "        indicator_vecs = sparse.coo_matrix(indicator_vecs)\n",
-    "        X_new = sparse.hstack((X_new, indicator_vecs))\n",
-    "    else:\n",
-    "        X_new = np.tile(X, (number_of_columns_Y, 1))\n",
-    "        X_new = np.hstack((X_new, indicator_vecs))\n",
-    "    return X_new\n",
-    "\n",
-    "def multi_output_transform_y(Y):\n",
-    "    return Y.reshape(-1, order=\"F\")\n",
-    "    \n",
-    "def multi_output_inverse_transform_y(y, number_of_columns_y):\n",
-    "    return y.reshape((-1, number_of_columns_y), order=\"F\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## AutoML experiment set up"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ws = Workspace.from_config()\n",
-    "\n",
-    "# choose a name for experiment\n",
-    "experiment_name = 'automl-local-multi-output'\n",
-    "# project folder\n",
-    "project_folder = './sample_projects/automl-local-multi-output'\n",
-    "\n",
-    "experiment=Experiment(ws, experiment_name)\n",
-    "\n",
-    "output = {}\n",
-    "output['SDK version'] = azureml.core.VERSION\n",
-    "output['Subscription ID'] = ws.subscription_id\n",
-    "output['Workspace'] = ws.name\n",
-    "output['Resource Group'] = ws.resource_group\n",
-    "output['Location'] = ws.location\n",
-    "output['Project Directory'] = project_folder\n",
-    "output['Experiment Name'] = experiment.name\n",
-    "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data=output, index=['']).T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create a random dataset for the test purpose "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rng = np.random.RandomState(1)\n",
-    "X_train = np.sort(200 * rng.rand(600, 1) - 100, axis=0)\n",
-    "Y_train = np.array([np.pi * np.sin(X_train).ravel(), np.pi * np.cos(X_train).ravel()]).T\n",
-    "Y_train += (0.5 - rng.rand(*Y_train.shape))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Perform X and Y transformation using transformer function"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train_transformed, y_train_transformed = multi_output_transform_x_y(X_train, Y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "automl_config = AutoMLConfig(task = 'regression',\n",
-    "                             debug_log='automl_errors_multi.log',\n",
-    "                             primary_metric='r2_score',\n",
-    "                             iterations=10,\n",
-    "                             n_cross_validations=2,\n",
-    "                             verbosity=logging.INFO,\n",
-    "                             X=X_train_transformed,\n",
-    "                             y=y_train_transformed,\n",
-    "                             path=project_folder)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Fit the transformed data "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "local_run = experiment.submit(automl_config, show_output=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get the best fit model\n",
-    "best_run, fitted_model = local_run.get_output()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Generate random data set for predicting\n",
-    "X_predict = np.sort(200 * rng.rand(200, 1) - 100, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Transform predict data\n",
-    "X_predict_transformed = multi_output_transformer_x(X_predict, Y_train.shape[1])\n",
-    "# Predict and inverse transform the prediction\n",
-    "y_predict = fitted_model.predict(X_predict_transformed)\n",
-    "Y_predict = multi_output_inverse_transform_y(y_predict, Y_train.shape[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(Y_predict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "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
-}

automl/11.auto-ml-sample-weight.ipynb

@@ -1,251 +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 11: Sample weight\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 sample weight with the AutoML Classifier.\n",
-    "Sample weight is used where some sample values are more important than others.\n",
-    "\n",
-    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
-    "\n",
-    "In this notebook you would see\n",
-    "1. How to specifying sample_weight\n",
-    "2. The difference that it makes to test results\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Experiment\n",
-    "\n",
-    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import logging\n",
-    "import os\n",
-    "import random\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from sklearn import datasets\n",
-    "\n",
-    "import azureml.core\n",
-    "from azureml.core.experiment import Experiment\n",
-    "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ws = Workspace.from_config()\n",
-    "\n",
-    "# choose a name for experiment\n",
-    "experiment_name = 'non_sample_weight_experiment'\n",
-    "sample_weight_experiment_name = 'sample_weight_experiment'\n",
-    "\n",
-    "# project folder\n",
-    "project_folder = './sample_projects/automl-local-classification'\n",
-    "\n",
-    "experiment=Experiment(ws, experiment_name)\n",
-    "sample_weight_experiment=Experiment(ws, sample_weight_experiment_name)\n",
-    "\n",
-    "output = {}\n",
-    "output['SDK version'] = azureml.core.VERSION\n",
-    "output['Subscription ID'] = ws.subscription_id\n",
-    "output['Workspace Name'] = ws.name\n",
-    "output['Resource Group'] = ws.resource_group\n",
-    "output['Location'] = ws.location\n",
-    "output['Project Directory'] = project_folder\n",
-    "output['Experiment Name'] = experiment.name\n",
-    "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data = output, index = ['']).T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnostics\n",
-    "\n",
-    "Opt-in diagnostics for better experience, quality, and security of future releases"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.telemetry import set_diagnostics_collection\n",
-    "set_diagnostics_collection(send_diagnostics=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instantiate Auto ML Config\n",
-    "\n",
-    "Instantiate two AutoMLConfig Objects. One will be used with sample_weight and one without."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "digits = datasets.load_digits()\n",
-    "X_digits = digits.data[100:,:]\n",
-    "y_digits = digits.target[100:]\n",
-    "\n",
-    "# The example makes the sample weight 0.9 for the digit 4 and 0.1 for all other digits.\n",
-    "# This makes the model more likely to classify as 4 if the image it not clear.\n",
-    "sample_weight = np.array([(0.9 if x == 4 else 0.01) for x in y_digits])\n",
-    "\n",
-    "automl_classifier = AutoMLConfig(task = 'classification',\n",
-    "                                 debug_log = 'automl_errors.log',\n",
-    "                                 primary_metric = 'AUC_weighted',\n",
-    "                                 max_time_sec = 3600,\n",
-    "                                 iterations = 10,\n",
-    "                                 n_cross_validations = 2,\n",
-    "                                 verbosity = logging.INFO,\n",
-    "                                 X = X_digits, \n",
-    "                                 y = y_digits,\n",
-    "                                 path=project_folder)\n",
-    "\n",
-    "automl_sample_weight = AutoMLConfig(task = 'classification',\n",
-    "                                    debug_log = 'automl_errors.log',\n",
-    "                                    primary_metric = 'AUC_weighted',\n",
-    "                                    max_time_sec = 3600,\n",
-    "                                    iterations = 10,\n",
-    "                                    n_cross_validations = 2,\n",
-    "                                    verbosity = logging.INFO,\n",
-    "                                    X = X_digits, \n",
-    "                                    y = y_digits,\n",
-    "                                    sample_weight = sample_weight,\n",
-    "                                    path=project_folder)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Training the Models\n",
-    "\n",
-    "Call the submit method on the experiment and pass the 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_classifier, show_output=True)\n",
-    "sample_weight_run = sample_weight_experiment.submit(automl_sample_weight, show_output=True)\n",
-    "\n",
-    "best_run, fitted_model = local_run.get_output()\n",
-    "best_run_sample_weight, fitted_model_sample_weight = sample_weight_run.get_output()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Testing the Fitted Models\n",
-    "\n",
-    "#### Load Test Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "digits = datasets.load_digits()\n",
-    "X_digits = digits.data[:100, :]\n",
-    "y_digits = digits.target[:100]\n",
-    "images = digits.images[:100]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Compare the pipelines\n",
-    "The prediction from the sample weight model is more likely to correctly predict 4's.  However, it is also more likely to predict 4 for some images that are not labelled as 4."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Randomly select digits and test\n",
-    "for index in range(0,len(y_digits)):\n",
-    "    predicted = fitted_model.predict(X_digits[index:index + 1])[0]\n",
-    "    predicted_sample_weight = fitted_model_sample_weight.predict(X_digits[index:index + 1])[0]\n",
-    "    label = y_digits[index]\n",
-    "    if predicted == 4 or predicted_sample_weight == 4 or label == 4:\n",
-    "        title = \"Label value = %d  Predicted value = %d Prediced with sample weight = %d\" % ( label,predicted,predicted_sample_weight)\n",
-    "        fig = plt.figure(1, figsize=(3,3))\n",
-    "        ax1 = fig.add_axes((0,0,.8,.8))\n",
-    "        ax1.set_title(title)\n",
-    "        plt.imshow(images[index], cmap=plt.cm.gray_r, interpolation='nearest')\n",
-    "        plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.6",
-   "language": "python",
-   "name": "python36"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

automl/12.auto-ml-retrieve-the-training-sdk-versions.ipynb

@@ -1,240 +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 12: Retrieving Training SDK Versions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import logging\n",
-    "import os\n",
-    "import random\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from sklearn import datasets\n",
-    "\n",
-    "import azureml.core\n",
-    "from azureml.core.experiment import Experiment\n",
-    "from azureml.core.workspace import Workspace\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun\n",
-    "from azureml.train.automl.utilities import get_sdk_dependencies"
-   ]
-  },
-  {
-   "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": [
-    "# 1. Retrieve the SDK versions in the current env"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To retrieve the SDK versions in the current env, simple running get_sdk_dependencies()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "get_sdk_dependencies()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 2. Training Model Using AutoML"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ws = Workspace.from_config()\n",
-    "\n",
-    "# choose a name for experiment\n",
-    "experiment_name = 'automl-local-classification'\n",
-    "# project folder\n",
-    "project_folder = './sample_projects/automl-local-classification'\n",
-    "\n",
-    "experiment=Experiment(ws, experiment_name)\n",
-    "\n",
-    "output = {}\n",
-    "output['SDK version'] = azureml.core.VERSION\n",
-    "output['Subscription ID'] = ws.subscription_id\n",
-    "output['Workspace'] = ws.name\n",
-    "output['Resource Group'] = ws.resource_group\n",
-    "output['Location'] = ws.location\n",
-    "output['Project Directory'] = project_folder\n",
-    "output['Experiment Name'] = experiment.name\n",
-    "pd.set_option('display.max_colwidth', -1)\n",
-    "pd.DataFrame(data=output, index=['']).T"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "digits = datasets.load_digits()\n",
-    "X_digits = digits.data[10:,:]\n",
-    "y_digits = digits.target[10:]\n",
-    "\n",
-    "automl_config = AutoMLConfig(task = 'classification',\n",
-    "                             debug_log='automl_errors.log',\n",
-    "                             primary_metric='AUC_weighted',\n",
-    "                             iterations=3,\n",
-    "                             n_cross_validations=2,\n",
-    "                             verbosity=logging.INFO,\n",
-    "                             X = X_digits, \n",
-    "                             y = y_digits,\n",
-    "                             path=project_folder)\n",
-    "\n",
-    "local_run = experiment.submit(automl_config, show_output=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 3. Retrieve the SDK versions from RunHistory"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To get the SDK versions from RunHistory, first the RunId need to be recorded. This can either be done by copy it from the output message or retieve if after each run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run_id = local_run.id\n",
-    "print(run_id)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Initialize a new AutoMLRunClass."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "experiment_name = 'automl-local-classification'\n",
-    "#run_id = 'AutoML_c0585b1f-a0e6-490b-84c7-3a099468b28e'\n",
-    "\n",
-    "experiment = Experiment(ws, experiment_name)\n",
-    "ml_run = AutoMLRun(experiment=experiment, run_id=run_id)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get parent training SDK versions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ml_run.get_run_sdk_dependencies()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get the traning SDK versions of a specific run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ml_run.get_run_sdk_dependencies(iteration=2)"
-   ]
-  }
- ],
- "metadata": {
-  "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
-}

automl/13.auto-ml-dataprep.ipynb

@@ -1,567 +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 13: Prepare Data using `azureml.dataprep`\n",
-    "In this example we showcase how you can use `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone - full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
-    "\n",
-    "Make sure you have executed the [setup](00.configuration.ipynb) before running this notebook.\n",
-    "\n",
-    "In this notebook you would see\n",
-    "1. Defining data loading and preparation steps in a `Dataflow` using `azureml.dataprep`\n",
-    "2. Passing the `Dataflow` to AutoML for local run\n",
-    "3. Passing the `Dataflow` to AutoML for remote run"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Install `azureml.dataprep` SDK"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Please restart your kernel after the below installs.\n",
-    "\n",
-    "Tornado must be downgraded to a pre-5 version due to a known Tornado x Jupyter event loop bug."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install azureml-dataprep\n",
-    "!pip install tornado==4.5.1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnostics\n",
-    "\n",
-    "Opt-in diagnostics for better experience, quality, and security of future releases."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.telemetry import set_diagnostics_collection\n",
-    "set_diagnostics_collection(send_diagnostics = True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Experiment\n",
-    "\n",
-    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import logging\n",
-    "import os\n",
-    "\n",
-    "import pandas as pd\n",
-    "\n",
-    "import azureml.core\n",
-    "from azureml.core.compute import DsvmCompute\n",
-    "from azureml.core.experiment import Experiment\n",
-    "from azureml.core.runconfig import CondaDependencies\n",
-    "from azureml.core.runconfig import RunConfiguration\n",
-    "from azureml.core.workspace import Workspace\n",
-    "import azureml.dataprep as dprep\n",
-    "from azureml.train.automl import AutoMLConfig"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ws = Workspace.from_config()\n",
-    " \n",
-    "# choose a name for experiment\n",
-    "experiment_name = 'automl-dataprep-classification'\n",
-    "# project folder\n",
-    "project_folder = './sample_projects/automl-dataprep-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": [
-    "## Loading Data using DataPrep"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# You can use `smart_read_file` which intelligently figures out delimiters and datatypes of a file\n",
-    "# data pulled from sklearn.datasets.load_digits()\n",
-    "simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
-    "X = dprep.smart_read_file(simple_example_data_root + 'X.csv').skip(1)  # remove header\n",
-    "\n",
-    "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter).\n",
-    "# and convert column types manually.\n",
-    "# Here we read a comma delimited file and convert all columns to integers.\n",
-    "y = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Review the Data Preparation Result\n",
-    "\n",
-    "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X.skip(1).head(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instantiate AutoML Settings\n",
-    "\n",
-    "This creates a general Auto ML Settings applicable for both Local and Remote runs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "automl_settings = {\n",
-    "    \"max_time_sec\": 600,\n",
-    "    \"iterations\": 2,\n",
-    "    \"primary_metric\": 'AUC_weighted',\n",
-    "    \"preprocess\": False,\n",
-    "    \"verbosity\": logging.INFO,\n",
-    "    \"n_cross_validations\" : 3\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Local Run"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Pass data with Dataflows\n",
-    "\n",
-    "The `Dataflow` objects captured above can be passed to `submit` method for local run. AutoML will retrieve the results from the `Dataflow` for model training."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "automl_config = AutoMLConfig(task = 'classification',\n",
-    "                             debug_log = 'automl_errors.log',\n",
-    "                             X = X,\n",
-    "                             y = y,\n",
-    "                             **automl_settings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "local_run = experiment.submit(automl_config, show_output=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Remote Run\n",
-    "*Note: This feature might not work properly in your workspace region before the October update. You may jump to the \"Exploring the results\" section below to explore other features AutoML and DataPrep has to offer.*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create or Attach a Remote Linux DSVM"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dsvm_name = 'mydsvm'\n",
-    "try:\n",
-    "    dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
-    "    print('found existing dsvm.')\n",
-    "except:\n",
-    "    print('creating new dsvm.')\n",
-    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
-    "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
-    "    dsvm_compute.wait_for_completion(show_output = True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Update Conda Dependency file to have AutoML and DataPrep SDK\n",
-    "\n",
-    "Currently AutoML and DataPrep SDK is not installed with Azure ML SDK by default. Due to this we update the conda dependency file to add such dependencies."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cd = CondaDependencies()\n",
-    "cd.add_pip_package(pip_package='azureml-dataprep')\n",
-    "cd.add_pip_package(pip_package='tornado==4.5.1')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create a RunConfiguration with DSVM name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "run_config = RunConfiguration(conda_dependencies=cd)\n",
-    "run_config.target = dsvm_compute\n",
-    "run_config.auto_prepare_environment = True"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Pass data with Dataflows\n",
-    "\n",
-    "The `Dataflow` objects captured above can also be passed to `submit` method for remote run. AutoML will serialize the `Dataflow` and send to remote compute target. The `Dataflow` will not be evaluated locally."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "automl_config = AutoMLConfig(task = 'classification',\n",
-    "                             debug_log = 'automl_errors.log',\n",
-    "                             path = project_folder,\n",
-    "                             run_configuration = run_config,\n",
-    "                             X = X,\n",
-    "                             y = y,\n",
-    "                             **automl_settings)\n",
-    "# Please uncomment the line below to try out remote run with dataprep. \n",
-    "# This feature might not work properly in your workspace region before the October update.\n",
-    "# remote_run = experiment.submit(automl_config, show_output = True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exploring the results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Widget for monitoring runs\n",
-    "\n",
-    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
-    "\n",
-    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.train.widgets import RunDetails\n",
-    "RunDetails(local_run).show() "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Retrieve all child runs\n",
-    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "children = list(local_run.get_children())\n",
-    "metricslist = {}\n",
-    "for run in children:\n",
-    "    properties = run.get_properties()\n",
-    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
-    "    metricslist[int(properties['iteration'])] = metrics\n",
-    "    \n",
-    "import pandas as pd\n",
-    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
-    "rundata"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Retrieve the Best Model\n",
-    "\n",
-    "Below we select the best pipeline from our iterations. The *get_output* method on automl_classifier returns the best run and the fitted model for the last *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_run, fitted_model = local_run.get_output()\n",
-    "print(best_run)\n",
-    "print(fitted_model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Best Model based on any other metric\n",
-    "Give me the run and the model that has the smallest `log_loss`:"
-   ]
-  },
-  {
-   "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": [
-    "#### Best Model based on any iteration\n",
-    "Give me the run and the model from the 1st iteration:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iteration = 0\n",
-    "best_run, fitted_model = local_run.get_output(iteration = iteration)\n",
-    "print(best_run)\n",
-    "print(fitted_model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Testing the Fitted Model \n",
-    "\n",
-    "#### Load Test Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn import datasets\n",
-    "\n",
-    "digits = datasets.load_digits()\n",
-    "X_digits = digits.data[:10, :]\n",
-    "y_digits = digits.target[:10]\n",
-    "images = digits.images[:10]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Testing our best pipeline\n",
-    "We will try to predict 2 digits and see how our model works."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Randomly select digits and test\n",
-    "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\n",
-    "import random\n",
-    "import numpy as np\n",
-    "\n",
-    "for index in np.random.choice(len(y_digits), 2):\n",
-    "    print(index)\n",
-    "    predicted = fitted_model.predict(X_digits[index:index + 1])[0]\n",
-    "    label = y_digits[index]\n",
-    "    title = \"Label value = %d  Predicted value = %d \" % ( label,predicted)\n",
-    "    fig = plt.figure(1, figsize=(3,3))\n",
-    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
-    "    ax1.set_title(title)\n",
-    "    plt.imshow(images[index], cmap=plt.cm.gray_r, interpolation='nearest')\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Appendix"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Capture the Dataflows to use for AutoML later\n",
-    "\n",
-    "`Dataflow` objects are immutable. Each of them is composed of a list of data preparation steps. A `Dataflow` can be branched at any point for further usage."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# sklearn.digits.data + target\n",
-    "digits_complete = dprep.smart_read_file('https://dprepdata.blob.core.windows.net/automl-notebook-data/digits-complete.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`digits_complete` (sourced from `sklearn.datasets.load_digits()`)is forked into `dflow_X` to capture all the feature columns and `dflow_y` to capture the label column."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "digits_complete.to_pandas_dataframe().shape\n",
-    "labels_column = 'Column64'\n",
-    "dflow_X = digits_complete.drop_columns(columns = [labels_column])\n",
-    "dflow_y = digits_complete.keep_columns(columns = [labels_column])"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.6",
-   "language": "python",
-   "name": "python36"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

automl/README.md

@@ -1,264 +0,0 @@
-# Table of Contents
-1. [Automated ML Introduction](#introduction)
-1. [Running samples in Azure Notebooks](#jupyter)
-1. [Running samples in a Local Conda environment](#localconda)
-1. [Automated ML SDK Sample Notebooks](#samples)
-1. [Documentation](#documentation)
-1. [Running using python command](#pythoncommand)
-1. [Troubleshooting](#troubleshooting)
-
-<a name="introduction"></a>
-# Automated ML introduction 
-Automated machine learning (automated ML) builds high quality machine learning models for you by automating model and hyperparameter selection. Bring a labelled dataset that you want to build a model for, automated ML will give you a high quality machine learning model that you can use for predictions.
-
-
-If you are new to Data Science, AutoML will help you get jumpstarted by simplifying machine learning model building. It abstracts you from needing to perform model selection, hyperparameter selection and in one step creates a high quality trained model for you to use.
-
-If you are an experienced data scientist, AutoML will help increase your productivity by intelligently performing the model and hyperparameter selection for your training and generates high quality models much quicker than manually specifying several combinations of the parameters and running training jobs. AutoML provides visibility and access to all the training jobs and the performance characteristics of the models to help you further tune the pipeline if you desire.
-
- <a name="jupyter"></a>
-## Running samples in Azure Notebooks - Jupyter based notebooks in the Azure cloud
-
-1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
-[Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks if they are not already there.
-1.  Create a workspace and its configuration file (**config.json**) using [these instructions](https://aka.ms/aml-how-to-configure-environment).
-1. Select `+New` in the Azure Notebook toolbar to add your **config.json** file to the imported folder.
-    ![upload config file to notebook folder](../images/additems.png)
-1. Open the notebook.
-    
-    **Make sure the Azure Notebook kernal is set to `Python 3.6`** when you open a notebook. 
-    
-    ![set kernal to Python 3.6](../images/python36.png)
-
-<a name="localconda"></a>
-## Running samples in a Local Conda environment 
-
-To run these notebook on your own notebook server, use these installation instructions.
-
-The instructions below will install everything you need and then start a Jupyter notebook.  To start your Jupyter notebook manually, use:
-
-```
-conda activate azure_automl
-jupyter notebook
-```
-
-or on Mac:
-
-```
-source activate azure_automl
-jupyter notebook
-```
-
-
-### 1. Install mini-conda from [here](https://conda.io/miniconda.html), choose Python 3.7 or higher. 
-- **Note**: if you already have conda installed, you can keep using it but it should be version 4.4.10 or later (as shown by: conda -V).  If you have a previous version installed, you can update it using the command: conda update conda.
-There's no need to install mini-conda specifically.
-
-### 2. Downloading the sample notebooks
-- Download the sample notebooks from [GitHub](https://github.com/Azure/MachineLearningNotebooks) as zip and extract the contents to a local directory.  The AutoML sample notebooks are in the "automl" folder.
-
-### 3. Setup a new conda environment
-The **automl/automl_setup** script creates a new conda environment, installs the necessary packages, configures the widget and starts a jupyter notebook.
-It takes the conda environment name as an optional parameter.  The default conda environment name is azure_automl.  The exact command depends on the operating system.  It can take about 30 minutes to execute.
-## Windows
-Start a conda command windows, cd to the **automl** folder where the sample notebooks were extracted and then run: 
-```
-automl_setup
-```
-## Mac
-Install "Command line developer tools" if it is not already installed (you can use the command: `xcode-select --install`).
-
-Start a Terminal windows, cd to the **automl** folder where the sample notebooks were extracted and then run: 
-
-```
-bash automl_setup_mac.sh
-```
-
-## Linux
-cd to the **automl** folder where the sample notebooks were extracted and then run: 
-
-```
-bash automl_setup_linux.sh
-```
-
-### 4. Running configuration.ipynb
-- Before running any samples you next need to run the configuration notebook. Click on 00.configuration.ipynb notebook
-- Execute the cells in the notebook to Register Machine Learning Services Resource Provider and create a workspace. (*instructions in notebook*)
-
-### 5. Running Samples
-- Please make sure you use the Python [conda env:azure_automl] kernel when trying the sample Notebooks.
-- Follow the instructions in the individual notebooks to explore various features in AutoML
-
-<a name="samples"></a>
-# Automated ML SDK Sample Notebooks 
-- [00.configuration.ipynb](00.configuration.ipynb)
-    - Register Machine Learning Services Resource Provider
-    - Create new Azure ML Workspace
-    - Save Workspace configuration file
-
-- [01.auto-ml-classification.ipynb](01.auto-ml-classification.ipynb)
-    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
-    - Simple example of using Auto ML for classification 
-    - Uses local compute for training
-
-- [02.auto-ml-regression.ipynb](02.auto-ml-regression.ipynb)
-    - Dataset: scikit learn's [diabetes dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html)
-    - Simple example of using Auto ML for regression 
-    - Uses local compute for training
-
-- [03.auto-ml-remote-execution.ipynb](03.auto-ml-remote-execution.ipynb)
-    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
-    - Example of using Auto ML for classification using a remote linux DSVM for training
-    - Parallel execution of iterations
-    - Async tracking of progress
-    - Cancelling individual iterations or entire run
-    - Retrieving models for any iteration or logged metric
-    - Specify automl settings as kwargs
-
-- [03b.auto-ml-remote-batchai.ipynb](03b.auto-ml-remote-batchai.ipynb)
-    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
-    - Example of using automated ML for classification using a remote Batch AI compute for training
-    - Parallel execution of iterations
-    - Async tracking of progress
-    - Cancelling individual iterations or entire run
-    - Retrieving models for any iteration or logged metric
-    - Specify automl settings as kwargs
-
-- [04.auto-ml-remote-execution-text-data-blob-store.ipynb](04.auto-ml-remote-execution-text-data-blob-store.ipynb)
-    - Dataset: [Burning Man 2016 dataset](https://innovate.burningman.org/datasets-page/)
-    - handling text data with preprocess flag
-    - Reading data from a blob store for remote executions
-    - using pandas dataframes for reading data    
-
-- [05.auto-ml-missing-data-blacklist-early-termination.ipynb](05.auto-ml-missing-data-blacklist-early-termination.ipynb)
-    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
-    - Blacklist certain pipelines
-    - Specify a target metrics to indicate stopping criteria
-    - Handling Missing Data in the input
-
-- [06.auto-ml-sparse-data-custom-cv-split.ipynb](06.auto-ml-sparse-data-custom-cv-split.ipynb)
-    - Dataset: Scikit learn's [20newsgroup](http://scikit-learn.org/stable/datasets/twenty_newsgroups.html) 
-    - Handle sparse datasets
-    - Specify custom train and validation set
-
-- [07.auto-ml-exploring-previous-runs.ipynb](07.auto-ml-exploring-previous-runs)
-    - List all projects for the workspace
-    - List all AutoML Runs for a given project
-    - Get details for a AutoML Run. (Automl settings, run widget & all metrics)
-    - Downlaod fitted pipeline for any iteration
-
-- [08.auto-ml-remote-execution-with-text-file-on-DSVM](08.auto-ml-remote-execution-with-text-file-on-DSVM.ipynb)
-    - Dataset: scikit learn's [digit dataset](https://innovate.burningman.org/datasets-page/)
-    - Download the data and store it in the DSVM to improve performance.
-
-- [09.auto-ml-classification-with-deployment.ipynb](09.auto-ml-classification-with-deployment.ipynb)
-    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
-    - Simple example of using Auto ML for classification 
-    - Registering the model
-    - Creating Image and creating aci service
-    - Testing the aci service
-
-- [10.auto-ml-multi-output-example.ipynb](10.auto-ml-multi-output-example.ipynb)
-    - Dataset: scikit learn's random example using multi-output pipeline(http://scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py)
-    - Simple example of using Auto ML for multi output regression
-    - Handle both the dense and sparse metrix
-
-- [11.auto-ml-sample-weight.ipynb](11.auto-ml-sample-weight.ipynb)
-    - How to specifying sample_weight
-    - The difference that it makes to test results
-
-- [12.auto-ml-retrieve-the-training-sdk-versions.ipynb](12.auto-ml-retrieve-the-training-sdk-versions.ipynb)
-    - How to get current and training env SDK versions
-
-- [13.auto-ml-dataprep.ipynb](13.auto-ml-dataprep.ipynb)
-    - Using DataPrep for reading data
-
-<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>balanced_accuracy</i><br><i>average_precision_score_weighted</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
-|**max_time_sec**|Time limit in seconds 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|
-|**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|
-|**exit_score**|*double* value indicating the target for *primary_metric*. <br> Once the target is surpassed the run terminates|None|
-|**blacklist_algos**|*Array* of *strings* indicating pipelines to ignore for Auto ML.<br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGDClassifierWrapper</i><br><i>NBWrapper</i><br><i>BernoulliNB</i><br><i>SVCWrapper</i><br><i>LinearSVMWrapper</i><br><i>KNeighborsClassifier</i><br><i>DecisionTreeClassifier</i><br><i>RandomForestClassifier</i><br><i>ExtraTreesClassifier</i><br><i>gradient boosting</i><br><i>LightGBMClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoostingRegressor</i><br><i>DecisionTreeRegressor</i><br><i>KNeighborsRegressor</i><br><i>LassoLars</i><br><i>SGDRegressor</i><br><i>RandomForestRegressor</i><br><i>ExtraTreesRegressor</i>|None|
-
- <a name="cvsplits"></a>
-## Cross validation split options
-### K-Folds Cross Validation
-Use *n_cross_validations* setting to specify the number of cross validations. The training data set will be randomly split into *n_cross_validations* folds of equal size. During each cross validation round, one of the folds will be used for validation of the model trained on the remaining folds. This process repeats for *n_cross_validations* rounds until each fold is used once as validation set. Finally, the average scores accross all *n_cross_validations* rounds will be reported, and the corresponding model will be retrained on the whole training data set.
-
-### Monte Carlo Cross Validation (a.k.a. Repeated Random Sub-Sampling)
-Use *validation_size* to specify the percentage of the training data set that should be used for validation, and use *n_cross_validations* to specify the number of cross validations. During each cross validation round, a subset of size *validation_size* will be randomly selected for validation of the model trained on the remaining data. Finally, the average scores accross all *n_cross_validations* rounds will be reported, and the corresponding model will be retrained on the whole training data set.
-
-### Custom train and validation set
-You can specify seperate train and validation set either through the get_data() or directly to the fit method.
-
-<a name="getdata"></a>
-## get_data() syntax 
-The *get_data()* function can be used to return a dictionary with these values:
-
-|Key|Type|Dependency|Mutually Exclusive with|Description|
-|:-|:-|:-|:-|:-|
-|X|Pandas Dataframe or Numpy Array|y|data_train, label, columns|All features to train with|
-|y|Pandas Dataframe or Numpy Array|X|label|Label data to train with.  For classification, this should be an array of integers. |
-|X_valid|Pandas Dataframe or Numpy Array|X, y, y_valid|data_train, label|*Optional* All features to validate with.  If this is not specified, X is split between train and validate|
-|y_valid|Pandas Dataframe or Numpy Array|X, y, X_valid|data_train, label|*Optional* The label data to validate with.  If this is not specified, y is split between train and validate|
-|sample_weight|Pandas Dataframe or Numpy Array|y|data_train, label, columns|*Optional* A weight value for each label. Higher values indicate that the sample is more important.|
-|sample_weight_valid|Pandas Dataframe or Numpy Array|y_valid|data_train, label, columns|*Optional* A weight value for each validation label. Higher values indicate that the sample is more important.  If this is not specified, sample_weight is split between train and validate|
-|data_train|Pandas Dataframe|label|X, y, X_valid, y_valid|All data (features+label) to train with|
-|label|string|data_train|X, y, X_valid, y_valid|Which column in data_train represents the label|
-|columns|Array of strings|data_train||*Optional* Whitelist of columns to use for features|
-|cv_splits_indices|Array of integers|data_train||*Optional* List of indexes to split the data for cross validation|
-
-<a name="preprocessing"></a>
-## Data pre-processing and featurization 
-If you use `preprocess=True`, the following data preprocessing steps are performed automatically for you:
-
-1. Dropping high cardinality or no variance features
-    - Features with no useful information are dropped from training and validation sets. These include features with all values missing, same value across all rows or with extremely high cardinality (e.g., hashes, IDs or GUIDs).
-2. Missing value imputation
-    - For numerical features, missing values are imputed with average of values in the column.
-    - For categorical features, missing values are imputed with most frequent value.
-3. Generating additional features
-    - For DateTime features: Year, Month, Day, Day of week, Day of year, Quarter, Week of the year, Hour, Minute, Second.
-    - For Text features: Term frequency based on bi-grams and tri-grams, Count vectorizer.
-4. Transformations and encodings
-    - Numeric features with very few unique values are transformed into categorical features.
-
-<a name="pythoncommand"></a>
-# Running using python command 
-Jupyter notebook provides a File / Download as / Python (.py) option for saving the notebook as a Python file.
-You can then run this file using the python command.
-However, on Windows the file needs to be modified before it can be run.
-The following condition must be added to the main code in the file:
-
-    if __name__ == "__main__":
-
-The main code of the file must be indented so that it is under this condition.
-
-<a name="troubleshooting"></a>
-# Troubleshooting 
-## Iterations fail and the log contains "MemoryError"
-This can be caused by insufficient memory on the DSVM.  AutoML loads all training data into memory.  So, the available memory should be more than the training data size.
-If you are using a remote DSVM, memory is needed for each concurrent iteration.  The concurrent_iterations setting specifies the maximum concurrent iterations.  For example, if the training data size is 8Gb and concurrent_iterations is set to 10, the minimum memory required is at least 80Gb.
-To resolve this issue, allocate a DSVM with more memory or reduce the value specified for concurrent_iterations.
-
-## Iterations show as "Not Responding" in the RunDetails widget.
-This can be caused by too many concurrent iterations for a remote DSVM.  Each concurrent iteration usually takes 100% of a core when it is running.  Some iterations can use multiple cores.  So, the concurrent_iterations setting should always be less than the number of cores of the DSVM.
-To resolve this issue, try reducing the value specified for the concurrent_iterations setting.
-

automl/automl_env.yml

@@ -1,20 +0,0 @@
-name: azure_automl
-dependencies:
-  # The python interpreter version.
-  # Currently Azure ML only supports 3.5.2 and later.
-- python=3.6
-- nb_conda
-- matplotlib
-- numpy>=1.11.0,<1.16.0
-- scipy>=0.19.0,<0.20.0
-- scikit-learn>=0.18.0,<=0.19.1
-- pandas>=0.19.0,<0.23.0
-
-- pip:
-    # Required packages for AzureML execution, history, and data preparation.
-  - --extra-index-url https://pypi.python.org/simple
-  - azureml-sdk[automl]
-  - azureml-train-widgets
-  - azure-cli
-  - pandas_ml
-  

automl/automl_setup.cmd

@@ -1,41 +0,0 @@
-@echo off
-set conda_env_name=%1
-
-IF "%conda_env_name%"=="" SET conda_env_name="azure_automl"
-
-call conda activate %conda_env_name% 2>nul:
-
-if not errorlevel 1 (
-  call conda env update --file automl_env.yml -n %conda_env_name%
-  if errorlevel 1 goto ErrorExit
-) else (
-  call conda env create -f automl_env.yml -n %conda_env_name%
-)
-
-call conda activate %conda_env_name% 2>nul:
-if errorlevel 1 goto ErrorExit
-
-call pip install psutil
-
-call jupyter nbextension install --py azureml.train.widgets
-if errorlevel 1 goto ErrorExit
-
-call jupyter nbextension enable --py azureml.train.widgets
-if errorlevel 1 goto ErrorExit
-
-echo.
-echo.
-echo ***************************************
-echo * AutoML setup completed successfully *
-echo ***************************************
-echo.
-echo Starting jupyter notebook - please run notebook 00.configuration
-echo.
-jupyter notebook --log-level=50
-
-goto End
-
-:ErrorExit
-echo Install failed
-
-:End

automl/automl_setup_linux.sh

@@ -1,34 +0,0 @@
-#!/bin/bash
-
-CONDA_ENV_NAME=$1
-
-if [ "$CONDA_ENV_NAME" == "" ]
-then
-  CONDA_ENV_NAME="azure_automl"
-fi
-
-if source activate $CONDA_ENV_NAME 2> /dev/null
-then
-   conda env update -file automl_env.yml -n $CONDA_ENV_NAME
-else
-   conda env create -f automl_env.yml -n $CONDA_ENV_NAME &&
-   source activate $CONDA_ENV_NAME &&
-   jupyter nbextension install --py azureml.train.widgets --user &&
-   jupyter nbextension enable --py azureml.train.widgets --user &&
-   echo "" &&
-   echo "" &&
-   echo "***************************************" &&
-   echo "* AutoML setup completed successfully *" &&
-   echo "***************************************" &&
-   echo "" &&
-   echo "Starting jupyter notebook - please run notebook 00.configuration" &&
-   echo "" &&
-   jupyter notebook --log-level=50
-fi
-
-if [ $? -gt 0 ]
-then
-   echo "Installation failed"
-fi
-
-

automl/automl_setup_mac.sh

@@ -1,35 +0,0 @@
-#!/bin/bash
-
-CONDA_ENV_NAME=$1
-
-if [ "$CONDA_ENV_NAME" == "" ]
-then
-  CONDA_ENV_NAME="azure_automl"
-fi
-
-if source activate $CONDA_ENV_NAME 2> /dev/null
-then
-   conda env update -file automl_env.yml -n $CONDA_ENV_NAME
-else
-   conda env create -f automl_env.yml -n $CONDA_ENV_NAME &&
-   source activate $CONDA_ENV_NAME &&
-   conda install lightgbm -c conda-forge -y &&
-   jupyter nbextension install --py azureml.train.widgets --user &&
-   jupyter nbextension enable --py azureml.train.widgets --user &&
-   echo "" &&
-   echo "" &&
-   echo "***************************************" &&
-   echo "* AutoML setup completed successfully *" &&
-   echo "***************************************" &&
-   echo "" &&
-   echo "Starting jupyter notebook - please run notebook 00.configuration" &&
-   echo "" &&
-   jupyter notebook --log-level=50
-fi
-
-if [ $? -gt 0 ]
-then
-   echo "Installation failed"
-fi
-
-

configuration.ipynb

@@ -0,0 +1,389 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/configuration.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Configuration\n",
+    "\n",
+    "_**Setting up your Azure Machine Learning services workspace and configuring your notebook library**_\n",
+    "\n",
+    "---\n",
+    "---\n",
+    "\n",
+    "## Table of Contents\n",
+    "\n",
+    "1. [Introduction](#Introduction)\n",
+    "    1. What is an Azure Machine Learning workspace\n",
+    "1. [Setup](#Setup)\n",
+    "    1. Azure subscription\n",
+    "    1. Azure ML SDK and other library installation\n",
+    "    1. Azure Container Instance registration\n",
+    "1. [Configure your Azure ML Workspace](#Configure%20your%20Azure%20ML%20workspace)\n",
+    "    1. Workspace parameters\n",
+    "    1. Access your workspace\n",
+    "    1. Create a new workspace\n",
+    "    1. Create compute resources\n",
+    "1. [Next steps](#Next%20steps)\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "This notebook configures your library of notebooks to connect to an Azure Machine Learning (ML) workspace.  In this case, a library contains all of the notebooks in the current folder and any nested folders.  You can configure this notebook library to use an existing workspace or create a new workspace.\n",
+    "\n",
+    "Typically you will need to run this notebook only once per notebook library as all other notebooks will use connection information that is written here.  If you want to redirect your notebook library to work with a different workspace, then you should re-run this notebook.\n",
+    "\n",
+    "In this notebook you will\n",
+    "* Learn about getting an Azure subscription\n",
+    "* Specify your workspace parameters\n",
+    "* Access or create your workspace\n",
+    "* Add a default compute cluster for your workspace\n",
+    "\n",
+    "### What is an Azure Machine Learning workspace\n",
+    "\n",
+    "An Azure ML Workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an Azure ML Workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, deployment, inference, and the monitoring of deployed models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "This section describes activities required before you can access any Azure ML services functionality."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Azure Subscription\n",
+    "\n",
+    "In order to create an Azure ML Workspace, first you need access to an Azure subscription.  An Azure subscription allows you to manage storage, compute, and other assets in the Azure cloud.  You can [create a new subscription](https://azure.microsoft.com/en-us/free/) or access existing subscription information from the [Azure portal](https://portal.azure.com).  Later in this notebook you will need information such as your subscription ID in order to create and access AML workspaces.\n",
+    "\n",
+    "### 2. Azure ML SDK and other library installation\n",
+    "\n",
+    "If you are running in your own environment, follow [SDK installation instructions](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment).  If you are running in Azure Notebooks or another Microsoft managed environment, the SDK is already installed.\n",
+    "\n",
+    "Also install following libraries to your environment. Many of the example notebooks depend on them\n",
+    "\n",
+    "```\n",
+    "(myenv) $ conda install -y matplotlib tqdm scikit-learn\n",
+    "```\n",
+    "\n",
+    "Once installation is complete, the following cell checks the Azure ML SDK version:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "install"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "import azureml.core\n",
+    "\n",
+    "print(\"This notebook was created using version AZUREML-SDK-VERSION of the Azure ML SDK\")\n",
+    "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you are using an older version of the SDK then this notebook was created using, you should upgrade your SDK.\n",
+    "\n",
+    "### 3. Azure Container Instance registration\n",
+    "Azure Machine Learning uses of [Azure Container Instance (ACI)](https://azure.microsoft.com/services/container-instances) to deploy dev/test web services. An Azure subscription needs to be registered to use ACI.  If you or the subscription owner have not yet registered ACI on your subscription, you will need to use the [Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest) and execute the following commands.  Note that if you ran through the AML [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) you have already registered ACI. \n",
+    "\n",
+    "```shell\n",
+    "# check to see if ACI is already registered\n",
+    "(myenv) $ az provider show -n Microsoft.ContainerInstance -o table\n",
+    "\n",
+    "# if ACI is not registered, run this command.\n",
+    "# note you need to be the subscription owner in order to execute this command successfully.\n",
+    "(myenv) $ az provider register -n Microsoft.ContainerInstance\n",
+    "```\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure your Azure ML workspace\n",
+    "\n",
+    "### Workspace parameters\n",
+    "\n",
+    "To use an AML Workspace, you will need to import the Azure ML SDK and supply the following information:\n",
+    "* Your subscription id\n",
+    "* A resource group name\n",
+    "* (optional) The region that will host your workspace\n",
+    "* A name for your workspace\n",
+    "\n",
+    "You can get your subscription ID from the [Azure portal](https://portal.azure.com).\n",
+    "\n",
+    "You will also need access to a [_resource group_](https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-group-overview#resource-groups), which organizes Azure resources and provides a default region for the resources in a group.  You can see what resource groups to which you have access, or create a new one in the [Azure portal](https://portal.azure.com).  If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n",
+    "\n",
+    "The region to host your workspace will be used if you are creating a new workspace.  You do not need to specify this if you are using an existing workspace. You can find the list of supported regions [here](https://azure.microsoft.com/en-us/global-infrastructure/services/?products=machine-learning-service).  You should pick a region that is close to your location or that contains your data.\n",
+    "\n",
+    "The name for your workspace is unique within the subscription and should be descriptive enough to discern among other AML Workspaces.  The subscription may be used only by you, or it may be used by your department or your entire enterprise, so choose a name that makes sense for your situation.\n",
+    "\n",
+    "The following cell allows you to specify your workspace parameters.  This cell uses the python method `os.getenv` to read values from environment variables which is useful for automation.  If no environment variable exists, the parameters will be set to the specified default values.  \n",
+    "\n",
+    "If you ran the Azure Machine Learning [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) in Azure Notebooks, you already have a configured workspace!  You can go to your Azure Machine Learning Getting Started library, view *config.json* file, and copy-paste the values for subscription ID, resource group and workspace name below.\n",
+    "\n",
+    "Replace the default values in the cell below with your workspace parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "subscription_id = os.getenv(\"SUBSCRIPTION_ID\", default=\"<my-subscription-id>\")\n",
+    "resource_group = os.getenv(\"RESOURCE_GROUP\", default=\"<my-resource-group>\")\n",
+    "workspace_name = os.getenv(\"WORKSPACE_NAME\", default=\"<my-workspace-name>\")\n",
+    "workspace_region = os.getenv(\"WORKSPACE_REGION\", default=\"eastus2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Access your workspace\n",
+    "\n",
+    "The following cell uses the Azure ML SDK to attempt to load the workspace specified by your parameters.  If this cell succeeds, your notebook library will be configured to access the workspace from all notebooks using the `Workspace.from_config()` method.  The cell can fail if the specified workspace doesn't exist or you don't have permissions to access it. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace\n",
+    "\n",
+    "try:\n",
+    "    ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name)\n",
+    "    # write the details of the workspace to a configuration file to the notebook library\n",
+    "    ws.write_config()\n",
+    "    print(\"Workspace configuration succeeded. Skip the workspace creation steps below\")\n",
+    "except:\n",
+    "    print(\"Workspace not accessible. Change your parameters or create a new workspace below\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a new workspace\n",
+    "\n",
+    "If you don't have an existing workspace and are the owner of the subscription or resource group, you can create a new workspace.  If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n",
+    "\n",
+    "**Note**: As with other Azure services, there are limits on certain resources (for example AmlCompute quota) associated with the Azure ML service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n",
+    "\n",
+    "This cell will create an Azure ML workspace for you in a subscription provided you have the correct permissions.\n",
+    "\n",
+    "This will fail if:\n",
+    "* You do not have permission to create a workspace in the resource group\n",
+    "* You do not have permission to create a resource group if it's non-existing.\n",
+    "* You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription\n",
+    "\n",
+    "If workspace creation fails, please work with your IT admin to provide you with the appropriate permissions or to provision the required resources.\n",
+    "\n",
+    "**Note**: A Basic workspace is created by default. If you would like to create an Enterprise workspace, please specify sku = 'enterprise'.\n",
+    "Please visit our [pricing page](https://azure.microsoft.com/en-us/pricing/details/machine-learning/) for more details on our Enterprise edition.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "create workspace"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace\n",
+    "\n",
+    "# Create the workspace using the specified parameters\n",
+    "ws = Workspace.create(name = workspace_name,\n",
+    "                      subscription_id = subscription_id,\n",
+    "                      resource_group = resource_group, \n",
+    "                      location = workspace_region,\n",
+    "                      create_resource_group = True,\n",
+    "                      sku = 'basic',\n",
+    "                      exist_ok = True)\n",
+    "ws.get_details()\n",
+    "\n",
+    "# write the details of the workspace to a configuration file to the notebook library\n",
+    "ws.write_config()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create compute resources for your training experiments\n",
+    "\n",
+    "Many of the sample notebooks use Azure ML managed compute (AmlCompute) to train models using a dynamically scalable pool of compute. In this section you will create default compute clusters for use by the other notebooks and any other operations you choose.\n",
+    "\n",
+    "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
+    "\n",
+    "To create a cluster, you need to specify a compute configuration that specifies the type of machine to be used and the scalability behaviors.  Then you choose a name for the cluster that is unique within the workspace that can be used to address the cluster later.\n",
+    "\n",
+    "The cluster parameters are:\n",
+    "* vm_size - this describes the virtual machine type and size used in the cluster.  All machines in the cluster are the same type.  You can get the list of vm sizes available in your region by using the CLI command\n",
+    "\n",
+    "```shell\n",
+    "az vm list-skus -o tsv\n",
+    "```\n",
+    "* min_nodes - this sets the minimum size of the cluster.  If you set the minimum to 0 the cluster will shut down all nodes while not in use.  Setting this number to a value higher than 0 will allow for faster start-up times, but you will also be billed when the cluster is not in use.\n",
+    "* max_nodes - this sets the maximum size of the cluster.  Setting this to a larger number allows for more concurrency and a greater distributed processing of scale-out jobs.\n",
+    "\n",
+    "\n",
+    "To create a **CPU** cluster now, run the cell below. The autoscale settings mean that the cluster will scale down to 0 nodes when inactive and up to 4 nodes when busy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+    "from azureml.core.compute_target import ComputeTargetException\n",
+    "\n",
+    "# Choose a name for your CPU cluster\n",
+    "cpu_cluster_name = \"cpu-cluster\"\n",
+    "\n",
+    "# Verify that cluster does not exist already\n",
+    "try:\n",
+    "    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
+    "    print(\"Found existing cpu-cluster\")\n",
+    "except ComputeTargetException:\n",
+    "    print(\"Creating new cpu-cluster\")\n",
+    "    \n",
+    "    # Specify the configuration for the new cluster\n",
+    "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_D2_V2\",\n",
+    "                                                           min_nodes=0,\n",
+    "                                                           max_nodes=4)\n",
+    "\n",
+    "    # Create the cluster with the specified name and configuration\n",
+    "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
+    "    \n",
+    "    # Wait for the cluster to complete, show the output log\n",
+    "    cpu_cluster.wait_for_completion(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create a **GPU** cluster, run the cell below. Note that your subscription must have sufficient quota for GPU VMs or the command will fail. To increase quota, see [these instructions](https://docs.microsoft.com/en-us/azure/azure-supportability/resource-manager-core-quotas-request). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+    "from azureml.core.compute_target import ComputeTargetException\n",
+    "\n",
+    "# Choose a name for your GPU cluster\n",
+    "gpu_cluster_name = \"gpu-cluster\"\n",
+    "\n",
+    "# Verify that cluster does not exist already\n",
+    "try:\n",
+    "    gpu_cluster = ComputeTarget(workspace=ws, name=gpu_cluster_name)\n",
+    "    print(\"Found existing gpu cluster\")\n",
+    "except ComputeTargetException:\n",
+    "    print(\"Creating new gpu-cluster\")\n",
+    "    \n",
+    "    # Specify the configuration for the new cluster\n",
+    "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_NC6\",\n",
+    "                                                           min_nodes=0,\n",
+    "                                                           max_nodes=4)\n",
+    "    # Create the cluster with the specified name and configuration\n",
+    "    gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, compute_config)\n",
+    "\n",
+    "    # Wait for the cluster to complete, show the output log\n",
+    "    gpu_cluster.wait_for_completion(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Next steps\n",
+    "\n",
+    "In this notebook you configured this notebook library to connect easily to an Azure ML workspace.  You can copy this notebook to your own libraries to connect them to you workspace, or use it to bootstrap new workspaces completely.\n",
+    "\n",
+    "If you came here from another notebook, you can return there and complete that exercise, or you can try out the [Tutorials](./tutorials) or jump into \"how-to\" notebooks and start creating and deploying models.  A good place to start is the [train within notebook](./how-to-use-azureml/training/train-within-notebook) example that walks through a simplified but complete end to end machine learning process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "ninhu"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

configuration.yml

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

databricks/01.Installation_and_Configuration.ipynb

@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","source":["Azure ML & Azure Databricks notebooks by Parashar Shah.\n\nCopyright (c) Microsoft Corporation. All rights reserved.\n\nLicensed under the MIT License."],"metadata":{}},{"cell_type":"markdown","source":["Please ensure you have run this notebook before proceeding."],"metadata":{}},{"cell_type":"markdown","source":["Now 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 (during private preview). You can select the option to attach the library to all clusters or just one cluster.\n\nProvide this full string to install the SDK:\n\nazureml-sdk[databricks]"],"metadata":{}},{"cell_type":"code","source":["import azureml.core\n\n# Check core SDK version number - based on build number of preview/master.\nprint(\"SDK version:\", azureml.core.VERSION)"],"metadata":{},"outputs":[],"execution_count":4},{"cell_type":"code","source":["subscription_id = \"<your-subscription-id>\"\nresource_group = \"<your-existing-resource-group>\"\nworkspace_name = \"<a-new-or-existing-workspace; it is unrelated to Databricks workspace>\"\nworkspace_region = \"<your-resource group-region>\""],"metadata":{},"outputs":[],"execution_count":5},{"cell_type":"code","source":["# import the Workspace class and check the azureml SDK version\n# exist_ok checks if workspace exists or not.\n\nfrom azureml.core import Workspace\n\nws = Workspace.create(name = workspace_name,\n                      subscription_id = subscription_id,\n                      resource_group = resource_group, \n                      location = workspace_region,\n                      exist_ok=True)\n\nws.get_details()"],"metadata":{},"outputs":[],"execution_count":6},{"cell_type":"code","source":["ws = Workspace(workspace_name = workspace_name,\n               subscription_id = subscription_id,\n               resource_group = resource_group)\n\n# persist the subscription id, resource group name, and workspace name in aml_config/config.json.\nws.write_config()"],"metadata":{},"outputs":[],"execution_count":7},{"cell_type":"code","source":["%sh\ncat /databricks/driver/aml_config/config.json"],"metadata":{},"outputs":[],"execution_count":8},{"cell_type":"code","source":["# import the Workspace class and check the azureml SDK version\nfrom azureml.core import Workspace\n\nws = Workspace.from_config()\nprint('Workspace name: ' + ws.name, \n      'Azure region: ' + ws.location, \n      'Subscription id: ' + ws.subscription_id, \n      'Resource group: ' + ws.resource_group, sep = '\\n')"],"metadata":{},"outputs":[],"execution_count":9},{"cell_type":"code","source":["dbutils.notebook.exit(\"success\")"],"metadata":{},"outputs":[],"execution_count":10},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":11}],"metadata":{"name":"01.Installation_and_Configuration","notebookId":3874566296719377},"nbformat":4,"nbformat_minor":0}

databricks/02.Ingest_data.ipynb

@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","source":["Azure ML & Azure Databricks notebooks by Parashar Shah.\n\nCopyright (c) Microsoft Corporation. All rights reserved.\n\nLicensed under the MIT License."],"metadata":{}},{"cell_type":"markdown","source":["Please ensure you have run all previous notebooks in sequence before running this."],"metadata":{}},{"cell_type":"markdown","source":["#Data Ingestion"],"metadata":{}},{"cell_type":"code","source":["import os\nimport urllib"],"metadata":{},"outputs":[],"execution_count":4},{"cell_type":"code","source":["# Download AdultCensusIncome.csv from Azure CDN. This file has 32,561 rows.\nbasedataurl = \"https://amldockerdatasets.azureedge.net\"\ndatafile = \"AdultCensusIncome.csv\"\ndatafile_dbfs = os.path.join(\"/dbfs\", datafile)\n\nif os.path.isfile(datafile_dbfs):\n    print(\"found {} at {}\".format(datafile, datafile_dbfs))\nelse:\n    print(\"downloading {} to {}\".format(datafile, datafile_dbfs))\n    urllib.request.urlretrieve(os.path.join(basedataurl, datafile), datafile_dbfs)"],"metadata":{},"outputs":[],"execution_count":5},{"cell_type":"code","source":["# Create a Spark dataframe out of the csv file.\ndata_all = sqlContext.read.format('csv').options(header='true', inferSchema='true', ignoreLeadingWhiteSpace='true', ignoreTrailingWhiteSpace='true').load(datafile)\nprint(\"({}, {})\".format(data_all.count(), len(data_all.columns)))\ndata_all.printSchema()"],"metadata":{},"outputs":[],"execution_count":6},{"cell_type":"code","source":["#renaming columns\ncolumns_new = [col.replace(\"-\", \"_\") for col in data_all.columns]\ndata_all = data_all.toDF(*columns_new)\ndata_all.printSchema()"],"metadata":{},"outputs":[],"execution_count":7},{"cell_type":"code","source":["display(data_all.limit(5))"],"metadata":{},"outputs":[],"execution_count":8},{"cell_type":"markdown","source":["#Data Preparation"],"metadata":{}},{"cell_type":"code","source":["# Choose feature columns and the label column.\nlabel = \"income\"\nxvals_all = set(data_all.columns) - {label}\n\n#dbutils.widgets.remove(\"xvars_multiselect\")\ndbutils.widgets.removeAll()\n\ndbutils.widgets.multiselect('xvars_multiselect', 'hours_per_week', xvals_all)\nxvars_multiselect = dbutils.widgets.get(\"xvars_multiselect\")\nxvars = xvars_multiselect.split(\",\")\n\nprint(\"label = {}\".format(label))\nprint(\"features = {}\".format(xvars))\n\ndata = data_all.select([*xvars, label])\n\n# Split data into train and test.\ntrain, test = data.randomSplit([0.75, 0.25], seed=123)\n\nprint(\"train ({}, {})\".format(train.count(), len(train.columns)))\nprint(\"test ({}, {})\".format(test.count(), len(test.columns)))"],"metadata":{},"outputs":[],"execution_count":10},{"cell_type":"markdown","source":["#Data Persistence"],"metadata":{}},{"cell_type":"code","source":["# Write the train and test data sets to intermediate storage\ntrain_data_path = \"AdultCensusIncomeTrain\"\ntest_data_path = \"AdultCensusIncomeTest\"\n\ntrain_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTrain\")\ntest_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTest\")\n\ntrain.write.mode('overwrite').parquet(train_data_path)\ntest.write.mode('overwrite').parquet(test_data_path)\nprint(\"train and test datasets saved to {} and {}\".format(train_data_path_dbfs, test_data_path_dbfs))"],"metadata":{},"outputs":[],"execution_count":12},{"cell_type":"code","source":["dbutils.notebook.exit(\"success\")"],"metadata":{},"outputs":[],"execution_count":13},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":14}],"metadata":{"name":"02.Ingest_data","notebookId":3874566296719393},"nbformat":4,"nbformat_minor":0}

databricks/05.Deploy_to_AKS_existingImage.ipynb

@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","source":["Azure ML & Azure Databricks notebooks by Parashar Shah.\n\nCopyright (c) Microsoft Corporation. All rights reserved.\n\nLicensed under the MIT License."],"metadata":{}},{"cell_type":"markdown","source":["Please ensure you have run all previous notebooks in sequence before running this. This notebook uses image from ACI notebook for deploying to AKS."],"metadata":{}},{"cell_type":"code","source":["from azureml.core import Workspace\nimport azureml.core\n\n# Check core SDK version number\nprint(\"SDK version:\", azureml.core.VERSION)\n\n#'''\nws = Workspace.from_config()\nprint('Workspace name: ' + ws.name, \n      'Azure region: ' + ws.location, \n      'Subscription id: ' + ws.subscription_id, \n      'Resource group: ' + ws.resource_group, sep = '\\n')\n#'''"],"metadata":{},"outputs":[],"execution_count":3},{"cell_type":"code","source":["# List images by ws\n\nfrom azureml.core.image import ContainerImage\nfor 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))"],"metadata":{},"outputs":[],"execution_count":4},{"cell_type":"code","source":["from azureml.core.image import Image\nmyimage = Image(workspace=ws, id=\"aciws:25\")"],"metadata":{},"outputs":[],"execution_count":5},{"cell_type":"code","source":["#create AKS compute\n#it may take 20-25 minutes to create a new cluster\n\nfrom azureml.core.compute import AksCompute, ComputeTarget\n\n# Use the default configuration (can also provide parameters to customize)\nprov_config = AksCompute.provisioning_configuration()\n\naks_name = 'ps-aks-clus2' \n\n# Create the cluster\naks_target = ComputeTarget.create(workspace = ws, \n                                  name = aks_name, \n                                  provisioning_configuration = prov_config)\n\naks_target.wait_for_completion(show_output = True)\n\nprint(aks_target.provisioning_state)\nprint(aks_target.provisioning_errors)"],"metadata":{},"outputs":[],"execution_count":6},{"cell_type":"code","source":["from azureml.core.webservice import Webservice\nhelp( Webservice.deploy_from_image)"],"metadata":{},"outputs":[],"execution_count":7},{"cell_type":"code","source":["from azureml.core.webservice import Webservice, AksWebservice\nfrom azureml.core.image import ContainerImage\n\n#Set the web service configuration (using default here)\naks_config = AksWebservice.deploy_configuration()\n\n#unique service name\nservice_name ='ps-aks-service'\n\n# Webservice creation using single command, there is a variant to use image directly as well.\naks_service = Webservice.deploy_from_image(\n  workspace=ws, \n  name=service_name,\n  deployment_config = aks_config,\n  image = myimage,\n  deployment_target = aks_target\n    )\n\naks_service.wait_for_deployment(show_output=True)"],"metadata":{},"outputs":[],"execution_count":8},{"cell_type":"code","source":["#for using the Web HTTP API \nprint(aks_service.scoring_uri)\nprint(aks_service.get_keys())"],"metadata":{},"outputs":[],"execution_count":9},{"cell_type":"code","source":["import json\n\n#get the some sample data\ntest_data_path = \"AdultCensusIncomeTest\"\ntest = spark.read.parquet(test_data_path).limit(5)\n\ntest_json = json.dumps(test.toJSON().collect())\n\nprint(test_json)"],"metadata":{},"outputs":[],"execution_count":10},{"cell_type":"code","source":["#using data defined above predict if income is >50K (1) or <=50K (0)\naks_service.run(input_data=test_json)"],"metadata":{},"outputs":[],"execution_count":11},{"cell_type":"code","source":["#comment to not delete the web service\naks_service.delete()\n#image.delete()\n#model.delete()\n#aks_target.delete()"],"metadata":{},"outputs":[],"execution_count":12},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":13}],"metadata":{"name":"04.DeploytoACI","notebookId":3874566296719318},"nbformat":4,"nbformat_minor":0}

databricks/readme.md

@@ -1,26 +0,0 @@
-# Azure Databricks - Azure ML SDK Sample Notebooks
-
-**NOTE**: With the latest version of our AML SDK, there are some API changes due to which previous version of notebooks will not work.
-Kindly use this v4 notebooks (updated Sep 18)– if you had installed the AML SDK in your Databricks cluster please update to latest SDK version by installing azureml-sdk[databricks] as a library from GUI. 
-
-**NOTE**: Please create your Azure Databricks cluster as v4.x (high concurrency preferred) with **Python 3** (dropdown). We are extending it to more runtimes asap.
-
-**NOTE**: Some packages like psutil upgrade libs that can cause a conflict, please install such packages by freezing lib version. Eg. "pstuil **cryptography==1.5 pyopenssl==16.0.0 ipython=2.2.0**" to avoid install error. This issue is related to Databricks and not related to AML SDK.
-
-**NOTE**: You should at least have contributor access to your Azure subcription to run some of the notebooks.
-
-The iPython Notebooks have to be run sequentially after making changes based on your subscription. The corresponding DBC archive contains all the notebooks and can be imported into your Databricks workspace. You can the run notebooks after importing .dbc instead of downloading individually.  
-
-This set of notebooks 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. For details on SDK concepts, please refer to [Private preview notebooks](https://github.com/Azure/ViennaDocs/tree/master/PrivatePreview/notebooks)
-
-(Recommended) [Azure Databricks AML SDK notebooks](Databricks_AMLSDK_github.dbc) A single DBC package to import all notebooks in your Databricks workspace.
-
-01. [Installation and Configuration](01.Installation_and_Configuration.ipynb): Install the Azure ML Python SDK and Initialize an Azure ML Workspace and save the Workspace configuration file.
-02. [Ingest data](02.Ingest_data.ipynb): Download the Adult Census Income dataset and split it into train and test sets.
-03. [Build model](03a.Build_model.ipynb): Train a binary classification model in Azure Databricks with a Spark ML Pipeline.
-04. [Build model with Run History](03b.Build_model_runHistory.ipynb): Train model and also capture run history (tracking) with Azure ML Python SDK.
-05. [Deploy to ACI](04.Deploy_to_ACI.ipynb): Deploy model to Azure Container Instance (ACI) with Azure ML Python SDK.
-06. [Deploy to AKS](04.Deploy_to_AKS_existingImage.ipynb): Deploy model to Azure Kubernetis Service (AKS) with Azure ML Python SDK from an existing Image with model, conda and score file.
-
-Copyright (c) Microsoft Corporation. All rights reserved. 
-All notebooks in this folder are licensed under the MIT License. 

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

@@ -0,0 +1,217 @@
+
+NOTICES AND INFORMATION
+Do Not Translate or Localize
+
+This Azure Machine Learning service example notebooks repository includes material from the projects listed below. 
+
+
+1. SSD-Tensorflow  (https://github.com/balancap/ssd-tensorflow)
+
+
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+END OF SSD-Tensorflow NOTICES AND INFORMATION

how-to-use-azureml/deployment/accelerated-models/README.md

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

@@ -0,0 +1,371 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deploy models to Azure Kubernetes Service (AKS) using controlled roll out\n",
+    "This notebook will show you how to deploy mulitple AKS webservices with the same scoring endpoint and how to roll out your models in a controlled manner by configuring % of scoring traffic going to each webservice. If you are using a Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) to install the Azure Machine Learning Python SDK and create an Azure ML Workspace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check for latest version\n",
+    "import azureml.core\n",
+    "print(azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize workspace\n",
+    "Create a [Workspace](https://docs.microsoft.com/python/api/azureml-core/azureml.core.workspace%28class%29?view=azure-ml-py) object from your persisted configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.workspace import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Register the model\n",
+    "Register a file or folder as a model by calling [Model.register()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#register-workspace--model-path--model-name--tags-none--properties-none--description-none--datasets-none--model-framework-none--model-framework-version-none--child-paths-none-).\n",
+    "In addition to the content of the model file itself, your registered model will also store model metadata -- model description, tags, and framework information -- that will be useful when managing and deploying models in your workspace. Using tags, for instance, you can categorize your models and apply filters when listing models in your workspace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Model\n",
+    "\n",
+    "model = Model.register(workspace=ws,\n",
+    "                       model_name='sklearn_regression_model.pkl',                # Name of the registered model in your workspace.\n",
+    "                       model_path='./sklearn_regression_model.pkl',  # Local file to upload and register as a model.\n",
+    "                       model_framework=Model.Framework.SCIKITLEARN,  # Framework used to create the model.\n",
+    "                       model_framework_version='0.19.1',             # Version of scikit-learn used to create the model.\n",
+    "                       description='Ridge regression model to predict diabetes progression.',\n",
+    "                       tags={'area': 'diabetes', 'type': 'regression'})\n",
+    "\n",
+    "print('Name:', model.name)\n",
+    "print('Version:', model.version)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Register an environment (for all models)\n",
+    "\n",
+    "If you control over how your model is run, or if it has special runtime requirements, you can specify your own environment and scoring method.\n",
+    "\n",
+    "Specify the model's runtime environment by creating an [Environment](https://docs.microsoft.com/python/api/azureml-core/azureml.core.environment%28class%29?view=azure-ml-py) object and providing the [CondaDependencies](https://docs.microsoft.com/python/api/azureml-core/azureml.core.conda_dependencies.condadependencies?view=azure-ml-py) needed by your model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Environment\n",
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "\n",
+    "environment=Environment('my-sklearn-environment')\n",
+    "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
+    "    'azureml-defaults',\n",
+    "    'inference-schema[numpy-support]',\n",
+    "    'numpy',\n",
+    "    'scikit-learn==0.19.1',\n",
+    "    'scipy'\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When using a custom environment, you must also provide Python code for initializing and running your model. An example script is included with this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('score.py') as f:\n",
+    "    print(f.read())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the InferenceConfig\n",
+    "Create the inference configuration to reference your environment and entry script during deployment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.model import InferenceConfig\n",
+    "\n",
+    "inference_config = InferenceConfig(entry_script='score.py', \n",
+    "                                   source_directory='.',\n",
+    "                                   environment=environment)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Provision the AKS Cluster\n",
+    "If you already have an AKS cluster attached to this workspace, skip the step below and provide the name of the cluster.\n",
+    "\n",
+    "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import AksCompute\n",
+    "from azureml.core.compute import ComputeTarget\n",
+    "# Use the default configuration (can also provide parameters to customize)\n",
+    "prov_config = AksCompute.provisioning_configuration()\n",
+    "\n",
+    "aks_name = 'my-aks' \n",
+    "# Create the cluster\n",
+    "aks_target = ComputeTarget.create(workspace = ws, \n",
+    "                                  name = aks_name, \n",
+    "                                  provisioning_configuration = prov_config) \n",
+    "aks_target.wait_for_completion(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create an Endpoint and add a version (AKS service)\n",
+    "This creates a new endpoint and adds a version behind it. By default the first version added is the default version. You can specify the traffic percentile a version takes behind an endpoint. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# deploying the model and create a new endpoint\n",
+    "from azureml.core.webservice import AksEndpoint\n",
+    "# from azureml.core.compute import ComputeTarget\n",
+    "\n",
+    "#select a created compute\n",
+    "compute = ComputeTarget(ws, 'my-aks')\n",
+    "namespace_name=\"endpointnamespace\"\n",
+    "# define the endpoint name\n",
+    "endpoint_name = \"myendpoint1\"\n",
+    "# define the service name\n",
+    "version_name= \"versiona\"\n",
+    "\n",
+    "endpoint_deployment_config = AksEndpoint.deploy_configuration(tags = {'modelVersion':'firstversion', 'department':'finance'}, \n",
+    "                                                              description = \"my first version\", namespace = namespace_name, \n",
+    "                                                              version_name = version_name, traffic_percentile = 40)\n",
+    "\n",
+    "endpoint = Model.deploy(ws, endpoint_name, [model], inference_config, endpoint_deployment_config, compute)\n",
+    "endpoint.wait_for_deployment(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "endpoint.get_logs()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Add another version of the service to an existing endpoint\n",
+    "This adds another version behind an existing endpoint. You can specify the traffic percentile the new version takes. If no traffic_percentile is specified then it defaults to 0. All the unspecified traffic percentile (in this example 50) across all versions goes to default version."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Adding a new version to an existing Endpoint.\n",
+    "version_name_add=\"versionb\" \n",
+    "\n",
+    "endpoint.create_version(version_name = version_name_add, inference_config=inference_config, models=[model], tags = {'modelVersion':'secondversion', 'department':'finance'}, \n",
+    "                        description = \"my second version\", traffic_percentile = 10)\n",
+    "endpoint.wait_for_deployment(True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Update an existing version in an endpoint\n",
+    "There are two types of versions: control and treatment. An endpoint contains one or more treatment versions but only one control version. This categorization helps compare the different versions against the defined control version."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "endpoint.update_version(version_name=endpoint.versions[version_name_add].name, description=\"my second version update\", traffic_percentile=40, is_default=True, is_control_version_type=True)\n",
+    "endpoint.wait_for_deployment(True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test the web service using run method\n",
+    "Test the web sevice by passing in data. Run() method retrieves API keys behind the scenes to make sure that call is authenticated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scoring on endpoint\n",
+    "import json\n",
+    "test_sample = json.dumps({'data': [\n",
+    "    [1,2,3,4,5,6,7,8,9,10], \n",
+    "    [10,9,8,7,6,5,4,3,2,1]\n",
+    "]})\n",
+    "\n",
+    "test_sample_encoded = bytes(test_sample, encoding='utf8')\n",
+    "prediction = endpoint.run(input_data=test_sample_encoded)\n",
+    "print(prediction)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Delete Resources"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# deleting a version in an endpoint\n",
+    "endpoint.delete_version(version_name=version_name)\n",
+    "endpoint.wait_for_deployment(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# deleting an endpoint, this will delete all versions in the endpoint and the endpoint itself\n",
+    "endpoint.delete()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "shipatel"
+   }
+  ],
+  "category": "deployment",
+  "compute": [
+   "None"
+  ],
+  "datasets": [
+   "Diabetes"
+  ],
+  "deployment": [
+   "Azure Kubernetes Service"
+  ],
+  "exclude_from_index": false,
+  "framework": [
+   "Scikit-learn"
+  ],
+  "friendly_name": "Deploy models to AKS using controlled roll out",
+  "index_order": 3,
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.0"
+  },
+  "star_tag": [
+   "featured"
+  ],
+  "tags": [
+   "None"
+  ],
+  "task": "Deploy a model with Azure Machine Learning"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

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

how-to-use-azureml/deployment/deploy-with-controlled-rollout/score.py

@@ -0,0 +1,28 @@
+import pickle
+import json
+import numpy
+from sklearn.externals import joblib
+from sklearn.linear_model import Ridge
+from azureml.core.model import Model
+
+
+def init():
+    global model
+    # note here "sklearn_regression_model.pkl" is the name of the model registered under
+    # this is a different behavior than before when the code is run locally, even though the code is the same.
+    model_path = Model.get_model_path('sklearn_regression_model.pkl')
+    # deserialize the model file back into a sklearn model
+    model = joblib.load(model_path)
+
+
+# note you can pass in multiple rows for scoring
+def run(raw_data):
+    try:
+        data = json.loads(raw_data)['data']
+        data = numpy.array(data)
+        result = model.predict(data)
+        # you can return any data type as long as it is JSON-serializable
+        return result.tolist()
+    except Exception as e:
+        error = str(e)
+        return error

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

@@ -0,0 +1,498 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Enabling App Insights for Services in Production\n",
+    "With this notebook, you can learn how to enable App Insights for standard service monitoring, plus, we provide examples for doing custom logging within a scoring files in a model.\n",
+    "\n",
+    "\n",
+    "## What does Application Insights monitor?\n",
+    "It monitors request rates, response times, failure rates, etc. For more information visit [App Insights docs.](https://docs.microsoft.com/en-us/azure/application-insights/app-insights-overview)\n",
+    "\n",
+    "\n",
+    "## What is different compared to standard production deployment process?\n",
+    "If you want to enable generic App Insights for a service run:\n",
+    "```python\n",
+    "aks_service= Webservice(ws, \"aks-w-dc2\")\n",
+    "aks_service.update(enable_app_insights=True)```\n",
+    "Where \"aks-w-dc2\" is your service name. You can also do this from the Azure Portal under your Workspace--> deployments--> Select deployment--> Edit--> Advanced Settings--> Select \"Enable AppInsights diagnostics\"\n",
+    "\n",
+    "If you want to log custom traces, you will follow the standard deplyment process for AKS and you will:\n",
+    "1. Update scoring file.\n",
+    "2. Update aks configuration.\n",
+    "3. Deploy the model with this new configuration. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Import your dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import azureml.core\n",
+    "import json\n",
+    "\n",
+    "from azureml.core import Workspace\n",
+    "from azureml.core.compute import AksCompute, ComputeTarget\n",
+    "from azureml.core.webservice import AksWebservice\n",
+    "\n",
+    "print(azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Set up your configuration and create a workspace\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Register Model\n",
+    "Register an existing trained model, add descirption and tags."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Model\n",
+    "\n",
+    "model = Model.register(model_path=\"sklearn_regression_model.pkl\", # This points to a local file.\n",
+    "                       model_name=\"sklearn_regression_model.pkl\", # This is the name the model is registered as.\n",
+    "                       tags={'area': \"diabetes\", 'type': \"regression\"},\n",
+    "                       description=\"Ridge regression model to predict diabetes\",\n",
+    "                       workspace=ws)\n",
+    "\n",
+    "print(model.name, model.description, model.version)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. *Update your scoring file with custom print statements*\n",
+    "Here is an example:\n",
+    "### a. In your init function add:\n",
+    "```python\n",
+    "print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))```\n",
+    "\n",
+    "### b. In your run function add:\n",
+    "```python\n",
+    "print (\"Prediction created\" + time.strftime(\"%H:%M:%S\"))```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile score.py\n",
+    "import os\n",
+    "import pickle\n",
+    "import json\n",
+    "import numpy\n",
+    "from sklearn.externals import joblib\n",
+    "from sklearn.linear_model import Ridge\n",
+    "import time\n",
+    "\n",
+    "def init():\n",
+    "    global model\n",
+    "    #Print statement for appinsights custom traces:\n",
+    "    print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))\n",
+    "\n",
+    "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
+    "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
+    "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
+    "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
+    "\n",
+    "    # deserialize the model file back into a sklearn model\n",
+    "    model = joblib.load(model_path)\n",
+    "\n",
+    "\n",
+    "# note you can pass in multiple rows for scoring\n",
+    "def run(raw_data):\n",
+    "    try:\n",
+    "        data = json.loads(raw_data)['data']\n",
+    "        data = numpy.array(data)\n",
+    "        result = model.predict(data)\n",
+    "        print (\"Prediction created\" + time.strftime(\"%H:%M:%S\"))\n",
+    "        # you can return any datatype as long as it is JSON-serializable\n",
+    "        return result.tolist()\n",
+    "    except Exception as e:\n",
+    "        error = str(e)\n",
+    "        print (error + time.strftime(\"%H:%M:%S\"))\n",
+    "        return error"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. *Create myenv.yml file*\n",
+    "Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service."
+   ]
+  },
+  {
+   "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==0.20.3'],\n",
+    "                                 pip_packages=['azureml-defaults'])\n",
+    "\n",
+    "with open(\"myenv.yml\",\"w\") as f:\n",
+    "    f.write(myenv.serialize_to_string())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Create Inference Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.environment import Environment\n",
+    "from azureml.core.model import InferenceConfig\n",
+    "\n",
+    "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
+    "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deploy to ACI (Optional)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.webservice import AciWebservice\n",
+    "\n",
+    "aci_deployment_config = AciWebservice.deploy_configuration(cpu_cores=1,\n",
+    "                                                           memory_gb=1,\n",
+    "                                                           tags={'area': \"diabetes\", 'type': \"regression\"},\n",
+    "                                                           description=\"Predict diabetes using regression model\",\n",
+    "                                                           enable_app_insights=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aci_service_name = \"aci-service-appinsights\"\n",
+    "\n",
+    "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aci_deployment_config, overwrite=True)\n",
+    "aci_service.wait_for_deployment(show_output=True)\n",
+    "\n",
+    "print(aci_service.state)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if aci_service.state == \"Healthy\":\n",
+    "    test_sample = json.dumps({\n",
+    "        \"data\": [\n",
+    "            [1,28,13,45,54,6,57,8,8,10],\n",
+    "            [101,9,8,37,6,45,4,3,2,41]\n",
+    "        ]\n",
+    "    })\n",
+    "\n",
+    "    prediction = aci_service.run(test_sample)\n",
+    "\n",
+    "    print(prediction)\n",
+    "else:\n",
+    "    raise ValueError(\"Service deployment isn't healthy, can't call the service. Error: \", aci_service.error)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Deploy to AKS service"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create AKS compute if you haven't done so.\n",
+    "\n",
+    "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import ComputeTarget, AksCompute\n",
+    "from azureml.core.compute_target import ComputeTargetException\n",
+    "\n",
+    "aks_name = \"my-aks-insights\"\n",
+    "\n",
+    "creating_compute = False\n",
+    "try:\n",
+    "    aks_target = ComputeTarget(ws, aks_name)\n",
+    "    print(\"Using existing AKS compute target {}.\".format(aks_name))\n",
+    "except ComputeTargetException:\n",
+    "    print(\"Creating a new AKS compute target {}.\".format(aks_name))\n",
+    "\n",
+    "    # Use the default configuration (can also provide parameters to customize).\n",
+    "    prov_config = AksCompute.provisioning_configuration()\n",
+    "    aks_target = ComputeTarget.create(workspace=ws,\n",
+    "                                      name=aks_name,\n",
+    "                                      provisioning_configuration=prov_config)\n",
+    "    creating_compute = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "if creating_compute and aks_target.provisioning_state != \"Succeeded\":\n",
+    "    aks_target.wait_for_completion(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(aks_target.provisioning_state)\n",
+    "print(aks_target.provisioning_errors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you already have a cluster you can attach the service to it:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "%%time\n",
+    "resource_id = '/subscriptions/<subscriptionid>/resourcegroups/<resourcegroupname>/providers/Microsoft.ContainerService/managedClusters/<aksservername>'\n",
+    "create_name= 'myaks4'\n",
+    "attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
+    "aks_target = ComputeTarget.attach(workspace=ws,\n",
+    "                                  name=create_name,\n",
+    "                                  attach_configuration=attach_config)\n",
+    "## Wait for the operation to complete\n",
+    "aks_target.wait_for_provisioning(True)```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### a. *Activate App Insights through updating AKS Webservice configuration*\n",
+    "In order to enable App Insights in your service you will need to update your AKS configuration file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set the web service configuration.\n",
+    "aks_deployment_config = AksWebservice.deploy_configuration(enable_app_insights=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### b. Deploy your service"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if aks_target.provisioning_state == \"Succeeded\":\n",
+    "    aks_service_name = \"aks-service-appinsights\"\n",
+    "    aks_service = Model.deploy(ws,\n",
+    "                               aks_service_name,\n",
+    "                               [model],\n",
+    "                               inference_config,\n",
+    "                               aks_deployment_config,\n",
+    "                               deployment_target=aks_target,\n",
+    "                               overwrite=True)\n",
+    "    aks_service.wait_for_deployment(show_output=True)\n",
+    "    print(aks_service.state)\n",
+    "else:\n",
+    "    raise ValueError(\"AKS cluster provisioning failed. Error: \", aks_target.provisioning_errors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 8. Test your service "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "\n",
+    "if aks_service.state == \"Healthy\":\n",
+    "    test_sample = json.dumps({\n",
+    "        \"data\": [\n",
+    "            [1,28,13,45,54,6,57,8,8,10],\n",
+    "            [101,9,8,37,6,45,4,3,2,41]\n",
+    "        ]\n",
+    "    })\n",
+    "\n",
+    "    prediction = aks_service.run(input_data=test_sample)\n",
+    "    print(prediction)\n",
+    "else:\n",
+    "    raise ValueError(\"Service deployment isn't healthy, can't call the service. Error: \", aks_service.error)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 9. See your service telemetry in App Insights\n",
+    "1. Go to the [Azure Portal](https://portal.azure.com/)\n",
+    "2. All resources--> Select the subscription/resource group where you created your Workspace--> Select the App Insights type\n",
+    "3. Click on the AppInsights resource. You'll see a highlevel dashboard with information on Requests, Server response time and availability.\n",
+    "4. Click on the top banner \"Analytics\"\n",
+    "5. In the \"Schema\" section select \"traces\" and run your query.\n",
+    "6. Voila! All your custom traces should be there."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Disable App Insights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aks_service.update(enable_app_insights=False)\n",
+    "aks_service.wait_for_deployment(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clean up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "aks_service.delete()\n",
+    "aci_service.delete()\n",
+    "model.delete()\n",
+    "if creating_compute:\n",
+    "    aks_target.delete()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "gopalv"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

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

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

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

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

@@ -0,0 +1,39 @@
+# ONNX on Azure Machine Learning
+
+These tutorials show how to create and deploy Open Neural Network eXchange ([ONNX](http://onnx.ai)) models in Azure Machine Learning environments using [ONNX Runtime](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-build-deploy-onnx) for inference. Once deployed as a web service, you can ping the model with your own set of images to be analyzed!
+
+## Tutorials
+
+0. If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, [Configure your Azure Machine Learning Workspace](../../../configuration.ipynb)
+
+#### Obtain pretrained models from the [ONNX Model Zoo](https://github.com/onnx/models) and deploy with ONNX Runtime
+1. [MNIST - Handwritten Digit Classification with ONNX Runtime](onnx-inference-mnist-deploy.ipynb)
+2. [Emotion FER+ - Facial Expression Recognition with ONNX Runtime](onnx-inference-facial-expression-recognition-deploy.ipynb)
+
+#### Train model on Azure ML, convert to ONNX, and deploy with ONNX Runtime
+3. [MNIST - Train using PyTorch and deploy with ONNX Runtime](onnx-train-pytorch-aml-deploy-mnist.ipynb)
+
+#### Demo Notebooks from Microsoft Ignite 2018
+Note that the following notebooks do not have evaluation sections for the models since they were deployed as part of a live demo. You can find the respective pre-processing and post-processing code linked from the ONNX Model Zoo Github pages ([ResNet](https://github.com/onnx/models/tree/master/models/image_classification/resnet), [TinyYoloV2](https://github.com/onnx/models/tree/master/tiny_yolov2)), or experiment with the ONNX models by [running them in the browser](https://microsoft.github.io/onnxjs-demo/#/).
+
+4. [ResNet50 - Image Recognition with ONNX Runtime](onnx-modelzoo-aml-deploy-resnet50.ipynb)
+5. [TinyYoloV2 - Convert from CoreML and deploy with ONNX Runtime](onnx-convert-aml-deploy-tinyyolo.ipynb)
+
+## Documentation
+- [ONNX Runtime Python API Documentation](http://aka.ms/onnxruntime-python)
+- [Azure Machine Learning API Documentation](http://aka.ms/aml-docs)
+
+## Related Articles
+- [Building and Deploying ONNX Runtime Models](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-build-deploy-onnx)
+- [Azure AI – Making AI Real for Business](https://aka.ms/aml-blog-overview)
+- [What’s new in Azure Machine Learning](https://aka.ms/aml-blog-whats-new)
+
+## License
+Copyright (c) Microsoft Corporation. All rights reserved.  
+Licensed under the MIT License.
+
+## Acknowledgements
+These tutorials were developed by Vinitra Swamy and Prasanth Pulavarthi of the Microsoft AI Frameworks team and adapted for presentation at Microsoft Ignite 2018.
+
+
+ ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/README.png)

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

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

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

@@ -0,0 +1,434 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# YOLO Real-time Object Detection using ONNX on AzureML\n",
+    "\n",
+    "This example shows how to convert the TinyYOLO model from CoreML to ONNX and operationalize it as a web service using Azure Machine Learning services and the ONNX Runtime.\n",
+    "\n",
+    "## What is ONNX\n",
+    "ONNX is an open format for representing machine learning and deep learning models. ONNX enables open and interoperable AI by enabling data scientists and developers to use the tools of their choice without worrying about lock-in and flexibility to deploy to a variety of platforms. ONNX is developed and supported by a community of partners including Microsoft, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai).\n",
+    "\n",
+    "## YOLO Details\n",
+    "You Only Look Once (YOLO) is a state-of-the-art, real-time object detection system. For more information about YOLO, please visit the [YOLO website](https://pjreddie.com/darknet/yolo/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "\n",
+    "To make the best use of your time, make sure you have done the following:\n",
+    "\n",
+    "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
+    "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook to:\n",
+    "    * install the AML SDK\n",
+    "    * create a workspace and its configuration file (config.json)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check core SDK version number\n",
+    "import azureml.core\n",
+    "\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Install necessary packages\n",
+    "\n",
+    "You'll need to run the following commands to use this tutorial:\n",
+    "\n",
+    "```sh\n",
+    "pip install onnxmltools\n",
+    "pip install coremltools  # use this on Linux and Mac\n",
+    "pip install git+https://github.com/apple/coremltools  # use this on Windows\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Convert model to ONNX\n",
+    "\n",
+    "First we download the CoreML model. We use the CoreML model from [Matthijs Hollemans's tutorial](https://github.com/hollance/YOLO-CoreML-MPSNNGraph). This may take a few minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import urllib.request\n",
+    "\n",
+    "coreml_model_url = \"https://github.com/hollance/YOLO-CoreML-MPSNNGraph/raw/master/TinyYOLO-CoreML/TinyYOLO-CoreML/TinyYOLO.mlmodel\"\n",
+    "urllib.request.urlretrieve(coreml_model_url, filename=\"TinyYOLO.mlmodel\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we use ONNXMLTools to convert the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import onnxmltools\n",
+    "import coremltools\n",
+    "\n",
+    "# Load a CoreML model\n",
+    "coreml_model = coremltools.utils.load_spec('TinyYOLO.mlmodel')\n",
+    "\n",
+    "# Convert from CoreML into ONNX\n",
+    "onnx_model = onnxmltools.convert_coreml(coreml_model, 'TinyYOLOv2')\n",
+    "\n",
+    "# Fix the preprocessor bias in the ImageScaler\n",
+    "for init in onnx_model.graph.initializer:\n",
+    "    if init.name == 'scalerPreprocessor_bias':\n",
+    "        init.dims[1] = 1\n",
+    "\n",
+    "# Save ONNX model\n",
+    "onnxmltools.utils.save_model(onnx_model, 'tinyyolov2.onnx')\n",
+    "\n",
+    "import os\n",
+    "print(os.path.getsize('tinyyolov2.onnx'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deploying as a web service with Azure ML\n",
+    "\n",
+    "### Load Azure ML workspace\n",
+    "\n",
+    "We begin by instantiating a workspace object from the existing workspace created earlier in the configuration notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.location, ws.resource_group, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Registering your model with Azure ML\n",
+    "\n",
+    "Now we upload the model and register it in the workspace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.model import Model\n",
+    "\n",
+    "model = Model.register(model_path = \"tinyyolov2.onnx\",\n",
+    "                       model_name = \"tinyyolov2\",\n",
+    "                       tags = {\"onnx\": \"demo\"},\n",
+    "                       description = \"TinyYOLO\",\n",
+    "                       workspace = ws)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Displaying your registered models\n",
+    "\n",
+    "You can optionally list out all the models that you have registered in this workspace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models = ws.models\n",
+    "for name, m in models.items():\n",
+    "    print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Write scoring file\n",
+    "\n",
+    "We are now going to deploy our ONNX model on Azure ML using the ONNX Runtime. We begin by writing a score.py file that will be invoked by the web service call. The `init()` function is called once when the container is started so we load the model using the ONNX Runtime into a global session object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile score.py\n",
+    "import json\n",
+    "import time\n",
+    "import sys\n",
+    "import os\n",
+    "from azureml.core.model import Model\n",
+    "import numpy as np    # we're going to use numpy to process input and output data\n",
+    "import onnxruntime    # to inference ONNX models, we use the ONNX Runtime\n",
+    "\n",
+    "def init():\n",
+    "    global session\n",
+    "    model = Model.get_model_path(model_name = 'tinyyolov2')\n",
+    "    session = onnxruntime.InferenceSession(model)\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",
+    "    return np.array(result).tolist()\n",
+    "\n",
+    "def run(input_data_json):\n",
+    "    try:\n",
+    "        start = time.time()   # start timer\n",
+    "        input_data = preprocess(input_data_json)\n",
+    "        input_name = session.get_inputs()[0].name  # get the id of the first input of the model   \n",
+    "        result = session.run([], {input_name: input_data})\n",
+    "        end = time.time()     # stop timer\n",
+    "        return {\"result\": postprocess(result),\n",
+    "                \"time\": end - start}\n",
+    "    except Exception as e:\n",
+    "        result = str(e)\n",
+    "        return {\"error\": result}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting up inference configuration\n",
+    "First we create a YAML file that specifies which dependencies we would like to see in our container. Please note that you must include azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.conda_dependencies import CondaDependencies \n",
+    "\n",
+    "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n",
+    "\n",
+    "with open(\"myenv.yml\",\"w\") as f:\n",
+    "    f.write(myenv.serialize_to_string())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we create the inference configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.model import InferenceConfig\n",
+    "from azureml.core.environment import Environment\n",
+    "\n",
+    "\n",
+    "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
+    "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deploy the model"
+   ]
+  },
+  {
+   "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 = {'demo': 'onnx'}, \n",
+    "                                               description = 'web service for TinyYOLO ONNX model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following cell will take a few minutes to run as the model gets packaged up and deployed to ACI."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aci_service_name = 'my-aci-service-tiny-yolo'\n",
+    "print(\"Service\", aci_service_name)\n",
+    "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
+    "aci_service.wait_for_deployment(True)\n",
+    "print(aci_service.state)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In case the deployment fails, you can check the logs. Make sure to delete your aci_service before trying again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if aci_service.state != 'Healthy':\n",
+    "    # run this command for debugging.\n",
+    "    print(aci_service.get_logs())\n",
+    "    aci_service.delete()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Success!\n",
+    "\n",
+    "If you've made it this far, you've deployed a working web service that does object detection using an ONNX model. You can get the URL for the webservice with the code below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(aci_service.scoring_uri)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you are eventually done using the web service, remember to delete it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aci_service.delete()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "viswamy"
+   }
+  ],
+  "category": "deployment",
+  "compute": [
+   "local"
+  ],
+  "datasets": [
+   "PASCAL VOC"
+  ],
+  "deployment": [
+   "Azure Container Instance"
+  ],
+  "exclude_from_index": false,
+  "framework": [
+   "ONNX"
+  ],
+  "friendly_name": "Convert and deploy TinyYolo with ONNX Runtime",
+  "index_order": 5,
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  },
+  "star_tag": [
+   "featured"
+  ],
+  "tags": [
+   "ONNX Converter"
+  ],
+  "task": "Object Detection"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

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

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

@@ -12,7 +12,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Facial Expression Recognition using ONNX Runtime on AzureML\n",
+    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Facial Expression Recognition (FER+) using ONNX Runtime on Azure ML\n",
     "\n",
     "This example shows how to deploy an image classification neural network using the Facial Expression Recognition ([FER](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data)) dataset and Open Neural Network eXchange format ([ONNX](http://aka.ms/onnxdocarticle)) on the Azure Machine Learning platform. This tutorial will show you how to deploy a FER+ model from the [ONNX model zoo](https://github.com/onnx/models), use it to make predictions using ONNX Runtime Inference, and deploy it as a web service in Azure.\n",
     "\n",
@@ -34,32 +41,54 @@
     "## Prerequisites\n",
     "\n",
     "### 1. Install Azure ML SDK and create a new workspace\n",
-    "Please follow [00.configuration.ipynb](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) notebook.\n",
-    "\n",
+    "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, please follow [Azure ML configuration notebook](../../../configuration.ipynb) to set up your environment.\n",
     "\n",
     "### 2. Install additional packages needed for this Notebook\n",
-    "You need to install the popular plotting library `matplotlib`, the image manipulation library `PIL`, and the `onnx` library in the conda environment where Azure Maching Learning SDK is installed.\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",
     "\n",
     "```sh\n",
-    "(myenv) $ pip install matplotlib onnx Pillow\n",
+    "(myenv) $ pip install matplotlib onnx opencv-python\n",
     "```\n",
     "\n",
+    "**Debugging tip**: Make sure that to activate your virtual environment (myenv) before you re-launch this notebook using the `jupyter notebook` comand. Choose the respective Python kernel for your new virtual environment using the `Kernel > Change Kernel` menu above. If you have completed the steps correctly, the upper right corner of your screen should state `Python [conda env:myenv]` instead of `Python [default]`.\n",
+    "\n",
     "### 3. Download sample data and pre-trained ONNX model from ONNX Model Zoo.\n",
     "\n",
-    "[Download the ONNX Emotion FER+ model and corresponding test data](https://www.cntk.ai/OnnxModels/emotion_ferplus/opset_7/emotion_ferplus.tar.gz) and place them in the same folder as this tutorial notebook. You can unzip the file through the following line of code.\n",
+    "In the following lines of code, we download [the trained ONNX Emotion FER+ model and corresponding test data](https://github.com/onnx/models/tree/master/vision/body_analysis/emotion_ferplus) and place them in the same folder as this tutorial notebook. For more information about the FER+ dataset, please visit Microsoft Researcher Emad Barsoum's [FER+ source data repository](https://github.com/ebarsoum/FERPlus)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# urllib is a built-in Python library to download files from URLs\n",
     "\n",
-    "```sh\n",
-    "(myenv) $ tar xvzf emotion_ferplus.tar.gz\n",
-    "```\n",
+    "# Objective: retrieve the latest version of the ONNX Emotion FER+ model files from the\n",
+    "# ONNX Model Zoo and save it in the same folder as this tutorial\n",
     "\n",
-    "More information can be found about the ONNX FER+ model on [github](https://github.com/onnx/models/tree/master/emotion_ferplus). For more information about the FER+ dataset, please visit Microsoft Researcher Emad Barsoum's [FER+ source data repository](https://github.com/ebarsoum/FERPlus)."
+    "import urllib.request\n",
+    "\n",
+    "onnx_model_url = \"https://github.com/onnx/models/blob/main/vision/body_analysis/emotion_ferplus/model/emotion-ferplus-7.tar.gz?raw=true\"\n",
+    "\n",
+    "urllib.request.urlretrieve(onnx_model_url, filename=\"emotion-ferplus-7.tar.gz\")\n",
+    "\n",
+    "# the ! magic command tells our jupyter notebook kernel to run the following line of \n",
+    "# code from the command line instead of the notebook kernel\n",
+    "\n",
+    "# We use tar and xvcf to unzip the files we just retrieved from the ONNX model zoo\n",
+    "\n",
+    "!tar xvzf emotion-ferplus-7.tar.gz"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Load Azure ML workspace\n",
+    "## Deploy a VM with your ONNX model in the Cloud\n",
+    "\n",
+    "### Load Azure ML workspace\n",
     "\n",
     "We begin by instantiating a workspace object from the existing workspace created earlier in the configuration notebook."
    ]
@@ -136,9 +165,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "models = ws.models()\n",
-    "for m in models:\n",
-    "    print(\"Name:\", m.name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
+    "models = ws.models\n",
+    "for name, m in models.items():\n",
+    "    print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
    ]
   },
   {
@@ -147,9 +176,9 @@
    "source": [
     "### ONNX FER+ Model Methodology\n",
     "\n",
-    "The image classification model we are using is pre-trained using Microsoft's deep learning cognitive toolkit, [CNTK](https://github.com/Microsoft/CNTK), from the [ONNX model zoo](http://github.com/onnx/models). The model zoo has many other models that can be deployed on cloud providers like AzureML without any additional training. To ensure that our cloud deployed model works, we use testing data from the famous FER+ data set, provided as part of the [trained Emotion Recognition model](https://github.com/onnx/models/tree/master/emotion_ferplus) in the ONNX model zoo.\n",
+    "The image classification model we are using is pre-trained using Microsoft's deep learning cognitive toolkit, [CNTK](https://github.com/Microsoft/CNTK), from the [ONNX model zoo](http://github.com/onnx/models). The model zoo has many other models that can be deployed on cloud providers like AzureML without any additional training. To ensure that our cloud deployed model works, we use testing data from the well-known FER+ data set, provided as part of the [trained Emotion Recognition model](https://github.com/onnx/models/tree/master/vision/body_analysis/emotion_ferplus) in the ONNX model zoo.\n",
     "\n",
-    "The original Facial Emotion Recognition (FER) Dataset was released in 2013, but some of the labels are not entirely appropriate for the expression. In the FER+ Dataset, each photo was evaluated by at least 10 croud sourced reviewers, creating a better basis for ground truth. \n",
+    "The original Facial Emotion Recognition (FER) Dataset was released in 2013 by Pierre-Luc Carrier and Aaron Courville as part of a [Kaggle Competition](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data), but some of the labels are not entirely appropriate for the expression. In the FER+ Dataset, each photo was evaluated by at least 10 croud sourced reviewers, creating a more accurate basis for ground truth. \n",
     "\n",
     "You can see the difference of label quality in the sample model input below. The FER labels are the first word below each image, and the FER+ labels are the second word below each image.\n",
     "\n",
@@ -175,7 +204,6 @@
    "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"
@@ -202,20 +230,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Deploy our model on Azure ML"
+    "### Specify our Score and Environment Files"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We are now going to deploy our ONNX Model on AML with inference in ONNX Runtime. We begin by writing a score.py file, which will help us run the model in our Azure ML virtual machine (VM), and then specify our environment by writing a yml file.\n",
-    "\n",
-    "You will also notice that we import the onnxruntime library to do runtime inference on our ONNX models (passing in input and evaluating out model's predicted output). More information on the API and commands can be found in the [ONNX Runtime documentation](https://aka.ms/onnxruntime).\n",
+    "We are now going to deploy our ONNX Model on AML with inference in ONNX Runtime. We begin by writing a score.py file, which will help us run the model in our Azure ML virtual machine (VM), and then specify our environment by writing a yml file. You will also notice that we import the onnxruntime library to do runtime inference on our ONNX models (passing in input and evaluating out model's predicted output). More information on the API and commands can be found in the [ONNX Runtime documentation](https://aka.ms/onnxruntime).\n",
     "\n",
     "### Write Score File\n",
     "\n",
-    "A score file is what tells our Azure cloud service what to do. After initializing our model using azureml.core.model, we start an ONNX Runtime GPU inference session to evaluate the data passed in on our function calls."
+    "A score file is what tells our Azure cloud service what to do. After initializing our model using azureml.core.model, we start an ONNX Runtime inference session to evaluate the data passed in on our function calls."
    ]
   },
   {
@@ -230,12 +256,11 @@
     "import onnxruntime\n",
     "import sys\n",
     "import os\n",
-    "from azureml.core.model import Model\n",
     "import time\n",
     "\n",
     "def init():\n",
     "    global session, input_name, output_name\n",
-    "    model = Model.get_model_path(model_name = 'onnx_emotion')\n",
+    "    model = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'model.onnx')\n",
     "    session = onnxruntime.InferenceSession(model, None)\n",
     "    input_name = session.get_inputs()[0].name\n",
     "    output_name = session.get_outputs()[0].name \n",
@@ -248,10 +273,13 @@
     "    try:\n",
     "        # load in our data, convert to readable format\n",
     "        data = np.array(json.loads(input_data)['data']).astype('float32')\n",
+    "        \n",
     "        start = time.time()\n",
     "        r = session.run([output_name], {input_name : data})\n",
     "        end = time.time()\n",
+    "        \n",
     "        result = emotion_map(postprocess(r[0]))\n",
+    "        \n",
     "        result_dict = {\"result\": result,\n",
     "                      \"time_in_sec\": [end - start]}\n",
     "    except Exception as e:\n",
@@ -260,9 +288,12 @@
     "    return json.dumps(result_dict)\n",
     "\n",
     "def emotion_map(classes, N=1):\n",
-    "    \"\"\"Take the most probable labels (output of postprocess) and returns the top N emotional labels that fit the picture.\"\"\"\n",
+    "    \"\"\"Take the most probable labels (output of postprocess) and returns the \n",
+    "    top N emotional labels that fit the picture.\"\"\"\n",
+    "    \n",
+    "    emotion_table = {'neutral':0, 'happiness':1, 'surprise':2, 'sadness':3, \n",
+    "                     'anger':4, 'disgust':5, 'fear':6, 'contempt':7}\n",
     "    \n",
-    "    emotion_table = {'neutral':0, 'happiness':1, 'surprise':2, 'sadness':3, 'anger':4, 'disgust':5, 'fear':6, 'contempt':7}\n",
     "    emotion_keys = list(emotion_table.keys())\n",
     "    emotions = []\n",
     "    for i in range(N):\n",
@@ -276,8 +307,8 @@
     "    return e_x / e_x.sum(axis=0)\n",
     "\n",
     "def postprocess(scores):\n",
-    "    \"\"\"This function takes the scores generated by the network and returns the class IDs in decreasing \n",
-    "    order of probability.\"\"\"\n",
+    "    \"\"\"This function takes the scores generated by the network and \n",
+    "    returns the class IDs in decreasing order of probability.\"\"\"\n",
     "    prob = softmax(scores)\n",
     "    prob = np.squeeze(prob)\n",
     "    classes = np.argsort(prob)[::-1]\n",
@@ -288,7 +319,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Write Environment File"
+    "### Write Environment File\n",
+    "Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service."
    ]
   },
   {
@@ -299,11 +331,8 @@
    "source": [
     "from azureml.core.conda_dependencies import CondaDependencies \n",
     "\n",
-    "myenv = CondaDependencies()\n",
-    "myenv.add_pip_package(\"numpy\")\n",
-    "myenv.add_pip_package(\"azureml-core\")\n",
-    "myenv.add_pip_package(\"onnxruntime\")\n",
     "\n",
+    "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n",
     "\n",
     "with open(\"myenv.yml\",\"w\") as f:\n",
     "    f.write(myenv.serialize_to_string())"
@@ -313,9 +342,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Create the Container Image\n",
-    "\n",
-    "This step will likely take a few minutes."
+    "### Setup inference configuration"
    ]
   },
   {
@@ -324,49 +351,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from azureml.core.image import ContainerImage\n",
-    "\n",
-    "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
-    "                                                  runtime = \"python\",\n",
-    "                                                  conda_file = \"myenv.yml\",\n",
-    "                                                  description = \"test\",\n",
-    "                                                  tags = {\"demo\": \"onnx\"})\n",
+    "from azureml.core.model import InferenceConfig\n",
+    "from azureml.core.environment import Environment\n",
     "\n",
     "\n",
-    "image = ContainerImage.create(name = \"onnxtest\",\n",
-    "                              # this is the model object\n",
-    "                              models = [model],\n",
-    "                              image_config = image_config,\n",
-    "                              workspace = ws)\n",
-    "\n",
-    "image.wait_for_creation(show_output = True)"
+    "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
+    "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Debugging\n",
-    "\n",
-    "In case you need to debug your code, the next line of code accesses the log file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(image.image_build_log_uri)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We're all set! Let's get our model chugging.\n",
-    "\n",
-    "## Deploy the container image"
+    "### Deploy the model"
    ]
   },
   {
@@ -383,33 +380,26 @@
     "                                               description = 'ONNX for emotion recognition model')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following cell will likely take a few minutes to run as well."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from azureml.core.webservice import Webservice\n",
-    "\n",
     "aci_service_name = 'onnx-demo-emotion'\n",
     "print(\"Service\", aci_service_name)\n",
-    "\n",
-    "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
-    "                                           image = image,\n",
-    "                                           name = aci_service_name,\n",
-    "                                           workspace = ws)\n",
-    "\n",
+    "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
     "aci_service.wait_for_deployment(True)\n",
     "print(aci_service.state)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following cell will likely take a few minutes to run as well."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -439,23 +429,56 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Testing and Evaluation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Useful Helper Functions\n",
+    "## Testing and Evaluation\n",
     "\n",
-    "We preprocess and postprocess our data (see score.py file) using the helper functions specified in the [ONNX FER+ Model page in the Model Zoo repository](https://github.com/onnx/models/tree/master/emotion_ferplus)."
+    "### Useful Helper Functions\n",
+    "\n",
+    "We preprocess and postprocess our data (see score.py file) using the helper functions specified in the [ONNX FER+ Model page in the Model Zoo repository](https://github.com/onnx/models/tree/master/vision/body_analysis/emotion_ferplus)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def emotion_map(classes, N=1):\n",
+    "    \"\"\"Take the most probable labels (output of postprocess) and returns the \n",
+    "    top N emotional labels that fit the picture.\"\"\"\n",
+    "    \n",
+    "    emotion_table = {'neutral':0, 'happiness':1, 'surprise':2, 'sadness':3, \n",
+    "                     'anger':4, 'disgust':5, 'fear':6, 'contempt':7}\n",
+    "    \n",
+    "    emotion_keys = list(emotion_table.keys())\n",
+    "    emotions = []\n",
+    "    for c in range(N):\n",
+    "        emotions.append(emotion_keys[classes[c]])\n",
+    "    return emotions\n",
+    "\n",
+    "def softmax(x):\n",
+    "    \"\"\"Compute softmax values (probabilities from 0 to 1) for each possible label.\"\"\"\n",
+    "    x = x.reshape(-1)\n",
+    "    e_x = np.exp(x - np.max(x))\n",
+    "    return e_x / e_x.sum(axis=0)\n",
+    "\n",
+    "def postprocess(scores):\n",
+    "    \"\"\"This function takes the scores generated by the network and \n",
+    "    returns the class IDs in decreasing order of probability.\"\"\"\n",
+    "    prob = softmax(scores)\n",
+    "    prob = np.squeeze(prob)\n",
+    "    classes = np.argsort(prob)[::-1]\n",
+    "    return classes"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Load Test Data"
+    "### Load Test Data\n",
+    "\n",
+    "These are already in your directory from your ONNX model download (from the model zoo).\n",
+    "\n",
+    "Notice that our Model Zoo files have a .pb extension. This is because they are [protobuf files (Protocol Buffers)](https://developers.google.com/protocol-buffers/docs/pythontutorial), so we need to read in our data through our ONNX TensorProto reader into a format we can work with, like numerical arrays."
    ]
   },
   {
@@ -475,17 +498,15 @@
     "import json\n",
     "import os\n",
     "\n",
-    "from score import emotion_map, softmax, postprocess\n",
-    "\n",
     "test_inputs = []\n",
     "test_outputs = []\n",
     "\n",
     "# 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",
@@ -499,7 +520,7 @@
     "        tensor.ParseFromString(f.read())\n",
     "    \n",
     "    output_data = numpy_helper.to_array(tensor)\n",
-    "    output_processed = emotion_map(postprocess(output_data))[0]\n",
+    "    output_processed = emotion_map(postprocess(output_data[0]))[0]\n",
     "    test_outputs.append(output_processed)"
    ]
   },
@@ -512,7 +533,7 @@
    },
    "source": [
     "### Show some sample images\n",
-    "We use `matplotlib` to plot 3 test images from the model zoo with their labels over them."
+    "We use `matplotlib` to plot 3 test images from the dataset."
    ]
   },
   {
@@ -532,7 +553,7 @@
     "    plt.axhline('')\n",
     "    plt.axvline('')\n",
     "    plt.text(x = 10, y = -10, s = test_outputs[test_image], fontsize = 18)\n",
-    "    plt.imshow(test_inputs[test_image].reshape(64, 64), cmap = plt.cm.Greys)\n",
+    "    plt.imshow(test_inputs[test_image].reshape(64, 64), cmap = plt.cm.gray)\n",
     "plt.show()"
    ]
   },
@@ -549,7 +570,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.figure(figsize = (16, 6), frameon=False)\n",
+    "plt.figure(figsize = (16, 6))\n",
     "plt.subplot(1, 8, 1)\n",
     "\n",
     "plt.text(x = 0, y = -30, s = \"True Label: \", fontsize = 13, color = 'black')\n",
@@ -571,7 +592,7 @@
     "        print(r['error'])\n",
     "        break\n",
     "        \n",
-    "    result = r['result'][0][0]\n",
+    "    result = r['result'][0]\n",
     "    time_ms = np.round(r['time_in_sec'][0] * 1000, 2)\n",
     "    \n",
     "    ground_truth = test_outputs[i]\n",
@@ -583,7 +604,7 @@
     "\n",
     "    # use different color for misclassified sample\n",
     "    font_color = 'red' if ground_truth != result else 'black'\n",
-    "    clr_map = plt.cm.gray if ground_truth != result else plt.cm.Greys\n",
+    "    clr_map = plt.cm.Greys if ground_truth != result else plt.cm.gray\n",
     "\n",
     "    # ground truth labels are in blue\n",
     "    plt.text(x = 10, y = -70, s = ground_truth, fontsize = 18, color = 'blue')\n",
@@ -611,15 +632,30 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from PIL import Image\n",
+    "# Preprocessing functions take your image and format it so it can be passed\n",
+    "# as input into our ONNX model\n",
     "\n",
-    "def preprocess(image_path):\n",
-    "    input_shape = (1, 1, 64, 64)\n",
-    "    img = Image.open(image_path)\n",
-    "    img = img.resize((64, 64), Image.ANTIALIAS)\n",
-    "    img_data = np.array(img)\n",
-    "    img_data = np.resize(img_data, input_shape)\n",
-    "    return img_data"
+    "import cv2\n",
+    "\n",
+    "def rgb2gray(rgb):\n",
+    "    \"\"\"Convert the input image into grayscale\"\"\"\n",
+    "    return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])\n",
+    "\n",
+    "def resize_img(img_to_resize):\n",
+    "    \"\"\"Resize image to FER+ model input dimensions\"\"\"\n",
+    "    r_img = cv2.resize(img_to_resize, dsize=(64, 64), interpolation=cv2.INTER_AREA)\n",
+    "    r_img.resize((1, 1, 64, 64))\n",
+    "    return r_img\n",
+    "\n",
+    "def preprocess(img_to_preprocess):\n",
+    "    \"\"\"Resize input images and convert them to grayscale.\"\"\"\n",
+    "    if img_to_preprocess.shape == (64, 64):\n",
+    "        img_to_preprocess.resize((1, 1, 64, 64))\n",
+    "        return img_to_preprocess\n",
+    "    \n",
+    "    grayscale = rgb2gray(img_to_preprocess)\n",
+    "    processed_img = resize_img(grayscale)\n",
+    "    return processed_img"
    ]
   },
   {
@@ -634,14 +670,19 @@
     "# Any PNG or JPG image file should work\n",
     "# Make sure to include the entire path with // instead of /\n",
     "\n",
-    "# e.g. your_test_image = \"C://Users//vinitra.swamy//Pictures//emotion_test_images//img_1.png\"\n",
+    "# e.g. your_test_image = \"C:/Users/vinitra.swamy/Pictures/face.png\"\n",
     "\n",
     "your_test_image = \"<path to file>\"\n",
     "\n",
+    "import matplotlib.image as mpimg\n",
+    "\n",
     "if your_test_image != \"<path to file>\":\n",
-    "    img = preprocess(your_test_image)\n",
+    "    img = mpimg.imread(your_test_image)\n",
     "    plt.subplot(1,3,1)\n",
-    "    plt.imshow(img.reshape((64,64)), cmap = plt.cm.gray)\n",
+    "    plt.imshow(img, cmap = plt.cm.Greys)\n",
+    "    print(\"Old Dimensions: \", img.shape)\n",
+    "    img = preprocess(img)\n",
+    "    print(\"New Dimensions: \", img.shape)\n",
     "else:\n",
     "    img = None"
    ]
@@ -659,21 +700,22 @@
     "\n",
     "    try:\n",
     "        r = json.loads(aci_service.run(input_data))\n",
-    "        result = r['result'][0][0]\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",
     "    plt.subplot(1,8,1)\n",
     "    plt.axhline('')\n",
     "    plt.axvline('')\n",
-    "    plt.text(x = -10, y = -35, s = \"Model prediction: \", fontsize = 14)\n",
-    "    plt.text(x = -10, y = -20, s = \"Inference time: \", fontsize = 14)\n",
-    "    plt.text(x = 100, y = -35, s = str(result), fontsize = 14)\n",
-    "    plt.text(x = 100, y = -20, s = str(time_ms) + \" ms\", fontsize = 14)\n",
-    "    plt.text(x = -10, y = -8, s = \"Input image: \", fontsize = 14)\n",
-    "    plt.imshow(img.reshape(64, 64), cmap = plt.cm.gray)    "
+    "    plt.text(x = -10, y = -40, s = \"Model prediction: \", fontsize = 14)\n",
+    "    plt.text(x = -10, y = -25, s = \"Inference time: \", fontsize = 14)\n",
+    "    plt.text(x = 100, y = -40, s = str(result), fontsize = 14)\n",
+    "    plt.text(x = 100, y = -25, s = str(time_ms) + \" ms\", fontsize = 14)\n",
+    "    plt.text(x = -10, y = -10, s = \"Model Input image: \", fontsize = 14)\n",
+    "    plt.imshow(img.reshape((64, 64)), cmap = plt.cm.gray)    \n",
+    "     "
    ]
   },
   {
@@ -684,7 +726,7 @@
    "source": [
     "# remember to delete your service after you are done using it!\n",
     "\n",
-    "# aci_service.delete()"
+    "aci_service.delete()"
    ]
   },
   {
@@ -701,17 +743,38 @@
     "- ensured that your deep learning model is working perfectly (in the cloud) on test data, and checked it against some of your own!\n",
     "\n",
     "Next steps:\n",
-    "- If you have not already, check out another interesting ONNX/AML application that lets you set up a state-of-the-art [handwritten image classification model (MNIST)](https://github.com/Azure/MachineLearningNotebooks/tree/master/onnx/onnx-inference-mnist.ipynb) in the cloud! This tutorial deploys a pre-trained ONNX Computer Vision model for handwritten digit classification in an Azure ML virtual machine.\n",
+    "- If you have not already, check out another interesting ONNX/AML application that lets you set up a state-of-the-art [handwritten image classification model (MNIST)](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb) in the cloud! This tutorial deploys a pre-trained ONNX Computer Vision model for handwritten digit classification in an Azure ML virtual machine.\n",
     "- Keep an eye out for an updated version of this tutorial that uses ONNX Runtime GPU.\n",
     "- Contribute to our [open source ONNX repository on github](http://github.com/onnx/onnx) and/or add to our [ONNX model zoo](http://github.com/onnx/models)"
    ]
   }
  ],
  "metadata": {
+  "authors": [
+   {
+    "name": "viswamy"
+   }
+  ],
+  "category": "deployment",
+  "compute": [
+   "Local"
+  ],
+  "datasets": [
+   "Emotion FER"
+  ],
+  "deployment": [
+   "Azure Container Instance"
+  ],
+  "exclude_from_index": false,
+  "framework": [
+   "ONNX"
+  ],
+  "friendly_name": "Deploy Facial Expression Recognition (FER+) with ONNX Runtime",
+  "index_order": 2,
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.6",
    "language": "python",
-   "name": "python3"
+   "name": "python36"
   },
   "language_info": {
    "codemirror_mode": {
@@ -725,7 +788,12 @@
    "pygments_lexer": "ipython3",
    "version": "3.6.5"
   },
-  "msauthor": "vinitra.swamy"
+  "msauthor": "vinitra.swamy",
+  "star_tag": [],
+  "tags": [
+   "ONNX Model Zoo"
+  ],
+  "task": "Facial Expression Recognition"
  },
  "nbformat": 4,
  "nbformat_minor": 2

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

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

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

@@ -12,7 +12,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Handwritten Digit Classification (MNIST) using ONNX Runtime on AzureML\n",
+    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Handwritten Digit Classification (MNIST) using ONNX Runtime on Azure ML\n",
     "\n",
     "This example shows how to deploy an image classification neural network using the Modified National Institute of Standards and Technology ([MNIST](http://yann.lecun.com/exdb/mnist/)) dataset and Open Neural Network eXchange format ([ONNX](http://aka.ms/onnxdocarticle)) on the Azure Machine Learning platform. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing number from 0 to 9. This tutorial will show you how to deploy a MNIST model from the [ONNX model zoo](https://github.com/onnx/models), use it to make predictions using ONNX Runtime Inference, and deploy it as a web service in Azure.\n",
     "\n",
@@ -22,9 +29,9 @@
     "\n",
     "#### Tutorial Objectives:\n",
     "\n",
-    "1. Describe the MNIST dataset and pretrained Convolutional Neural Net ONNX model, stored in the ONNX model zoo.\n",
-    "2. Deploy and run the pretrained MNIST ONNX model on an Azure Machine Learning instance\n",
-    "3. Predict labels for test set data points in the cloud using ONNX Runtime and Azure ML"
+    "- Describe the MNIST dataset and pretrained Convolutional Neural Net ONNX model, stored in the ONNX model zoo.\n",
+    "- Deploy and run the pretrained MNIST ONNX model on an Azure Machine Learning instance\n",
+    "- Predict labels for test set data points in the cloud using ONNX Runtime and Azure ML"
    ]
   },
   {
@@ -34,31 +41,61 @@
     "## Prerequisites\n",
     "\n",
     "### 1. Install Azure ML SDK and create a new workspace\n",
-    "Please follow [00.configuration.ipynb](https://github.com/Azure/MachineLearningNotebooks/blob/master/00.configuration.ipynb) notebook.\n",
+    "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, please follow [Azure ML configuration notebook](../../../configuration.ipynb) to set up your environment.\n",
     "\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",
+    "### 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",
     "\n",
     "```sh\n",
     "(myenv) $ pip install matplotlib onnx opencv-python\n",
     "```\n",
     "\n",
+    "**Debugging tip**: Make sure that you run the \"jupyter notebook\" command to launch this notebook after activating your virtual environment. Choose the respective Python kernel for your new virtual environment using the `Kernel > Change Kernel` menu above. If you have completed the steps correctly, the upper right corner of your screen should state `Python [conda env:myenv]` instead of `Python [default]`.\n",
+    "\n",
     "### 3. Download sample data and pre-trained ONNX model from ONNX Model Zoo.\n",
     "\n",
-    "[Download the ONNX MNIST model and corresponding test data](https://www.cntk.ai/OnnxModels/mnist/opset_7/mnist.tar.gz) and place them in the same folder as this tutorial notebook. You can unzip the file through the following line of code.\n",
+    "In the following lines of code, we download [the trained ONNX MNIST model and corresponding test data](https://github.com/onnx/models/tree/master/vision/classification/mnist) and place them in the same folder as this tutorial notebook. For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# urllib is a built-in Python library to download files from URLs\n",
     "\n",
-    "```sh\n",
-    "(myenv) $ tar xvzf mnist.tar.gz\n",
-    "```\n",
+    "# Objective: retrieve the latest version of the ONNX MNIST model files from the\n",
+    "# ONNX Model Zoo and save it in the same folder as this tutorial\n",
     "\n",
-    "More information can be found about the ONNX MNIST model on [github](https://github.com/onnx/models/tree/master/mnist). For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/)."
+    "import urllib.request\n",
+    "\n",
+    "onnx_model_url = \"https://github.com/onnx/models/blob/main/vision/classification/mnist/model/mnist-7.tar.gz?raw=true\"\n",
+    "\n",
+    "urllib.request.urlretrieve(onnx_model_url, filename=\"mnist-7.tar.gz\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the ! magic command tells our jupyter notebook kernel to run the following line of \n",
+    "# code from the command line instead of the notebook kernel\n",
+    "\n",
+    "# We use tar and xvcf to unzip the files we just retrieved from the ONNX model zoo\n",
+    "\n",
+    "!tar xvzf mnist-7.tar.gz"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Load Azure ML workspace\n",
+    "## Deploy a VM with your ONNX model in the Cloud\n",
+    "\n",
+    "### Load Azure ML workspace\n",
     "\n",
     "We begin by instantiating a workspace object from the existing workspace created earlier in the configuration notebook."
    ]
@@ -113,11 +150,11 @@
    "source": [
     "from azureml.core.model import Model\n",
     "\n",
-    "model = Model.register(model_path = model_dir + \"//model.onnx\",\n",
+    "model = Model.register(workspace = ws,\n",
+    "                       model_path = model_dir + \"/\" + \"model.onnx\",\n",
     "                       model_name = \"mnist_1\",\n",
     "                       tags = {\"onnx\": \"demo\"},\n",
-    "                       description = \"MNIST image classification CNN from ONNX Model Zoo\",\n",
-    "                       workspace = ws)"
+    "                       description = \"MNIST image classification CNN from ONNX Model Zoo\",)"
    ]
   },
   {
@@ -135,9 +172,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "models = ws.models()\n",
-    "for m in models:\n",
-    "    print(\"Name:\", m.name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
+    "models = ws.models\n",
+    "for name, m in models.items():\n",
+    "    print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
    ]
   },
   {
@@ -150,7 +187,7 @@
    "source": [
     "### ONNX MNIST Model Methodology\n",
     "\n",
-    "The image classification model we are using is pre-trained using Microsoft's deep learning cognitive toolkit, [CNTK](https://github.com/Microsoft/CNTK), from the [ONNX model zoo](http://github.com/onnx/models). The model zoo has many other models that can be deployed on cloud providers like AzureML without any additional training. To ensure that our cloud deployed model works, we use testing data from the famous MNIST data set, provided as part of the [trained MNIST model](https://github.com/onnx/models/tree/master/mnist) in the ONNX model zoo.\n",
+    "The image classification model we are using is pre-trained using Microsoft's deep learning cognitive toolkit, [CNTK](https://github.com/Microsoft/CNTK), from the [ONNX model zoo](http://github.com/onnx/models). The model zoo has many other models that can be deployed on cloud providers like AzureML without any additional training. To ensure that our cloud deployed model works, we use testing data from the famous MNIST data set, provided as part of the [trained MNIST model](https://github.com/onnx/models/tree/master/vision/classification/mnist) in the ONNX model zoo.\n",
     "\n",
     "***Input: Handwritten Images from MNIST Dataset***\n",
     "\n",
@@ -188,16 +225,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Deploy our model on Azure ML"
+    "### Specify our Score and Environment Files"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We are now going to deploy our ONNX Model on AML with inference in ONNX Runtime. We begin by writing a score.py file, which will help us run the model in our Azure ML virtual machine (VM), and then specify our environment by writing a yml file.\n",
-    "\n",
-    "You will also notice that we import the onnxruntime library to do runtime inference on our ONNX models (passing in input and evaluating out model's predicted output). More information on the API and commands can be found in the [ONNX Runtime documentation](https://aka.ms/onnxruntime).\n",
+    "We are now going to deploy our ONNX Model on AML with inference in ONNX Runtime. We begin by writing a score.py file, which will help us run the model in our Azure ML virtual machine (VM), and then specify our environment by writing a yml file. You will also notice that we import the onnxruntime library to do runtime inference on our ONNX models (passing in input and evaluating out model's predicted output). More information on the API and commands can be found in the [ONNX Runtime documentation](https://aka.ms/onnxruntime).\n",
     "\n",
     "### Write Score File\n",
     "\n",
@@ -216,39 +251,52 @@
     "import onnxruntime\n",
     "import sys\n",
     "import os\n",
-    "from azureml.core.model import Model\n",
     "import time\n",
     "\n",
     "\n",
     "def init():\n",
     "    global session, input_name, output_name\n",
-    "    model = Model.get_model_path(model_name = 'mnist_1')\n",
+    "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
+    "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
+    "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
+    "    model = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'model.onnx')\n",
     "    session = onnxruntime.InferenceSession(model, None)\n",
     "    input_name = session.get_inputs()[0].name\n",
     "    output_name = session.get_outputs()[0].name \n",
     "    \n",
+    "\n",
+    "def preprocess(input_data_json):\n",
+    "    # convert the JSON data into the tensor input\n",
+    "    return np.array(json.loads(input_data_json)['data']).astype('float32')\n",
+    "\n",
+    "def postprocess(result):\n",
+    "    # We use argmax to pick the highest confidence label\n",
+    "    return int(np.argmax(np.array(result).squeeze(), axis=0))\n",
+    "    \n",
     "def run(input_data):\n",
-    "    '''Purpose: evaluate test input in Azure Cloud using onnxruntime.\n",
-    "        We will call the run function later from our Jupyter Notebook \n",
-    "        so our azure service can evaluate our model input in the cloud. '''\n",
     "\n",
     "    try:\n",
     "        # load in our data, convert to readable format\n",
-    "        data = np.array(json.loads(input_data)['data']).astype('float32')\n",
-    "\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, after calling softmax on the 10 numbers we receive\"\"\"\n",
+    "    \"\"\"We use argmax to determine the right label to choose from our output\"\"\"\n",
     "    return int(np.argmax(result_prob, axis=0))"
    ]
   },
@@ -256,14 +304,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Write Environment File"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This step creates a YAML file that specifies which dependencies we would like to see in our Linux Virtual Machine."
+    "### Write Environment File\n",
+    "\n",
+    "This step creates a YAML environment file that specifies which dependencies we would like to see in our Linux Virtual Machine. Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service."
    ]
   },
   {
@@ -274,11 +317,7 @@
    "source": [
     "from azureml.core.conda_dependencies import CondaDependencies \n",
     "\n",
-    "myenv = CondaDependencies()\n",
-    "myenv.add_pip_package(\"numpy\")\n",
-    "myenv.add_pip_package(\"azureml-core\")\n",
-    "myenv.add_pip_package(\"onnxruntime\")\n",
-    "\n",
+    "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n",
     "\n",
     "with open(\"myenv.yml\",\"w\") as f:\n",
     "    f.write(myenv.serialize_to_string())"
@@ -288,9 +327,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Create the Container Image\n",
-    "\n",
-    "This step will likely take a few minutes."
+    "### Create Inference Configuration"
    ]
   },
   {
@@ -299,49 +336,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from azureml.core.image import ContainerImage\n",
-    "\n",
-    "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
-    "                                                  runtime = \"python\",\n",
-    "                                                  conda_file = \"myenv.yml\",\n",
-    "                                                  description = \"test\",\n",
-    "                                                  tags = {\"demo\": \"onnx\"})                                                 )\n",
+    "from azureml.core.model import InferenceConfig\n",
+    "from azureml.core.environment import Environment\n",
     "\n",
     "\n",
-    "image = ContainerImage.create(name = \"onnxtest\",\n",
-    "                              # this is the model object\n",
-    "                              models = [model],\n",
-    "                              image_config = image_config,\n",
-    "                              workspace = ws)\n",
-    "\n",
-    "image.wait_for_creation(show_output = True)"
+    "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
+    "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Debugging\n",
-    "\n",
-    "In case you need to debug your code, the next line of code accesses the log file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(image.image_build_log_uri)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We're all set! Let's get our model chugging.\n",
-    "\n",
-    "## Deploy the container image"
+    "### Deploy the model"
    ]
   },
   {
@@ -362,7 +369,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The following cell will likely take a few minutes to run as well."
+    "The following cell will likely take a few minutes to run."
    ]
   },
   {
@@ -371,16 +378,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from azureml.core.webservice import Webservice\n",
-    "\n",
     "aci_service_name = 'onnx-demo-mnist'\n",
     "print(\"Service\", aci_service_name)\n",
-    "\n",
-    "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
-    "                                           image = image,\n",
-    "                                           name = aci_service_name,\n",
-    "                                           workspace = ws)\n",
-    "\n",
+    "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
     "aci_service.wait_for_deployment(True)\n",
     "print(aci_service.state)"
    ]
@@ -407,23 +407,29 @@
     "\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)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Testing and Evaluation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load Test Data\n",
+    "## Testing and Evaluation\n",
     "\n",
-    "These are already in your directory from your ONNX model download (from the model zoo). If you didn't place your model and test data in the same directory as this notebook, edit the \"model_dir\" filename below."
+    "### Load Test Data\n",
+    "\n",
+    "These are already in your directory from your ONNX model download (from the model zoo).\n",
+    "\n",
+    "Notice that our Model Zoo files have a .pb extension. This is because they are [protobuf files (Protocol Buffers)](https://developers.google.com/protocol-buffers/docs/pythontutorial), so we need to read in our data through our ONNX TensorProto reader into a format we can work with, like numerical arrays."
    ]
   },
   {
@@ -515,7 +521,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.figure(figsize = (16, 6), frameon=False)\n",
+    "plt.figure(figsize = (16, 6))\n",
     "plt.subplot(1, 8, 1)\n",
     "\n",
     "plt.text(x = 0, y = -30, s = \"True Label: \", fontsize = 13, color = 'black')\n",
@@ -531,14 +537,14 @@
     "    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",
@@ -579,21 +585,28 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Preprocessing functions\n",
+    "# Preprocessing functions take your image and format it so it can be passed\n",
+    "# as input into our ONNX model\n",
+    "\n",
     "import cv2\n",
     "\n",
     "def rgb2gray(rgb):\n",
     "    \"\"\"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",
-    "    img = cv2.resize(img, dsize=(28, 28), interpolation=cv2.INTER_AREA)\n",
-    "    img.resize((1, 1, 28, 28))\n",
-    "    return img\n",
+    "def resize_img(img_to_resize):\n",
+    "    \"\"\"Resize image to MNIST model input dimensions\"\"\"\n",
+    "    r_img = cv2.resize(img_to_resize, dsize=(28, 28), interpolation=cv2.INTER_AREA)\n",
+    "    r_img.resize((1, 1, 28, 28))\n",
+    "    return r_img\n",
     "\n",
-    "def preprocess(img):\n",
+    "def preprocess(img_to_preprocess):\n",
     "    \"\"\"Resize input images and convert them to grayscale.\"\"\"\n",
-    "    grayscale = rgb2gray(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_to_preprocess)\n",
     "    processed_img = resize_img(grayscale)\n",
     "    return processed_img"
    ]
@@ -608,12 +621,11 @@
     "# Make sure your image is square and the dimensions are equal (i.e. 100 * 100 pixels or 28 * 28 pixels)\n",
     "\n",
     "# Any PNG or JPG image file should work\n",
-    "# Make sure to include the entire path with // instead of /\n",
-    "\n",
-    "# e.g. your_test_image = \"C://Users//vinitra.swamy//Pictures//digit.png\"\n",
     "\n",
     "your_test_image = \"<path to file>\"\n",
     "\n",
+    "# e.g. your_test_image = \"C:/Users/vinitra.swamy/Pictures/handwritten_digit.png\"\n",
+    "\n",
     "import matplotlib.image as mpimg\n",
     "\n",
     "if your_test_image != \"<path to file>\":\n",
@@ -639,10 +651,10 @@
     "    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",
@@ -672,18 +684,7 @@
     "\n",
     "A convolution layer is a set of filters. Each filter is defined by a weight (**W**) matrix, and  bias ($b$).\n",
     "\n",
-    "![](https://www.cntk.ai/jup/cntk103d_filterset_v2.png)\n",
-    "\n",
-    "These filters are scanned across the image performing the dot product between the weights and corresponding input value ($x$). The bias value is added to the output of the dot product and the resulting sum is optionally mapped through an activation function. This process is illustrated in the following animation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Image(url=\"https://www.cntk.ai/jup/cntk103d_conv2d_final.gif\", width= 200)"
+    "These filters are scanned across the image performing the dot product between the weights and corresponding input value ($x$). The bias value is added to the output of the dot product and the resulting sum is optionally mapped through an activation function."
    ]
   },
   {
@@ -695,24 +696,6 @@
     "The MNIST model from the ONNX Model Zoo uses maxpooling to update the weights in its convolutions, summarized by the graphic below. You can see the entire workflow of our pre-trained model in the following image, with our input images and our output probabilities of each of our 10 labels. If you're interested in exploring the logic behind creating a Deep Learning model further, please look at the [training tutorial for our ONNX MNIST Convolutional Neural Network](https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_103D_MNIST_ConvolutionalNeuralNetwork.ipynb). "
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Max-Pooling for Convolutional Neural Nets\n",
-    "\n",
-    "![](http://www.cntk.ai/jup/c103d_max_pooling.gif)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Pre-Trained Model Architecture\n",
-    "\n",
-    "![](http://www.cntk.ai/jup/conv103d_mnist-conv-mp.png)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -721,7 +704,7 @@
    "source": [
     "# remember to delete your service after you are done using it!\n",
     "\n",
-    "# aci_service.delete()"
+    "aci_service.delete()"
    ]
   },
   {
@@ -738,16 +721,37 @@
     "- ensured that your deep learning model is working perfectly (in the cloud) on test data, and checked it against some of your own!\n",
     "\n",
     "Next steps:\n",
-    "- Check out another interesting application based on a Microsoft Research computer vision paper that lets you set up a [facial emotion recognition model](https://github.com/Azure/MachineLearningNotebooks/tree/master/onnx/onnx-inference-emotion-recognition.ipynb) in the cloud! This tutorial deploys a pre-trained ONNX Computer Vision model in an Azure ML virtual machine with GPU support.\n",
+    "- Check out another interesting application based on a Microsoft Research computer vision paper that lets you set up a [facial emotion recognition model](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb) in the cloud! This tutorial deploys a pre-trained ONNX Computer Vision model in an Azure ML virtual machine.\n",
     "- Contribute to our [open source ONNX repository on github](http://github.com/onnx/onnx) and/or add to our [ONNX model zoo](http://github.com/onnx/models)"
    ]
   }
  ],
  "metadata": {
+  "authors": [
+   {
+    "name": "viswamy"
+   }
+  ],
+  "category": "deployment",
+  "compute": [
+   "Local"
+  ],
+  "datasets": [
+   "MNIST"
+  ],
+  "deployment": [
+   "Azure Container Instance"
+  ],
+  "exclude_from_index": false,
+  "framework": [
+   "ONNX"
+  ],
+  "friendly_name": "Deploy MNIST digit recognition with ONNX Runtime",
+  "index_order": 1,
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.6",
    "language": "python",
-   "name": "python3"
+   "name": "python36"
   },
   "language_info": {
    "codemirror_mode": {
@@ -761,7 +765,12 @@
    "pygments_lexer": "ipython3",
    "version": "3.6.5"
   },
-  "msauthor": "vinitra.swamy"
+  "msauthor": "vinitra.swamy",
+  "star_tag": [],
+  "tags": [
+   "ONNX Model Zoo"
+  ],
+  "task": "Image Classification"
  },
  "nbformat": 4,
  "nbformat_minor": 2

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

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

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

@@ -0,0 +1 @@
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how-to-use-azureml/deployment/onnx/onnx-model-register-and-deploy.ipynb

@@ -0,0 +1,228 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-model-register-and-deploy.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Register ONNX model and deploy as webservice\n",
+    "\n",
+    "Following this notebook, you will:\n",
+    "\n",
+    " - Learn how to register an ONNX in your Azure Machine Learning Workspace.\n",
+    " - Deploy your model as a web service in an Azure Container Instance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "\n",
+    "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration notebook](../../../configuration.ipynb) to install the Azure Machine Learning Python SDK and create a workspace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import azureml.core\n",
+    "\n",
+    "# Check core SDK version number.\n",
+    "print('SDK version:', azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize workspace\n",
+    "\n",
+    "Create a [Workspace](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace%28class%29?view=azure-ml-py) object from your persisted configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "create workspace"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Register model\n",
+    "\n",
+    "Register a file or folder as a model by calling [Model.register()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#register-workspace--model-path--model-name--tags-none--properties-none--description-none--datasets-none--model-framework-none--model-framework-version-none--child-paths-none-). For this example, we have provided a trained ONNX MNIST model(`mnist-model.onnx` in the notebook's directory).\n",
+    "\n",
+    "In addition to the content of the model file itself, your registered model will also store model metadata -- model description, tags, and framework information -- that will be useful when managing and deploying models in your workspace. Using tags, for instance, you can categorize your models and apply filters when listing models in your workspace. Also, marking this model with the scikit-learn framework will simplify deploying it as a web service, as we'll see later."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "register model from file"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from azureml.core import Model\n",
+    "\n",
+    "model = Model.register(workspace=ws,\n",
+    "                       model_name='mnist-sample',                  # Name of the registered model in your workspace.\n",
+    "                       model_path='mnist-model.onnx',              # Local ONNX model to upload and register as a model.\n",
+    "                       model_framework=Model.Framework.ONNX ,      # Framework used to create the model.\n",
+    "                       model_framework_version='1.3',              # Version of ONNX used to create the model.\n",
+    "                       description='Onnx MNIST model')\n",
+    "\n",
+    "print('Name:', model.name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deploy model\n",
+    "\n",
+    "Deploy your model as a web service using [Model.deploy()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#deploy-workspace--name--models--inference-config--deployment-config-none--deployment-target-none-). Web services take one or more models, load them in an environment, and run them on one of several supported deployment targets.\n",
+    "\n",
+    "For this example, we will deploy the ONNX model to an Azure Container Instance (ACI)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Use a default environment (for supported models)\n",
+    "\n",
+    "The Azure Machine Learning service provides a default environment for supported model frameworks, including ONNX, based on the metadata you provided when registering your model. This is the easiest way to deploy your model.\n",
+    "\n",
+    "**Note**: This step can take several minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Webservice\n",
+    "from azureml.exceptions import WebserviceException\n",
+    "\n",
+    "service_name = 'onnx-mnist-service'\n",
+    "\n",
+    "# Remove any existing service under the same name.\n",
+    "try:\n",
+    "    Webservice(ws, service_name).delete()\n",
+    "except WebserviceException:\n",
+    "    pass\n",
+    "\n",
+    "service = Model.deploy(ws, service_name, [model])\n",
+    "service.wait_for_deployment(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After your model is deployed, perform a call to the web service."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "\n",
+    "headers = {'Content-Type': 'application/json', 'Accept': 'application/json'}\n",
+    "\n",
+    "if service.auth_enabled:\n",
+    "    headers['Authorization'] = 'Bearer '+ service.get_keys()[0]\n",
+    "elif service.token_auth_enabled:\n",
+    "    headers['Authorization'] = 'Bearer '+ service.get_token()[0]\n",
+    "\n",
+    "scoring_uri = service.scoring_uri\n",
+    "print(scoring_uri)\n",
+    "with open('onnx-mnist-predict-input.json', 'rb') as data_file:\n",
+    "    response = requests.post(\n",
+    "        scoring_uri, data=data_file, headers=headers)\n",
+    "print(response.status_code)\n",
+    "print(response.elapsed)\n",
+    "print(response.json())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you are finished testing your service, clean up the deployment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "service.delete()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "vaidyas"
+   }
+  ],
+  "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"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -0,0 +1,4 @@
+name: onnx-model-register-and-deploy
+dependencies:
+- pip:
+  - azureml-sdk

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

@@ -0,0 +1,416 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ResNet50 Image Classification using ONNX and AzureML\n",
+    "\n",
+    "This example shows how to deploy the ResNet50 ONNX model as a web service using Azure Machine Learning services and the ONNX Runtime.\n",
+    "\n",
+    "## What is ONNX\n",
+    "ONNX is an open format for representing machine learning and deep learning models. ONNX enables open and interoperable AI by enabling data scientists and developers to use the tools of their choice without worrying about lock-in and flexibility to deploy to a variety of platforms. ONNX is developed and supported by a community of partners including Microsoft, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai).\n",
+    "\n",
+    "## ResNet50 Details\n",
+    "ResNet classifies the major object in an input image into a set of 1000 pre-defined classes. For more information about the ResNet50 model and how it was created can be found on the [ONNX Model Zoo github](https://github.com/onnx/models/tree/master/vision/classification/resnet). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "\n",
+    "To make the best use of your time, make sure you have done the following:\n",
+    "\n",
+    "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
+    "* If you are using an Azure Machine Learning Notebook VM, you are all set.  Otherwise, go through the [configuration notebook](../../../configuration.ipynb) to:\n",
+    "    * install the AML SDK\n",
+    "    * create a workspace and its configuration file (config.json)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check core SDK version number\n",
+    "import azureml.core\n",
+    "\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Download pre-trained ONNX model from ONNX Model Zoo.\n",
+    "\n",
+    "Download the [ResNet50v2 model and test data](https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.tar.gz) and extract it in the same folder as this tutorial notebook.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import urllib.request\n",
+    "\n",
+    "onnx_model_url = \"https://s3.amazonaws.com/onnx-model-zoo/resnet/resnet50v2/resnet50v2.tar.gz\"\n",
+    "urllib.request.urlretrieve(onnx_model_url, filename=\"resnet50v2.tar.gz\")\n",
+    "\n",
+    "!tar xvzf resnet50v2.tar.gz"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deploying as a web service with Azure ML"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load your Azure ML workspace\n",
+    "\n",
+    "We begin by instantiating a workspace object from the existing workspace created earlier in the configuration notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.location, ws.resource_group, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Register your model with Azure ML\n",
+    "\n",
+    "Now we upload the model and register it in the workspace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.model import Model\n",
+    "\n",
+    "model = Model.register(model_path = \"resnet50v2/resnet50v2.onnx\",\n",
+    "                       model_name = \"resnet50v2\",\n",
+    "                       tags = {\"onnx\": \"demo\"},\n",
+    "                       description = \"ResNet50v2 from ONNX Model Zoo\",\n",
+    "                       workspace = ws)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Displaying your registered models\n",
+    "\n",
+    "You can optionally list out all the models that you have registered in this workspace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models = ws.models\n",
+    "for name, m in models.items():\n",
+    "    print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Write scoring file\n",
+    "\n",
+    "We are now going to deploy our ONNX model on Azure ML using the ONNX Runtime. We begin by writing a score.py file that will be invoked by the web service call. The `init()` function is called once when the container is started so we load the model using the ONNX Runtime into a global session object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile score.py\n",
+    "import json\n",
+    "import time\n",
+    "import sys\n",
+    "import os\n",
+    "import numpy as np    # we're going to use numpy to process input and output data\n",
+    "import onnxruntime    # to inference ONNX models, we use the ONNX Runtime\n",
+    "\n",
+    "def softmax(x):\n",
+    "    x = x.reshape(-1)\n",
+    "    e_x = np.exp(x - np.max(x))\n",
+    "    return e_x / e_x.sum(axis=0)\n",
+    "\n",
+    "def init():\n",
+    "    global session\n",
+    "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
+    "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
+    "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
+    "    model = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'resnet50v2.onnx')\n",
+    "    session = onnxruntime.InferenceSession(model, None)\n",
+    "\n",
+    "def preprocess(input_data_json):\n",
+    "    # convert the JSON data into the tensor input\n",
+    "    img_data = np.array(json.loads(input_data_json)['data']).astype('float32')\n",
+    "    \n",
+    "    #normalize\n",
+    "    mean_vec = np.array([0.485, 0.456, 0.406])\n",
+    "    stddev_vec = np.array([0.229, 0.224, 0.225])\n",
+    "    norm_img_data = np.zeros(img_data.shape).astype('float32')\n",
+    "    for i in range(img_data.shape[0]):\n",
+    "        norm_img_data[i,:,:] = (img_data[i,:,:]/255 - mean_vec[i]) / stddev_vec[i]\n",
+    "\n",
+    "    return norm_img_data\n",
+    "\n",
+    "def postprocess(result):\n",
+    "    return softmax(np.array(result)).tolist()\n",
+    "\n",
+    "def run(input_data_json):\n",
+    "    try:\n",
+    "        start = time.time()\n",
+    "        # load in our data which is expected as NCHW 224x224 image\n",
+    "        input_data = preprocess(input_data_json)\n",
+    "        input_name = session.get_inputs()[0].name  # get the id of the first input of the model   \n",
+    "        result = session.run([], {input_name: input_data})\n",
+    "        end = time.time()     # stop timer\n",
+    "        return {\"result\": postprocess(result),\n",
+    "                \"time\": end - start}\n",
+    "    except Exception as e:\n",
+    "        result = str(e)\n",
+    "        return {\"error\": result}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create inference configuration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we create a YAML file that specifies which dependencies we would like to see in our container."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.conda_dependencies import CondaDependencies \n",
+    "\n",
+    "\n",
+    "myenv = CondaDependencies.create(pip_packages=[\"numpy\", \"onnxruntime\", \"azureml-core\", \"azureml-defaults\"])\n",
+    "\n",
+    "with open(\"myenv.yml\",\"w\") as f:\n",
+    "    f.write(myenv.serialize_to_string())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the inference configuration object. Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.model import InferenceConfig\n",
+    "from azureml.core.environment import Environment\n",
+    "\n",
+    "\n",
+    "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
+    "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deploy the model"
+   ]
+  },
+  {
+   "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 = {'demo': 'onnx'}, \n",
+    "                                               description = 'web service for ResNet50 ONNX model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following cell will likely take a few minutes to run as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from random import randint\n",
+    "\n",
+    "aci_service_name = 'onnx-demo-resnet50'+str(randint(0,100))\n",
+    "print(\"Service\", aci_service_name)\n",
+    "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
+    "aci_service.wait_for_deployment(True)\n",
+    "print(aci_service.state)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In case the deployment fails, you can check the logs. Make sure to delete your aci_service before trying again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if aci_service.state != 'Healthy':\n",
+    "    # run this command for debugging.\n",
+    "    print(aci_service.get_logs())\n",
+    "    aci_service.delete()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Success!\n",
+    "\n",
+    "If you've made it this far, you've deployed a working web service that does image classification using an ONNX model. You can get the URL for the webservice with the code below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(aci_service.scoring_uri)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you are eventually done using the web service, remember to delete it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aci_service.delete()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "viswamy"
+   }
+  ],
+  "category": "deployment",
+  "compute": [
+   "Local"
+  ],
+  "datasets": [
+   "ImageNet"
+  ],
+  "deployment": [
+   "Azure Container Instance"
+  ],
+  "exclude_from_index": false,
+  "framework": [
+   "ONNX"
+  ],
+  "friendly_name": "Deploy ResNet50 with ONNX Runtime",
+  "index_order": 4,
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  },
+  "star_tag": [],
+  "tags": [
+   "ONNX Model Zoo"
+  ],
+  "task": "Image Classification"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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

@@ -0,0 +1,4 @@
+name: onnx-modelzoo-aml-deploy-resnet50
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.yml

@@ -0,0 +1,5 @@
+name: onnx-train-pytorch-aml-deploy-mnist
+dependencies:
+- pip:
+  - azureml-sdk
+  - azureml-widgets

how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.ipynb

@@ -0,0 +1,350 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deploying a web service to Azure Kubernetes Service (AKS)\n",
+    "This notebook shows the steps for deploying a service: registering a model, creating an image, provisioning a cluster (one time action), and deploying a service to it. \n",
+    "We then test and delete the service, image and model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import azureml.core\n",
+    "print(azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Get workspace\n",
+    "Load existing workspace from the config file info."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.workspace import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Download the model\n",
+    "\n",
+    "Prior to registering the model, you should have a TensorFlow [Saved Model](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md) in the `resnet50` directory. This cell will download a [pretrained resnet50](http://download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v1_fp32_savedmodel_NCHW_jpg.tar.gz) and unpack it to that directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import requests\n",
+    "import shutil\n",
+    "import tarfile\n",
+    "import tempfile\n",
+    "\n",
+    "from io import BytesIO\n",
+    "\n",
+    "model_url = \"http://download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v1_fp32_savedmodel_NCHW_jpg.tar.gz\"\n",
+    "\n",
+    "archive_prefix = \"./resnet_v1_fp32_savedmodel_NCHW_jpg/1538686758/\"\n",
+    "target_folder = \"resnet50\"\n",
+    "\n",
+    "if not os.path.exists(target_folder):\n",
+    "    response = requests.get(model_url)\n",
+    "    archive = tarfile.open(fileobj=BytesIO(response.content))\n",
+    "    with tempfile.TemporaryDirectory() as temp_folder:\n",
+    "        archive.extractall(temp_folder)\n",
+    "        shutil.copytree(os.path.join(temp_folder, archive_prefix), target_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Register the model\n",
+    "Register an existing trained model, add description and tags."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.model import Model\n",
+    "\n",
+    "model = Model.register(model_path=\"resnet50\", # This points to the local directory to upload.\n",
+    "                       model_name=\"resnet50\", # This is the name the model is registered as.\n",
+    "                       tags={'area': \"Image classification\", 'type': \"classification\"},\n",
+    "                       description=\"Image classification trained on Imagenet Dataset\",\n",
+    "                       workspace=ws)\n",
+    "\n",
+    "print(model.name, model.description, model.version)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Provision the AKS Cluster\n",
+    "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import ComputeTarget, AksCompute\n",
+    "from azureml.core.compute_target import ComputeTargetException\n",
+    "\n",
+    "# Choose a name for your GPU cluster\n",
+    "gpu_cluster_name = \"aks-gpu-cluster\"\n",
+    "\n",
+    "# Verify that cluster does not exist already\n",
+    "try:\n",
+    "    gpu_cluster = ComputeTarget(workspace=ws, name=gpu_cluster_name)\n",
+    "    print(\"Found existing gpu cluster\")\n",
+    "except ComputeTargetException:\n",
+    "    print(\"Creating new gpu-cluster\")\n",
+    "    \n",
+    "    # Specify the configuration for the new cluster\n",
+    "    compute_config = AksCompute.provisioning_configuration(cluster_purpose=AksCompute.ClusterPurpose.DEV_TEST,\n",
+    "                                                           agent_count=1,\n",
+    "                                                           vm_size=\"Standard_NV6\")\n",
+    "    # Create the cluster with the specified name and configuration\n",
+    "    gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, compute_config)\n",
+    "\n",
+    "    # Wait for the cluster to complete, show the output log\n",
+    "    gpu_cluster.wait_for_completion(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deploy the model as a web service to AKS\n",
+    "\n",
+    "First create a scoring script"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile score.py\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "import json\n",
+    "import os\n",
+    "from azureml.contrib.services.aml_request import AMLRequest, rawhttp\n",
+    "from azureml.contrib.services.aml_response import AMLResponse\n",
+    "\n",
+    "def init():\n",
+    "    global session\n",
+    "    global input_name\n",
+    "    global output_name\n",
+    "    \n",
+    "    session = tf.Session()\n",
+    "\n",
+    "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
+    "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
+    "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
+    "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'resnet50')\n",
+    "    model = tf.saved_model.loader.load(session, ['serve'], model_path)\n",
+    "    if len(model.signature_def['serving_default'].inputs) > 1:\n",
+    "        raise ValueError(\"This score.py only supports one input\")\n",
+    "    input_name = [tensor.name for tensor in model.signature_def['serving_default'].inputs.values()][0]\n",
+    "    output_name = [tensor.name for tensor in model.signature_def['serving_default'].outputs.values()]\n",
+    "    \n",
+    "\n",
+    "@rawhttp\n",
+    "def run(request):\n",
+    "    if request.method == 'POST':\n",
+    "        reqBody = request.get_data(False)\n",
+    "        resp = score(reqBody)\n",
+    "        return AMLResponse(resp, 200)\n",
+    "    if request.method == 'GET':\n",
+    "        respBody = str.encode(\"GET is not supported\")\n",
+    "        return AMLResponse(respBody, 405)\n",
+    "    return AMLResponse(\"bad request\", 500)\n",
+    "\n",
+    "def score(data):\n",
+    "    result = session.run(output_name, {input_name: [data]})\n",
+    "    return json.dumps(result[1].tolist())\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    init()\n",
+    "    with open(\"test_image.jpg\", 'rb') as f:\n",
+    "        content = f.read()\n",
+    "        print(score(content))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now create the deployment configuration objects and deploy the model as a webservice."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set the web service configuration (using default here)\n",
+    "from azureml.core.model import InferenceConfig\n",
+    "from azureml.core.webservice import AksWebservice\n",
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "from azureml.core.environment import Environment, DEFAULT_GPU_IMAGE\n",
+    "\n",
+    "env = Environment('deploytocloudenv')\n",
+    "# Please see [Azure ML Containers repository](https://github.com/Azure/AzureML-Containers#featured-tags)\n",
+    "# for open-sourced GPU base images.\n",
+    "env.docker.base_image = DEFAULT_GPU_IMAGE\n",
+    "env.python.conda_dependencies = CondaDependencies.create(conda_packages=['tensorflow-gpu==1.12.0','numpy'],\n",
+    "                                 pip_packages=['azureml-contrib-services', 'azureml-defaults'])\n",
+    "\n",
+    "inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)\n",
+    "aks_config = AksWebservice.deploy_configuration()\n",
+    "\n",
+    "# # Enable token auth and disable (key) auth on the webservice\n",
+    "# aks_config = AksWebservice.deploy_configuration(token_auth_enabled=True, auth_enabled=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "aks_service_name ='gpu-rn50'\n",
+    "\n",
+    "aks_service = Model.deploy(workspace=ws,\n",
+    "                           name=aks_service_name,\n",
+    "                           models=[model],\n",
+    "                           inference_config=inference_config,\n",
+    "                           deployment_config=aks_config,\n",
+    "                           deployment_target=gpu_cluster)\n",
+    "\n",
+    "aks_service.wait_for_deployment(show_output = True)\n",
+    "print(aks_service.state)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Test the web service\n",
+    "We test the web sevice by passing the test images content."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "import requests\n",
+    "\n",
+    "# if (key) auth is enabled, fetch keys and include in the request\n",
+    "key1, key2 = aks_service.get_keys()\n",
+    "\n",
+    "headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n",
+    "\n",
+    "# # if token auth is enabled, fetch token and include in the request\n",
+    "# access_token, fetch_after = aks_service.get_token()\n",
+    "# headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + access_token}\n",
+    "\n",
+    "test_sample = open('snowleopardgaze.jpg', 'rb').read()\n",
+    "resp = requests.post(aks_service.scoring_uri, test_sample, headers=headers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clean up\n",
+    "Delete the service, image, model and compute target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "aks_service.delete()\n",
+    "model.delete()\n",
+    "gpu_cluster.delete()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "vaidyas"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.yml

@@ -0,0 +1,5 @@
+name: production-deploy-to-aks-gpu
+dependencies:
+- pip:
+  - azureml-sdk
+  - tensorflow

how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks-ssl.ipynb

@@ -9,12 +9,19 @@
     "Licensed under the MIT License."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.png)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Deploying a web service to Azure Kubernetes Service (AKS)\n",
-    "This notebook shows the steps for deploying a service: registering a model, creating an image, provisioning a cluster (one time action), and deploying a service to it. \n",
+    "This notebook shows the steps for deploying a service: registering a model, provisioning a cluster with ssl (one time action), and deploying a service to it. \n",
     "We then test and delete the service, image and model."
    ]
   },
@@ -27,7 +34,6 @@
     "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"
    ]
   },
@@ -78,7 +84,7 @@
     "#Register the model\n",
     "from azureml.core.model import Model\n",
     "model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n",
-    "                       model_name = \"sklearn_regression_model.pkl\", # this is the name the model is registered as\n",
+    "                       model_name = \"sklearn_model\", # this is the name the model is registered as\n",
     "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
     "                       description = \"Ridge regression model to predict diabetes\",\n",
     "                       workspace = ws)\n",
@@ -90,41 +96,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Create an image\n",
-    "Create an image using the registered model the script that will load and run the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%writefile score.py\n",
-    "import pickle\n",
-    "import json\n",
-    "import numpy\n",
-    "from sklearn.externals import joblib\n",
-    "from sklearn.linear_model import Ridge\n",
-    "from azureml.core.model import Model\n",
-    "\n",
-    "def init():\n",
-    "    global model\n",
-    "    # note here \"sklearn_regression_model.pkl\" is the name of the model registered under\n",
-    "    # this is a different behavior than before when the code is run locally, even though the code is the same.\n",
-    "    model_path = Model.get_model_path('sklearn_regression_model.pkl')\n",
-    "    # deserialize the model file back into a sklearn model\n",
-    "    model = joblib.load(model_path)\n",
-    "\n",
-    "# note you can pass in multiple rows for scoring\n",
-    "def run(raw_data):\n",
-    "    try:\n",
-    "        data = json.loads(raw_data)['data']\n",
-    "        data = numpy.array(data)\n",
-    "        result = model.predict(data)\n",
-    "    except Exception as e:\n",
-    "        result = str(e)\n",
-    "    return json.dumps({\"result\": result.tolist()})"
+    "# Create the Environment\n",
+    "Create an environment that the model will be deployed with"
    ]
   },
   {
@@ -133,44 +106,114 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from azureml.core import Environment\n",
     "from azureml.core.conda_dependencies import CondaDependencies \n",
     "\n",
-    "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
-    "\n",
-    "with open(\"myenv.yml\",\"w\") as f:\n",
-    "    f.write(myenv.serialize_to_string())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.core.image import ContainerImage\n",
-    "\n",
-    "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
-    "                                                  runtime = \"python\",\n",
-    "                                                  conda_file = \"myenv.yml\",\n",
-    "                                                  description = \"Image with ridge regression model\",\n",
-    "                                                  tags = {'area': \"diabetes\", 'type': \"regression\"}\n",
-    "                                                 )\n",
-    "\n",
-    "image = ContainerImage.create(name = \"myimage1\",\n",
-    "                              # this is the model object\n",
-    "                              models = [model],\n",
-    "                              image_config = image_config,\n",
-    "                              workspace = ws)\n",
-    "\n",
-    "image.wait_for_creation(show_output = True)"
+    "conda_deps = CondaDependencies.create(conda_packages=['numpy', 'scikit-learn==0.19.1', 'scipy'], pip_packages=['azureml-defaults', 'inference-schema'])\n",
+    "myenv = Environment(name='myenv')\n",
+    "myenv.python.conda_dependencies = conda_deps"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Provision the AKS Cluster\n",
-    "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it."
+    "#### Use a custom Docker image\n",
+    "\n",
+    "You can also specify a custom Docker image to be used as base image if you don't want to use the default base image provided by Azure ML. Please make sure the custom Docker image has Ubuntu >= 16.04, Conda >= 4.5.\\* and Python(3.5.\\* or 3.6.\\*).\n",
+    "\n",
+    "Only supported with `python` runtime.\n",
+    "```python\n",
+    "# use an image available in public Container Registry without authentication\n",
+    "myenv.docker.base_image = \"mcr.microsoft.com/azureml/o16n-sample-user-base/ubuntu-miniconda\"\n",
+    "\n",
+    "# or, use an image available in a private Container Registry\n",
+    "myenv.docker.base_image = \"myregistry.azurecr.io/mycustomimage:1.0\"\n",
+    "myenv.docker.base_image_registry.address = \"myregistry.azurecr.io\"\n",
+    "myenv.docker.base_image_registry.username = \"username\"\n",
+    "myenv.docker.base_image_registry.password = \"password\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Write the Entry Script\n",
+    "Write the script that will be used to predict on your model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile score_ssl.py\n",
+    "import os\n",
+    "import pickle\n",
+    "import json\n",
+    "import numpy\n",
+    "from sklearn.externals import joblib\n",
+    "from sklearn.linear_model import Ridge\n",
+    "from inference_schema.schema_decorators import input_schema, output_schema\n",
+    "from inference_schema.parameter_types.standard_py_parameter_type import StandardPythonParameterType\n",
+    "\n",
+    "def init():\n",
+    "    global model\n",
+    "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
+    "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
+    "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
+    "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
+    "    # deserialize the model file back into a sklearn model\n",
+    "    model = joblib.load(model_path)\n",
+    "\n",
+    "\n",
+    "standard_sample_input = {'a': 10, 'b': 9, 'c': 8, 'd': 7, 'e': 6, 'f': 5, 'g': 4, 'h': 3, 'i': 2, 'j': 1 }\n",
+    "standard_sample_output = {'outcome': 1}\n",
+    "\n",
+    "@input_schema('param', StandardPythonParameterType(standard_sample_input))\n",
+    "@output_schema(StandardPythonParameterType(standard_sample_output))\n",
+    "def run(param):\n",
+    "    try:\n",
+    "        raw_data = [param['a'], param['b'], param['c'], param['d'], param['e'], param['f'], param['g'], param['h'], param['i'], param['j']]\n",
+    "        data = numpy.array([raw_data])\n",
+    "        result = model.predict(data)\n",
+    "        return { 'outcome' : result[0] }\n",
+    "    except Exception as e:\n",
+    "        error = str(e)\n",
+    "        return error"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create the InferenceConfig\n",
+    "Create the inference config that will be used when deploying the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.model import InferenceConfig\n",
+    "\n",
+    "inf_config = InferenceConfig(entry_script='score_ssl.py', environment=myenv)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Provision the AKS Cluster with SSL\n",
+    "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\n",
+    "\n",
+    "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
+    "\n",
+    "See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-secure-web-service) for more details"
    ]
   },
   {
@@ -180,13 +223,18 @@
    "outputs": [],
    "source": [
     "# Use the default configuration (can also provide parameters to customize)\n",
-    "prov_config = AksCompute.provisioning_configuration()\n",
     "\n",
-    "aks_name = 'my-aks-9' \n",
+    "provisioning_config = AksCompute.provisioning_configuration()\n",
+    "# Leaf domain label generates a name using the formula\n",
+    "#  \"<leaf-domain-label>######.<azure-region>.cloudapp.azure.net\"\n",
+    "#  where \"######\" is a random series of characters\n",
+    "provisioning_config.enable_ssl(leaf_domain_label = \"contoso\", overwrite_existing_domain = True)\n",
+    "\n",
+    "aks_name = 'my-aks-ssl-1' \n",
     "# Create the cluster\n",
     "aks_target = ComputeTarget.create(workspace = ws, \n",
     "                                  name = aks_name, \n",
-    "                                  provisioning_configuration = prov_config)"
+    "                                  provisioning_configuration = provisioning_config)"
    ]
   },
   {
@@ -201,33 +249,6 @@
     "print(aks_target.provisioning_errors)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Optional step: Attach existing AKS cluster\n",
-    "\n",
-    "If you have existing AKS cluster in your Azure subscription, you can attach it to the Workspace."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "'''\n",
-    "# Use the default configuration (can also provide parameters to customize)\n",
-    "resource_id = '/subscriptions/92c76a2f-0e1c-4216-b65e-abf7a3f34c1e/resourcegroups/raymondsdk0604/providers/Microsoft.ContainerService/managedClusters/my-aks-0605d37425356b7d01'\n",
-    "\n",
-    "create_name='my-existing-aks' \n",
-    "# Create the cluster\n",
-    "aks_target = AksCompute.attach(workspace=ws, name=create_name, resource_id=resource_id)\n",
-    "# Wait for the operation to complete\n",
-    "aks_target.wait_for_completion(True)\n",
-    "'''"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -238,27 +259,27 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Set the web service configuration (using default here)\n",
-    "aks_config = AksWebservice.deploy_configuration()"
+   "metadata": {
+    "tags": [
+     "sample-deploy-to-aks"
     ]
    },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
    "outputs": [],
    "source": [
     "%%time\n",
-    "aks_service_name ='aks-service-1'\n",
     "\n",
-    "aks_service = Webservice.deploy_from_image(workspace = ws, \n",
-    "                                           name = aks_service_name,\n",
-    "                                           image = image,\n",
-    "                                           deployment_config = aks_config,\n",
-    "                                           deployment_target = aks_target)\n",
+    "aks_config = AksWebservice.deploy_configuration()\n",
+    "\n",
+    "aks_service_name ='aks-service-ssl-1'\n",
+    "\n",
+    "aks_service = Model.deploy(workspace=ws,\n",
+    "                           name=aks_service_name,\n",
+    "                           models=[model],\n",
+    "                           inference_config=inf_config,\n",
+    "                           deployment_config=aks_config,\n",
+    "                           deployment_target=aks_target,\n",
+    "                          overwrite=True)\n",
+    "\n",
     "aks_service.wait_for_deployment(show_output = True)\n",
     "print(aks_service.state)"
    ]
@@ -267,8 +288,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Test the web service\n",
-    "We test the web sevice by passing data."
+    "# Test the web service using run method\n",
+    "We test the web sevice by passing data.\n",
+    "Run() method retrieves API keys behind the scenes to make sure that call is authenticated."
    ]
   },
   {
@@ -280,14 +302,9 @@
     "%%time\n",
     "import json\n",
     "\n",
-    "test_sample = json.dumps({'data': [\n",
-    "    [1,2,3,4,5,6,7,8,9,10], \n",
-    "    [10,9,8,7,6,5,4,3,2,1]\n",
-    "]})\n",
-    "test_sample = bytes(test_sample,encoding = 'utf8')\n",
+    "standard_sample_input = json.dumps({'param': {'a': 10, 'b': 9, 'c': 8, 'd': 7, 'e': 6, 'f': 5, 'g': 4, 'h': 3, 'i': 2, 'j': 1 }})\n",
     "\n",
-    "prediction = aks_service.run(input_data = test_sample)\n",
-    "print(prediction)"
+    "aks_service.run(input_data=standard_sample_input)"
    ]
   },
   {
@@ -306,12 +323,16 @@
    "source": [
     "%%time\n",
     "aks_service.delete()\n",
-    "image.delete()\n",
     "model.delete()"
    ]
   }
  ],
  "metadata": {
+  "authors": [
+   {
+    "name": "vaidyas"
+   }
+  ],
   "kernelspec": {
    "display_name": "Python 3.6",
    "language": "python",
@@ -327,7 +348,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.6"
   }
  },
  "nbformat": 4,

how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks-ssl.yml

@@ -0,0 +1,8 @@
+name: production-deploy-to-aks-ssl
+dependencies:
+- pip:
+  - azureml-sdk
+  - matplotlib
+  - tqdm
+  - scipy
+  - sklearn

how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb

@@ -0,0 +1,623 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deploying a web service to Azure Kubernetes Service (AKS)\n",
+    "This notebook shows the steps for deploying a service: registering a model, creating an image, provisioning a cluster (one time action), and deploying a service to it. \n",
+    "We then test and delete the service, image and model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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.model import Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import azureml.core\n",
+    "print(azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Get workspace\n",
+    "Load existing workspace from the config file info."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.workspace import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Register the model\n",
+    "Register an existing trained model, add descirption and tags."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Register the model\n",
+    "from azureml.core.model import Model\n",
+    "model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n",
+    "                       model_name = \"sklearn_regression_model.pkl\", # this is the name the model is registered as\n",
+    "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
+    "                       description = \"Ridge regression model to predict diabetes\",\n",
+    "                       workspace = ws)\n",
+    "\n",
+    "print(model.name, model.description, model.version)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create the Environment\n",
+    "Create an environment that the model will be deployed with"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Environment\n",
+    "from azureml.core.conda_dependencies import CondaDependencies \n",
+    "\n",
+    "conda_deps = CondaDependencies.create(conda_packages=['numpy','scikit-learn==0.19.1','scipy'], pip_packages=['azureml-defaults', 'inference-schema'])\n",
+    "myenv = Environment(name='myenv')\n",
+    "myenv.python.conda_dependencies = conda_deps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Use a custom Docker image\n",
+    "\n",
+    "You can also specify a custom Docker image to be used as base image if you don't want to use the default base image provided by Azure ML. Please make sure the custom Docker image has Ubuntu >= 16.04, Conda >= 4.5.\\* and Python(3.5.\\* or 3.6.\\*).\n",
+    "\n",
+    "Only supported with `python` runtime.\n",
+    "```python\n",
+    "# use an image available in public Container Registry without authentication\n",
+    "myenv.docker.base_image = \"mcr.microsoft.com/azureml/o16n-sample-user-base/ubuntu-miniconda\"\n",
+    "\n",
+    "# or, use an image available in a private Container Registry\n",
+    "myenv.docker.base_image = \"myregistry.azurecr.io/mycustomimage:1.0\"\n",
+    "myenv.docker.base_image_registry.address = \"myregistry.azurecr.io\"\n",
+    "myenv.docker.base_image_registry.username = \"username\"\n",
+    "myenv.docker.base_image_registry.password = \"password\"\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Write the Entry Script\n",
+    "Write the script that will be used to predict on your model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile score.py\n",
+    "import os\n",
+    "import pickle\n",
+    "import json\n",
+    "import numpy\n",
+    "from sklearn.externals import joblib\n",
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "def init():\n",
+    "    global model\n",
+    "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
+    "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
+    "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
+    "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
+    "    # deserialize the model file back into a sklearn model\n",
+    "    model = joblib.load(model_path)\n",
+    "\n",
+    "# note you can pass in multiple rows for scoring\n",
+    "def run(raw_data):\n",
+    "    try:\n",
+    "        data = json.loads(raw_data)['data']\n",
+    "        data = numpy.array(data)\n",
+    "        result = model.predict(data)\n",
+    "        # you can return any data type as long as it is JSON-serializable\n",
+    "        return result.tolist()\n",
+    "    except Exception as e:\n",
+    "        error = str(e)\n",
+    "        return error"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create the InferenceConfig\n",
+    "Create the inference config that will be used when deploying the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.model import InferenceConfig\n",
+    "\n",
+    "inf_config = InferenceConfig(entry_script='score.py', environment=myenv)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model Profiling\n",
+    "\n",
+    "Profile your model to understand how much CPU and memory the service, created as a result of its deployment, will need. Profiling returns information such as CPU usage, memory usage, and response latency. It also provides a CPU and memory recommendation based on the resource usage. You can profile your model (or more precisely the service built based on your model) on any CPU and/or memory combination where 0.1 <= CPU <= 3.5 and 0.1GB <= memory <= 15GB. If you do not provide a CPU and/or memory requirement, we will test it on the default configuration of 3.5 CPU and 15GB memory.\n",
+    "\n",
+    "In order to profile your model you will need:\n",
+    "- a registered model\n",
+    "- an entry script\n",
+    "- an inference configuration\n",
+    "- a single column tabular dataset, where each row contains a string representing sample request data sent to the service.\n",
+    "\n",
+    "Please, note that profiling is a long running operation and can take up to 25 minutes depending on the size of the dataset.\n",
+    "\n",
+    "At this point we only support profiling of services that expect their request data to be a string, for example: string serialized json, text, string serialized image, etc. The content of each row of the dataset (string) will be put into the body of the HTTP request and sent to the service encapsulating the model for scoring.\n",
+    "\n",
+    "Below is an example of how you can construct an input dataset to profile a service which expects its incoming requests to contain serialized json. In this case we created a dataset based one hundred instances of the same request data. In real world scenarios however, we suggest that you use larger datasets with various inputs, especially if your model resource usage/behavior is input dependent."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You may want to register datasets using the register() method to your workspace so they can be shared with others, reused and referred to by name in your script.\n",
+    "You can try get the dataset first to see if it's already registered."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "from azureml.core import Datastore\n",
+    "from azureml.core.dataset import Dataset\n",
+    "from azureml.data import dataset_type_definitions\n",
+    "\n",
+    "dataset_name='sample_request_data'\n",
+    "\n",
+    "dataset_registered = False\n",
+    "try:\n",
+    "    sample_request_data = Dataset.get_by_name(workspace = ws, name = dataset_name)\n",
+    "    dataset_registered = True\n",
+    "except:\n",
+    "    print(\"The dataset {} is not registered in workspace yet.\".format(dataset_name))\n",
+    "\n",
+    "if not dataset_registered:\n",
+    "    input_json = {'data': [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
+    "                        [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]]}\n",
+    "    # create a string that can be put in the body of the request\n",
+    "    serialized_input_json = json.dumps(input_json)\n",
+    "    dataset_content = []\n",
+    "    for i in range(100):\n",
+    "        dataset_content.append(serialized_input_json)\n",
+    "    sample_request_data = '\\n'.join(dataset_content)\n",
+    "    file_name = \"{}.txt\".format(dataset_name)\n",
+    "    f = open(file_name, 'w')\n",
+    "    f.write(sample_request_data)\n",
+    "    f.close()\n",
+    "\n",
+    "    # upload the txt file created above to the Datastore and create a dataset from it\n",
+    "    data_store = Datastore.get_default(ws)\n",
+    "    data_store.upload_files(['./' + file_name], target_path='sample_request_data')\n",
+    "    datastore_path = [(data_store, 'sample_request_data' +'/' + file_name)]\n",
+    "    sample_request_data = Dataset.Tabular.from_delimited_files(\n",
+    "        datastore_path,\n",
+    "        separator='\\n',\n",
+    "        infer_column_types=True,\n",
+    "        header=dataset_type_definitions.PromoteHeadersBehavior.NO_HEADERS)\n",
+    "    sample_request_data = sample_request_data.register(workspace=ws,\n",
+    "                                                    name=dataset_name,\n",
+    "                                                    create_new_version=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have an input dataset we are ready to go ahead with profiling. In this case we are testing the previously introduced sklearn regression model on 1 CPU and 0.5 GB memory. The memory usage and recommendation presented in the result is measured in Gigabytes. The CPU usage and recommendation is measured in CPU cores."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "from azureml.core import Environment\n",
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "from azureml.core.model import Model, InferenceConfig\n",
+    "\n",
+    "\n",
+    "environment = Environment('my-sklearn-environment')\n",
+    "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
+    "    'azureml-defaults',\n",
+    "    'inference-schema[numpy-support]',\n",
+    "    'joblib',\n",
+    "    'numpy',\n",
+    "    'scikit-learn==0.19.1',\n",
+    "    'scipy'\n",
+    "])\n",
+    "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n",
+    "# if cpu and memory_in_gb parameters are not provided\n",
+    "# the model will be profiled on default configuration of\n",
+    "# 3.5CPU and 15GB memory\n",
+    "profile = Model.profile(ws,\n",
+    "            'sklearn-%s' % datetime.now().strftime('%m%d%Y-%H%M%S'),\n",
+    "            [model],\n",
+    "            inference_config,\n",
+    "            input_dataset=sample_request_data,\n",
+    "            cpu=1.0,\n",
+    "            memory_in_gb=0.5)\n",
+    "\n",
+    "# profiling is a long running operation and may take up to 25 min\n",
+    "profile.wait_for_completion(True)\n",
+    "details = profile.get_details()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Provision the AKS Cluster\n",
+    "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\n",
+    "\n",
+    "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import ComputeTarget\n",
+    "from azureml.core.compute_target import ComputeTargetException\n",
+    "\n",
+    "# Choose a name for your AKS cluster\n",
+    "aks_name = 'my-aks-9' \n",
+    "\n",
+    "# Verify that cluster does not exist already\n",
+    "try:\n",
+    "    aks_target = ComputeTarget(workspace=ws, name=aks_name)\n",
+    "    print('Found existing cluster, use it.')\n",
+    "except ComputeTargetException:\n",
+    "    # Use the default configuration (can also provide parameters to customize)\n",
+    "    prov_config = AksCompute.provisioning_configuration()\n",
+    "\n",
+    "    # Create the cluster\n",
+    "    aks_target = ComputeTarget.create(workspace = ws, \n",
+    "                                    name = aks_name, \n",
+    "                                    provisioning_configuration = prov_config)\n",
+    "\n",
+    "if aks_target.get_status() != \"Succeeded\":\n",
+    "    aks_target.wait_for_completion(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create AKS Cluster in an existing virtual network (optional)\n",
+    "See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-enable-virtual-network#use-azure-kubernetes-service) for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from azureml.core.compute import ComputeTarget, AksCompute\n",
+    "\n",
+    "# # Create the compute configuration and set virtual network information\n",
+    "# config = AksCompute.provisioning_configuration(location=\"eastus2\")\n",
+    "# config.vnet_resourcegroup_name = \"mygroup\"\n",
+    "# config.vnet_name = \"mynetwork\"\n",
+    "# config.subnet_name = \"default\"\n",
+    "# config.service_cidr = \"10.0.0.0/16\"\n",
+    "# config.dns_service_ip = \"10.0.0.10\"\n",
+    "# config.docker_bridge_cidr = \"172.17.0.1/16\"\n",
+    "\n",
+    "# # Create the compute target\n",
+    "# aks_target = ComputeTarget.create(workspace = ws,\n",
+    "#                                   name = \"myaks\",\n",
+    "#                                   provisioning_configuration = config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Enable SSL on the AKS Cluster (optional)\n",
+    "See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-secure-web-service) for more details"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# provisioning_config = AksCompute.provisioning_configuration(ssl_cert_pem_file=\"cert.pem\", ssl_key_pem_file=\"key.pem\", ssl_cname=\"www.contoso.com\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "aks_target.wait_for_completion(show_output = True)\n",
+    "print(aks_target.provisioning_state)\n",
+    "print(aks_target.provisioning_errors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Optional step: Attach existing AKS cluster\n",
+    "\n",
+    "If you have existing AKS cluster in your Azure subscription, you can attach it to the Workspace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Use the default configuration (can also provide parameters to customize)\n",
+    "# resource_id = '/subscriptions/92c76a2f-0e1c-4216-b65e-abf7a3f34c1e/resourcegroups/raymondsdk0604/providers/Microsoft.ContainerService/managedClusters/my-aks-0605d37425356b7d01'\n",
+    "\n",
+    "# create_name='my-existing-aks' \n",
+    "# # Create the cluster\n",
+    "# attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
+    "# aks_target = ComputeTarget.attach(workspace=ws, name=create_name, attach_configuration=attach_config)\n",
+    "# # Wait for the operation to complete\n",
+    "# aks_target.wait_for_completion(True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deploy web service to AKS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "sample-deploy-to-aks"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Set the web service configuration (using default here)\n",
+    "aks_config = AksWebservice.deploy_configuration()\n",
+    "\n",
+    "# # Enable token auth and disable (key) auth on the webservice\n",
+    "# aks_config = AksWebservice.deploy_configuration(token_auth_enabled=True, auth_enabled=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "sample-deploy-to-aks"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "aks_service_name ='aks-service-1'\n",
+    "\n",
+    "aks_service = Model.deploy(workspace=ws,\n",
+    "                           name=aks_service_name,\n",
+    "                           models=[model],\n",
+    "                           inference_config=inf_config,\n",
+    "                           deployment_config=aks_config,\n",
+    "                           deployment_target=aks_target)\n",
+    "\n",
+    "aks_service.wait_for_deployment(show_output = True)\n",
+    "print(aks_service.state)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Test the web service using run method\n",
+    "We test the web sevice by passing data.\n",
+    "Run() method retrieves API keys behind the scenes to make sure that call is authenticated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "import json\n",
+    "\n",
+    "test_sample = json.dumps({'data': [\n",
+    "    [1,2,3,4,5,6,7,8,9,10], \n",
+    "    [10,9,8,7,6,5,4,3,2,1]\n",
+    "]})\n",
+    "test_sample = bytes(test_sample,encoding = 'utf8')\n",
+    "\n",
+    "prediction = aks_service.run(input_data = test_sample)\n",
+    "print(prediction)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Test the web service using raw HTTP request (optional)\n",
+    "Alternatively you can construct a raw HTTP request and send it to the service. In this case you need to explicitly pass the HTTP header. This process is shown in the next 2 cells."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # if (key) auth is enabled, retrieve the API keys. AML generates two keys.\n",
+    "# key1, Key2 = aks_service.get_keys()\n",
+    "# print(key1)\n",
+    "\n",
+    "# # if token auth is enabled, retrieve the token.\n",
+    "# access_token, refresh_after = aks_service.get_token()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# construct raw HTTP request and send to the service\n",
+    "# %%time\n",
+    "\n",
+    "# import requests\n",
+    "\n",
+    "# import json\n",
+    "\n",
+    "# test_sample = json.dumps({'data': [\n",
+    "#     [1,2,3,4,5,6,7,8,9,10], \n",
+    "#     [10,9,8,7,6,5,4,3,2,1]\n",
+    "# ]})\n",
+    "# test_sample = bytes(test_sample,encoding = 'utf8')\n",
+    "\n",
+    "# # If (key) auth is enabled, don't forget to add key to the HTTP header.\n",
+    "# headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n",
+    "\n",
+    "# # If token auth is enabled, don't forget to add token to the HTTP header.\n",
+    "# headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + access_token}\n",
+    "\n",
+    "# resp = requests.post(aks_service.scoring_uri, test_sample, headers=headers)\n",
+    "\n",
+    "\n",
+    "# print(\"prediction:\", resp.text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clean up\n",
+    "Delete the service, image and model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "aks_service.delete()\n",
+    "model.delete()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "vaidyas"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python",
+   "name": "python36"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.yml

@@ -0,0 +1,8 @@
+name: production-deploy-to-aks
+dependencies:
+- pip:
+  - azureml-sdk
+  - matplotlib
+  - tqdm
+  - scipy
+  - sklearn