update samples from Release-42 as a part of SDK release

update samples from Release-41 as a part of  SDK release

README.md

@@ -13,7 +13,7 @@ Read more detailed instructions on [how to set up your environment](./NBSETUP.md
 
 ## How to navigate and use the example notebooks?
 If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, you should always run the [Configuration](./configuration.ipynb) notebook first when setting up a notebook library on a new machine or in a new environment. It configures your notebook library to connect to an Azure Machine Learning workspace, and sets up your workspace and compute to be used by many of the other examples. 
-This [index](.index.md) should assist in navigating the Azure Machine Learning notebook samples and encourage efficient retrieval of topics and content. 
+This [index](./index.md) should assist in navigating the Azure Machine Learning notebook samples and encourage efficient retrieval of topics and content. 
 
 If you want to...
 

configuration.ipynb

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.1.1rc0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.1.5 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

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

@@ -144,7 +144,7 @@ jupyter notebook
     - Dataset: forecasting for a bike-sharing
     - Example of training an automated ML forecasting model on multiple time-series
 
-- [automl-forecasting-function.ipynb](forecasting-high-frequency/automl-forecasting-function.ipynb)
+- [auto-ml-forecasting-function.ipynb](forecasting-high-frequency/auto-ml-forecasting-function.ipynb)
     - Example of training an automated ML forecasting model on multiple time-series
 
 - [auto-ml-forecasting-beer-remote.ipynb](forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb)

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

@@ -21,6 +21,7 @@ dependencies:
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
   - azureml-defaults
+  - azureml-dataprep[pandas]
   - azureml-train-automl
   - azureml-train
   - azureml-widgets
@@ -28,7 +29,6 @@ dependencies:
   - azureml-contrib-interpret
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
-  - joblib
   - onnxruntime==1.0.0
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
 

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

@@ -22,6 +22,7 @@ dependencies:
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
   - azureml-defaults
+  - azureml-dataprep[pandas]
   - azureml-train-automl
   - azureml-train
   - azureml-widgets
@@ -29,7 +30,6 @@ dependencies:
   - azureml-contrib-interpret
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
-  - joblib
   - onnxruntime==1.0.0
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz  
 

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

@@ -320,7 +320,6 @@
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**training_data**|Input dataset, containing both features and label column.|\n",
         "|**label_column_name**|The name of the label column.|\n",
-        "|**model_explainability**|Indicate to explain each trained pipeline or not.|\n",
         "\n",
         "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
       ]
@@ -352,7 +351,6 @@
         "                             training_data = train_data,\n",
         "                             label_column_name = label,\n",
         "                             validation_data = validation_dataset,\n",
-        "                             model_explainability=True,\n",
         "                             **automl_settings\n",
         "                            )"
       ]
@@ -500,11 +498,11 @@
       "outputs": [],
       "source": [
         "# Wait for the best model explanation run to complete\n",
-        "from azureml.train.automl.run import AutoMLRun\n",
+        "from azureml.core.run import Run\n",
         "model_explainability_run_id = remote_run.get_properties().get('ModelExplainRunId')\n",
         "print(model_explainability_run_id)\n",
         "if model_explainability_run_id is not None:\n",
-        "    model_explainability_run = AutoMLRun(experiment=experiment, run_id=model_explainability_run_id)\n",
+        "    model_explainability_run = Run(experiment=experiment, run_id=model_explainability_run_id)\n",
         "    model_explainability_run.wait_for_completion()\n",
         "\n",
         "# Get the best run object\n",

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

@@ -343,7 +343,7 @@
       "outputs": [],
       "source": [
         "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl import AutoMLStep\n",
+        "from azureml.pipeline.steps import AutoMLStep\n",
         "\n",
         "automl_settings = {\n",
         "    \"iteration_timeout_minutes\": 10,\n",

how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.yml

@@ -4,6 +4,7 @@ dependencies:
 - py-xgboost<=0.80
 - pip:
   - azureml-sdk
+  - numpy==1.16.2
   - azureml-train-automl
   - azureml-widgets
   - matplotlib

how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.yml

@@ -4,6 +4,7 @@ dependencies:
 - py-xgboost<=0.80
 - pip:
   - azureml-sdk
+  - numpy==1.16.2
   - azureml-train-automl
   - azureml-widgets
   - matplotlib

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

@@ -2,6 +2,7 @@ name: auto-ml-forecasting-energy-demand
 dependencies:
 - pip:
   - azureml-sdk
+  - numpy==1.16.2
   - azureml-train-automl
   - azureml-widgets
   - matplotlib

how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/auto-ml-forecasting-function.ipynb

@@ -459,8 +459,8 @@
         "# use forecast_quantiles function, not the forecast() one\n",
         "y_pred_quantiles =  fitted_model.forecast_quantiles(X_test)\n",
         "\n",
-        "# it all nicely aligns column-wise\n",
-        "pd.concat([X_test.reset_index(), y_pred_quantiles], axis=1)"
+        "# quantile forecasts returned in a Dataframe along with the time and grain columns \n",
+        "y_pred_quantiles"
       ]
     },
     {
@@ -701,7 +701,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "erwright, nirovins"
+        "name": "erwright"
       }
     ],
     "category": "tutorial",

how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/auto-ml-forecasting-function.yml

@@ -1,9 +1,10 @@
-name: automl-forecasting-function
+name: auto-ml-forecasting-function
 dependencies:
 - fbprophet==0.5
 - py-xgboost<=0.80
 - pip:
   - azureml-sdk
+  - numpy==1.16.2
   - azureml-train-automl
   - azureml-widgets
   - matplotlib

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

@@ -4,6 +4,8 @@ dependencies:
 - py-xgboost<=0.80
 - pip:
   - azureml-sdk
+  - numpy==1.16.2
+  - pandas==0.23.4
   - azureml-train-automl
   - azureml-widgets
   - matplotlib

how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb

@@ -514,7 +514,7 @@
         "    content = cefr.read()\n",
         "\n",
         "# Replace the values in train_explainer.py file with the appropriate values\n",
-        "content = content.replace('<<experimnet_name>>', automl_run.experiment.name) # your experiment name.\n",
+        "content = content.replace('<<experiment_name>>', automl_run.experiment.name) # your experiment name.\n",
         "content = content.replace('<<run_id>>', automl_run.id) # Run-id of the AutoML run for which you want to explain the model.\n",
         "content = content.replace('<<target_column_name>>', 'ERP') # Your target column name\n",
         "content = content.replace('<<task>>', 'regression') # Training task type\n",
@@ -532,8 +532,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "#### Create conda configuration for model explanations experiment\n",
-        "We need `azureml-explain-model`, `azureml-train-automl` and `azureml-core` packages for computing model explanations for your AutoML model on remote compute."
+        "#### Create conda configuration for model explanations experiment from automl_run object"
       ]
     },
     {
@@ -552,13 +551,9 @@
         "# Set compute target to AmlCompute\n",
         "conda_run_config.target = compute_target\n",
         "conda_run_config.environment.docker.enabled = True\n",
-        "azureml_pip_packages = [\n",
-        "    'azureml-train-automl', 'azureml-core', 'azureml-explain-model'\n",
-        "]\n",
         "\n",
         "# specify CondaDependencies obj\n",
-        "conda_run_config.environment.python.conda_dependencies = CondaDependencies.create(\n",
-        "    pip_packages=azureml_pip_packages)"
+        "conda_run_config.environment.python.conda_dependencies = automl_run.get_environment().python.conda_dependencies"
       ]
     },
     {

how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/train_explainer.py

@@ -22,7 +22,7 @@ run = Run.get_context()
 ws = run.experiment.workspace
 
 # Get the AutoML run object from the experiment name and the workspace
-experiment = Experiment(ws, '<<experimnet_name>>')
+experiment = Experiment(ws, '<<experiment_name>>')
 automl_run = Run(experiment=experiment, run_id='<<run_id>>')
 
 # Check if this AutoML model is explainable

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

@@ -2,6 +2,7 @@ name: auto-ml-regression
 dependencies:
 - pip:
   - azureml-sdk
+  - pandas==0.23.4
   - azureml-train-automl
   - azureml-widgets
   - matplotlib

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

@@ -341,9 +341,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "import json\n",
-        "\n",
-        "\n",
         "input_payload = json.dumps({\n",
         "    'data': [\n",
         "        [ 0.03807591,  0.05068012,  0.06169621, 0.02187235, -0.0442235,\n",
@@ -376,16 +373,101 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Model profiling\n",
+        "### Model Profiling\n",
         "\n",
-        "You can also take advantage of the profiling feature to estimate CPU and memory requirements for models.\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",
-        "```python\n",
-        "profile = Model.profile(ws, \"profilename\", [model], inference_config, test_sample)\n",
-        "profile.wait_for_profiling(True)\n",
-        "profiling_results = profile.get_results()\n",
-        "print(profiling_results)\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",
+        "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": "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",
+        "\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 = 'sample_request_data.txt'\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='diabetes_sample_request_data',\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'\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",
+        "profile.wait_for_completion(True)\n",
+        "details = profile.get_details()"
       ]
     },
     {

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

@@ -145,6 +145,110 @@
         "                                   environment=environment)"
       ]
     },
+    {
+      "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",
+        "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": "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",
+        "\n",
+        "# create a string that can be put in the body of the request\n",
+        "serialized_input_json = json.dumps({\n",
+        "    '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",
+        "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 = 'sample_request_data_diabetes.txt'\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_diabetes')\n",
+        "datastore_path = [(data_store, 'sample_request_data_diabetes' +'/' + file_name)]\n",
+        "sample_request_data_diabetes = 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_diabetes = sample_request_data_diabetes.register(workspace=ws,\n",
+        "                                                   name='sample_request_data_diabetes',\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'\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",
+        "            'profile-%s' % datetime.now().strftime('%m%d%Y-%H%M%S'),\n",
+        "            [model],\n",
+        "            inference_config,\n",
+        "            input_dataset=sample_request_data_diabetes,\n",
+        "            cpu=1.0,\n",
+        "            memory_in_gb=0.5)\n",
+        "\n",
+        "profile.wait_for_completion(True)\n",
+        "details = profile.get_details()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

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

@@ -0,0 +1,314 @@
+{
+  "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": [
+        "# Register the model\n",
+        "Register an existing trained model, add descirption and tags. 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. You can 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": [
+        "#Register the model\n",
+        "from azureml.core.model import Model\n",
+        "model = Model.register(model_path = \"resnet50\", # this points to a local file\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": "aashishb"
+      }
+    ],
+    "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.ipynb

@@ -198,6 +198,106 @@
         "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",
+        "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": "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",
+        "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 = 'sample_request_data.txt'\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='sample_request_data',\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'\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",
+        "profile.wait_for_completion(True)\n",
+        "details = profile.get_details()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb

@@ -76,7 +76,7 @@
         "from azureml.core.runconfig import RunConfiguration\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
         "\n",
-        "from azureml.train.automl.runtime import AutoMLStep\n",
+        "from azureml.pipeline.steps import AutoMLStep\n",
         "\n",
         "# Check core SDK version number\n",
         "print(\"SDK version:\", azureml.core.VERSION)"
@@ -173,12 +173,7 @@
       "source": [
         "# create a new RunConfig object\n",
         "conda_run_config = RunConfiguration(framework=\"python\")\n",
-        "\n",
-        "conda_run_config.environment.docker.enabled = True\n",
-        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
-        "\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], \n",
-        "                              conda_packages=['numpy', 'py-xgboost<=0.80'])\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'])\n",
         "conda_run_config.environment.python.conda_dependencies = cd\n",
         "\n",
         "print('run config is ready')"

how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb

@@ -100,7 +100,7 @@
         "\n",
         "# Check core SDK version number\n",
         "\n",
-        "print(\"This notebook was created using SDK version 1.1.1rc0, you are currently running version\", azureml.core.VERSION)"
+        "print(\"This notebook was created using SDK version 1.1.5, you are currently running version\", azureml.core.VERSION)"
       ]
     },
     {

how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.ipynb

@@ -145,9 +145,12 @@
         "import requests\n",
         "import os\n",
         "\n",
-        "tf_code = requests.get(\"https://raw.githubusercontent.com/tensorflow/tensorflow/r1.8/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py\")\n",
+        "tf_code = requests.get(\"https://raw.githubusercontent.com/tensorflow/tensorflow/r2.1/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py\")\n",
+        "input_code = requests.get(\"https://raw.githubusercontent.com/tensorflow/tensorflow/r2.1/tensorflow/examples/tutorials/mnist/input_data.py\")\n",
         "with open(os.path.join(exp_dir, \"mnist_with_summaries.py\"), \"w\") as file:\n",
-        "    file.write(tf_code.text)"
+        "    file.write(tf_code.text.replace(\"from tensorflow.examples.tutorials.mnist import input_data\", \"import input_data\"))\n",
+        "with open(os.path.join(exp_dir, \"input_data.py\"), \"w\") as file:\n",
+        "    file.write(input_code.text)"
       ]
     },
     {
@@ -186,7 +189,7 @@
         "from azureml.core import Experiment\n",
         "from azureml.core.script_run_config import ScriptRunConfig\n",
         "\n",
-        "logs_dir = os.path.join(os.curdir, \"logs\")\n",
+        "logs_dir = os.path.join(os.curdir, os.path.join(\"logs\", \"tb-logs\"))\n",
         "data_dir = os.path.abspath(os.path.join(os.curdir, \"mnist_data\"))\n",
         "\n",
         "if not path.exists(data_dir):\n",
@@ -334,7 +337,8 @@
         "tf_estimator = TensorFlow(source_directory=exp_dir,\n",
         "                          compute_target=attached_dsvm_compute,\n",
         "                          entry_script='mnist_with_summaries.py',\n",
-        "                          script_params=script_params)\n",
+        "                          script_params=script_params,\n",
+        "                          framework_version=\"2.0\")\n",
         "\n",
         "run = exp.submit(tf_estimator)\n",
         "\n",
@@ -396,17 +400,16 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
-        "\n",
+        "from azureml.core.compute import AmlCompute\n",
         "# choose a name for your cluster\n",
-        "cluster_name = \"cpucluster\"\n",
+        "cluster_name = \"cpu-cluster\"\n",
         "\n",
         "cts = ws.compute_targets\n",
         "found = False\n",
         "if cluster_name in cts and cts[cluster_name].type == 'AmlCompute':\n",
-        "   found = True\n",
-        "   print('Found existing compute target.')\n",
-        "   compute_target = cts[cluster_name]\n",
+        "    found = True\n",
+        "    print('Found existing compute target.')\n",
+        "    compute_target = cts[cluster_name]\n",
         "if not found:\n",
         "    print('Creating a new compute target...')\n",
         "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2', \n",
@@ -444,7 +447,8 @@
         "tf_estimator = TensorFlow(source_directory=exp_dir,\n",
         "                          compute_target=compute_target,\n",
         "                          entry_script='mnist_with_summaries.py',\n",
-        "                          script_params=script_params)\n",
+        "                          script_params=script_params,\n",
+        "                          framework_version=\"2.0\")\n",
         "\n",
         "run = exp.submit(tf_estimator)\n",
         "\n",
@@ -539,6 +543,24 @@
         "name": "roastala"
       }
     ],
+    "category": "training",
+    "compute": [
+      "Local",
+      "DSVM",
+      "AML Compute"
+    ],
+    "datasets": [
+      "None"
+    ],
+    "deployment": [
+      "None"
+    ],
+    "exclude_from_index": false,
+    "framework": [
+      "TensorFlow"
+    ],
+    "friendly_name": "Tensorboard integration with run history",
+    "index_order": 3,
     "kernelspec": {
       "display_name": "Python 3.6",
       "language": "python",
@@ -556,28 +578,10 @@
       "pygments_lexer": "ipython3",
       "version": "3.6.6"
     },
-    "friendly_name": "Tensorboard integration with run history",
-    "exclude_from_index": false,
-    "index_order": 3,
-    "category": "training",
-    "task": "Run a TensorFlow job and view its Tensorboard output live",
-    "datasets": [
-      "None"
-    ],
-    "compute": [
-      "Local",
-      "DSVM",
-      "AML Compute"
-    ],
-    "deployment": [
-      "None"
-    ],
-    "framework": [
-      "TensorFlow"
-    ],
     "tags": [
       "None"
-    ]
+    ],
+    "task": "Run a TensorFlow job and view its Tensorboard output live"
   },
   "nbformat": 4,
   "nbformat_minor": 2

how-to-use-azureml/track-and-monitor-experiments/tensorboard/tensorboard.yml

@@ -3,4 +3,5 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-tensorboard
-  - tensorflow<1.15
+  - tensorflow
+  - setuptools>=41.0.0

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

@@ -3,7 +3,8 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-tensorboard
-  - tensorflow<1.15.0
+  - tensorflow
   - tqdm
   - scipy
   - sklearn
+  - setuptools>=41.0.0

how-to-use-azureml/training/train-in-spark/train-in-spark.ipynb

@@ -149,6 +149,20 @@
         "                                                          ssh_port=22, \n",
         "                                                          username=os.environ.get('hdiusername', '<ssh_username>'), \n",
         "                                                          password=os.environ.get('hdipassword', '<my_password>'))\n",
+        "\n",
+        "# The following Azure regions do not support attaching a HDI Cluster using the public IP address of the HDI Cluster.\n",
+        "# Instead, use the Azure Resource Manager ID of the HDI Cluster with the resource_id parameter:\n",
+        "# US East\n",
+        "# US West 2\n",
+        "# US South Central\n",
+        "# The resource ID of the HDI Cluster can be constructed using the\n",
+        "# subscription ID, resource group name, and cluster name using the following string format:\n",
+        "# /subscriptions/<subscription_id>/resourceGroups/<resource_group>/providers/Microsoft.HDInsight/clusters/<cluster_name>.    \n",
+        "# If in US East, US West 2, or US South Central, use the following instead:\n",
+        "# attach_config = HDInsightCompute.attach_configuration(resource_id='<resource_id>',\n",
+        "#                                                       ssh_port=22,\n",
+        "#                                                       username=os.environ.get('hdiusername', '<ssh_username>'),\n",
+        "#                                                       password=os.environ.get('hdipassword', '<my_password>'))\n",
         "    hdi_compute = ComputeTarget.attach(workspace=ws, \n",
         "                                       name='myhdi', \n",
         "                                       attach_configuration=attach_config)\n",

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

@@ -167,7 +167,10 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "name": "user_managed_env",
+        "msdoc": "how-to-track-experiments.md"
+      },
       "outputs": [],
       "source": [
         "from azureml.core import Environment\n",
@@ -192,7 +195,10 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "name": "src",
+        "msdoc": "how-to-track-experiments.md"
+      },
       "outputs": [],
       "source": [
         "from azureml.core import ScriptRunConfig\n",
@@ -204,7 +210,10 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "name": "run",
+        "msdoc": "how-to-track-experiments.md"
+      },
       "outputs": [],
       "source": [
         "run = exp.submit(src)"

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

@@ -266,7 +266,23 @@
         "                                                       ssh_port=22,\n",
         "                                                       username=username,\n",
         "                                                       private_key_file='./.ssh/id_rsa')\n",
-        "    attached_dsvm_compute = ComputeTarget.attach(workspace=ws,\n",
+        "\n",
+        "\n",
+        "# The following Azure regions do not support attaching a virtual machine using the public IP address of the VM.\n",
+        "# Instead, use the Azure Resource Manager ID of the VM with the resource_id parameter:\n",
+        "# US East\n",
+        "# US West 2\n",
+        "# US South Central\n",
+        "# The resource ID of the VM can be constructed using the\n",
+        "# subscription ID, resource group name, and VM name using the following string format:\n",
+        "# /subscriptions/<subscription_id>/resourceGroups/<resource_group>/providers/Microsoft.Compute/virtualMachines/<vm_name>.    \n",
+        "# If in US East, US West 2, or US South Central, use the following instead:\n",
+        "# attach_config = RemoteCompute.attach_configuration(resource_id='<resource_id>',\n",
+        "#                                                    ssh_port=22,\n",
+        "#                                                    username='username',\n",
+        "#                                                    private_key_file='./.ssh/id_rsa')\n",
+        "\n",
+        "   attached_dsvm_compute = ComputeTarget.attach(workspace=ws,\n",
         "                                                 name=compute_target_name,\n",
         "                                                 attach_configuration=attach_config)\n",
         "    attached_dsvm_compute.wait_for_completion(show_output=True)"

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

@@ -80,7 +80,9 @@
       "metadata": {
         "tags": [
           "install"
-        ]
+        ],
+        "name": "load_ws",
+        "msdoc": "how-to-track-experiments.md"
       },
       "outputs": [],
       "source": [
@@ -113,7 +115,10 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "name": "load_data",
+        "msdoc": "how-to-track-experiments.md"
+      },
       "outputs": [],
       "source": [
         "from sklearn.datasets import load_diabetes\n",
@@ -155,7 +160,9 @@
         "tags": [
           "local run",
           "outputs upload"
-        ]
+        ],
+        "name": "create_experiment",
+        "msdoc": "how-to-track-experiments.md"
       },
       "outputs": [],
       "source": [

how-to-use-azureml/work-with-data/datasets-tutorial/labeled-datasets/labeled-datasets.yml

@@ -1,10 +0,0 @@
-name: labeled-datasets
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-dataprep
-  - pandas
-  - fuse
-  - azureml.contrib.dataset
-  - matplotlib
-  - torchvision

how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb

@@ -141,7 +141,7 @@
         "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "# choose a name for your cluster\n",
-        "cluster_name = \"your-cluster-name\"\n",
+        "cluster_name = \"gpu-cluster\"\n",
         "\n",
         "try:\n",
         "    compute_target = ComputeTarget(workspace=workspace, name=cluster_name)\n",

how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.yml

@@ -3,5 +3,5 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-dataprep
-  - pandas
+  - pandas<=0.23.4
   - fuse

how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.ipynb

@@ -543,7 +543,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "ylxiong"
+        "name": "jamgan"
       }
     ],
     "category": "tutorial",

how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.yml

@@ -3,4 +3,4 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-dataprep
-  - pandas
+  - pandas<=0.23.4

how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb

@@ -529,8 +529,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(run.get_metrics())\n",
-        "metrics = run.get_metrics()"
+        "run.wait_for_completion()\n",
+        "metrics = run.get_metrics()\n",
+        "print(metrics)"
       ]
     },
     {

how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.yml

@@ -4,6 +4,6 @@ dependencies:
   - azureml-sdk
   - azureml-widgets
   - azureml-dataprep
-  - pandas
+  - pandas<=0.23.4
   - fuse
   - scikit-learn

index.md

@@ -28,7 +28,7 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 | :star:[Datasets with ML Pipeline](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb) | Train | Fashion MNIST | Remote | None | Azure ML | Dataset, Pipeline, Estimator, ScriptRun |
 | :star:[Filtering data using Tabular Timeseiries Dataset related API](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.ipynb) | Filtering | NOAA | Local | None | Azure ML | Dataset, Tabular Timeseries |
 | :star:[Train with Datasets (Tabular and File)](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb) | Train | Iris, Diabetes | Remote | None | Azure ML | Dataset, Estimator, ScriptRun |
-| [Forecasting away from training data](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.ipynb) | Forecasting | None | Remote | None | Azure ML AutoML | Forecasting, Confidence Intervals |
+| [Forecasting away from training data](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/auto-ml-forecasting-function.ipynb) | Forecasting | None | Remote | None | Azure ML AutoML | Forecasting, Confidence Intervals |
 | [Automated ML run with basic edition features.](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb) | Classification | Bankmarketing | AML | ACI | None | featurization, explainability, remote_run, AutomatedML |
 | [Classification of credit card fraudulent transactions using Automated ML](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb) | Classification | Creditcard | AML Compute | None | None | remote_run, AutomatedML |
 | [Automated ML run with featurization and model explainability.](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb) | Regression | MachineData | AML | ACI | None | featurization, explainability, remote_run, AutomatedML |
@@ -117,6 +117,7 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 | [enable-app-insights-in-production-service](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb) |  |  |  |  |  |  |
 | [onnx-model-register-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-model-register-and-deploy.ipynb) |  |  |  |  |  |  |
 | [production-deploy-to-aks](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb) |  |  |  |  |  |  |
+| [production-deploy-to-aks-gpu](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.ipynb) |  |  |  |  |  |  |
 | [tensorflow-model-register-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/tensorflow/tensorflow-model-register-and-deploy.ipynb) |  |  |  |  |  |  |
 | [explain-model-on-amlcompute](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb) |  |  |  |  |  |  |
 | [save-retrieve-explanations-run-history](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.ipynb) |  |  |  |  |  |  |

setup-environment/configuration.ipynb

@@ -102,7 +102,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.1.1rc0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.1.5 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

tutorials/README.md

@@ -28,7 +28,6 @@ The following tutorials are intended to provide examples of more advanced featur
 | Tutorial | Description | Notebook | Task | Framework | 
 | --- | --- | --- | --- | --- |
 | [Build an Azure Machine Learning pipeline for batch scoring](https://docs.microsoft.com/azure/machine-learning/tutorial-pipeline-batch-scoring-classification) | Create an Azure Machine Learning pipeline to run batch scoring image classification jobs | [tutorial-pipeline-batch-scoring-classification.ipynb](machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb) | Image Classification | TensorFlow
-Complete these tutorials to learn how to train and deploy models using Azure Machine Learning services and Python SDK. These Notebooks accompany the tutorial articles for:
 
 For additional documentation and resources, see the [official documentation site for Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/).
 

tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb

@@ -30,7 +30,9 @@
         "\n",
         "## Prerequisites\n",
         "\n",
-        "See prerequisites in the [Azure Machine Learning documentation](https://docs.microsoft.com/azure/machine-learning/service/tutorial-train-models-with-aml#prerequisites)."
+        "See prerequisites in the [Azure Machine Learning documentation](https://docs.microsoft.com/azure/machine-learning/service/tutorial-train-models-with-aml#prerequisites).\n",
+        "\n",
+        "On the computer running this notebook, conda install matplotlib, numpy, scikit-learn=0.22.1"
       ]
     },
     {
@@ -126,7 +128,8 @@
       "metadata": {},
       "source": [
         "### Create or Attach existing compute resource\n",
-        "By using Azure Machine Learning Compute, a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you create Azure Machine Learning Compute as your training environment. The code below creates the compute clusters for you if they don't already exist in your workspace.\n",
+        "By using Azure Machine Learning Compute, a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you create Azure Machine Learning Compute as your training environment. You will submit Python code to run on this VM later in the tutorial. \n",
+        "The code below creates the compute clusters for you if they don't already exist in your workspace.\n",
         "\n",
         "**Creation of compute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace the code will skip the creation process."
       ]
@@ -263,7 +266,7 @@
       "source": [
         "## Train on a remote cluster\n",
         "\n",
-        "For this task, submit the job to the remote training cluster you set up earlier.  To submit a job you:\n",
+        "For this task, you submit the job to run on the remote training cluster you set up earlier.  To submit a job you:\n",
         "* Create a directory\n",
         "* Create a training script\n",
         "* Create an estimator object\n",
@@ -308,7 +311,7 @@
         "import glob\n",
         "\n",
         "from sklearn.linear_model import LogisticRegression\n",
-        "from sklearn.externals import joblib\n",
+        "import joblib\n",
         "\n",
         "from azureml.core import Run\n",
         "from utils import load_data\n",
@@ -396,15 +399,20 @@
       "source": [
         "### Create an estimator\n",
         "\n",
-        "An estimator object is used to submit the run. Azure Machine Learning has pre-configured estimators for common machine learning frameworks, as well as generic Estimator. Create SKLearn estimator for scikit-learn model, by specifying\n",
+        "An estimator object is used to submit the run. Azure Machine Learning has pre-configured estimators for common machine learning frameworks, as well as generic Estimator. Create an estimator by specifying\n",
         "\n",
         "* The name of the estimator object, `est`\n",
         "* The directory that contains your scripts. All the files in this directory are uploaded into the cluster nodes for execution. \n",
         "* The compute target.  In this case you will use the AmlCompute you created\n",
         "* The training script name, train.py\n",
-        "* Parameters required from the training script \n",
+        "* An environment that contains the libraries needed to run the script\n",
+        "* Parameters required from the training script. \n",
         "\n",
-        "In this tutorial, the target is AmlCompute. All files in the script folder are uploaded into the cluster nodes for execution. The data_folder is set to use the dataset."
+        "In this tutorial, the target is AmlCompute. All files in the script folder are uploaded into the cluster nodes for execution. The data_folder is set to use the dataset.\n",
+        "\n",
+        "First, create the environment that contains: the scikit-learn library, azureml-dataprep required for accessing the dataset, and azureml-defaults which contains the dependencies for logging metrics. The azureml-defaults also contains the dependencies required for deploying the model as a web service later in the part 2 of the tutorial.\n",
+        "\n",
+        "Once the environment is defined, register it with the Workspace to re-use it in part 2 of the tutorial."
       ]
     },
     {
@@ -417,10 +425,20 @@
         "from azureml.core.conda_dependencies import CondaDependencies\n",
         "\n",
         "# to install required packages\n",
-        "env = Environment('my_env')\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-sdk','scikit-learn==0.22.1','azureml-dataprep[pandas,fuse]>=1.1.14'])\n",
+        "env = Environment('tutorial-env')\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-dataprep[pandas,fuse]>=1.1.14', 'azureml-defaults'], conda_packages = ['scikit-learn==0.22.1'])\n",
         "\n",
-        "env.python.conda_dependencies = cd"
+        "env.python.conda_dependencies = cd\n",
+        "\n",
+        "# Register environment to re-use later\n",
+        "env.register(workspace = ws)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Then, create the estimator by specifying the training script, compute target and environment."
       ]
     },
     {
@@ -433,7 +451,7 @@
       },
       "outputs": [],
       "source": [
-        "from azureml.train.sklearn import SKLearn\n",
+        "from azureml.train.estimator import Estimator\n",
         "\n",
         "script_params = {\n",
         "    # to mount files referenced by mnist dataset\n",
@@ -441,7 +459,7 @@
         "    '--regularization': 0.5\n",
         "}\n",
         "\n",
-        "est = SKLearn(source_directory=script_folder,\n",
+        "est = Estimator(source_directory=script_folder,\n",
         "              script_params=script_params,\n",
         "              compute_target=compute_target,\n",
         "              environment_definition=env,\n",
@@ -666,7 +684,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.9"
+      "version": "3.7.6"
     },
     "msauthor": "roastala"
   },

tutorials/image-classification-mnist-data/img-classification-part2-deploy.ipynb

@@ -39,11 +39,7 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {
-        "tags": [
-          "register model from file"
-        ]
-      },
+      "metadata": {},
       "outputs": [],
       "source": [
         "# If you did NOT complete the tutorial, you can instead run this cell \n",
@@ -62,7 +58,19 @@
         "                        model_name=model_name,\n",
         "                        tags={\"data\": \"mnist\", \"model\": \"classification\"},\n",
         "                        description=\"Mnist handwriting recognition\",\n",
-        "                        workspace=ws)"
+        "                        workspace=ws)\n",
+        "\n",
+        "from azureml.core.environment import Environment\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "\n",
+        "# to install required packages\n",
+        "env = Environment('tutorial-env')\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-dataprep[pandas,fuse]>=1.1.14', 'azureml-defaults'], conda_packages = ['scikit-learn==0.22.1'])\n",
+        "\n",
+        "env.python.conda_dependencies = cd\n",
+        "\n",
+        "# Register environment to re-use later\n",
+        "env.register(workspace = ws)"
       ]
     },
     {
@@ -102,9 +110,61 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Retrieve the model\n",
+        "## Deploy as web service\n",
         "\n",
-        "You registered a model in your workspace in the previous tutorial. Now, load this workspace and download the model to your local directory."
+        "Deploy the model as a web service hosted in ACI. \n",
+        "\n",
+        "To build the correct environment for ACI, provide the following:\n",
+        "* A scoring script to show how to use the model\n",
+        "* A configuration file to build the ACI\n",
+        "* The model you trained before\n",
+        "\n",
+        "### Create scoring script\n",
+        "\n",
+        "Create the scoring script, called score.py, used by the web service call to show how to use the model.\n",
+        "\n",
+        "You must include two required functions into the scoring script:\n",
+        "* The `init()` function, which typically loads the model into a global object. This function is run only once when the Docker container is started. \n",
+        "\n",
+        "* The `run(input_data)` function uses the model to predict a value based on the input data. Inputs and outputs to the run typically use JSON for serialization and de-serialization, but other formats are supported.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%writefile score.py\n",
+        "import json\n",
+        "import numpy as np\n",
+        "import os\n",
+        "import pickle\n",
+        "import joblib\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_mnist_model.pkl')\n",
+        "    model = joblib.load(model_path)\n",
+        "\n",
+        "def run(raw_data):\n",
+        "    data = np.array(json.loads(raw_data)['data'])\n",
+        "    # make prediction\n",
+        "    y_hat = model.predict(data)\n",
+        "    # you can return any data type as long as it is JSON-serializable\n",
+        "    return y_hat.tolist()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create configuration file\n",
+        "\n",
+        "Create a deployment configuration file and specify the number of CPUs and gigabyte of RAM needed for your ACI container. While it depends on your model, the default of 1 core and 1 gigabyte of RAM is usually sufficient for many models. If you feel you need more later, you would have to recreate the image and redeploy the service."
       ]
     },
     {
@@ -112,37 +172,98 @@
       "execution_count": null,
       "metadata": {
         "tags": [
-          "load workspace",
-          "download model"
+          "configure web service",
+          "aci"
         ]
       },
       "outputs": [],
       "source": [
-        "from azureml.core import Workspace\n",
-        "from azureml.core.model import Model\n",
-        "import os \n",
-        "ws = Workspace.from_config()\n",
-        "model=Model(ws, 'sklearn_mnist')\n",
+        "from azureml.core.webservice import AciWebservice\n",
         "\n",
-        "model.download(target_dir=os.getcwd(), exist_ok=True)\n",
-        "\n",
-        "# verify the downloaded model file\n",
-        "file_path = os.path.join(os.getcwd(), \"sklearn_mnist_model.pkl\")\n",
-        "\n",
-        "os.stat(file_path)"
+        "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
+        "                                               memory_gb=1, \n",
+        "                                               tags={\"data\": \"MNIST\",  \"method\" : \"sklearn\"}, \n",
+        "                                               description='Predict MNIST with sklearn')"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Test model locally\n",
+        "### Deploy in ACI\n",
+        "Estimated time to complete: **about 2-5 minutes**\n",
         "\n",
-        "Before deploying, make sure your model is working locally by:\n",
-        "* Downloading the test data if you haven't already\n",
-        "* Loading test data\n",
-        "* Predicting test data\n",
-        "* Examining the confusion matrix"
+        "Configure the image and deploy. The following code goes through these steps:\n",
+        "\n",
+        "1. Create environment object containing dependencies needed by the model using the environment file (`myenv.yml`)\n",
+        "1. Create inference configuration necessary to deploy the model as a web service using:\n",
+        "   * The scoring file (`score.py`)\n",
+        "   * envrionment object created in previous step\n",
+        "1. Deploy the model to the ACI container.\n",
+        "1. Get the web service HTTP endpoint."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "configure image",
+          "create image",
+          "deploy web service",
+          "aci"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "from azureml.core.webservice import Webservice\n",
+        "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.environment import Environment\n",
+        "from azureml.core import Workspace\n",
+        "from azureml.core.model import Model\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "model = Model(ws, 'sklearn_mnist')\n",
+        "\n",
+        "\n",
+        "myenv = Environment.get(workspace=ws, name=\"tutorial-env\", version=\"1\")\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)\n",
+        "\n",
+        "service = Model.deploy(workspace=ws, \n",
+        "                       name='sklearn-mnist-svc', \n",
+        "                       models=[model], \n",
+        "                       inference_config=inference_config, \n",
+        "                       deployment_config=aciconfig)\n",
+        "\n",
+        "service.wait_for_deployment(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Get the scoring web service's HTTP endpoint, which accepts REST client calls. This endpoint can be shared with anyone who wants to test the web service or integrate it into an application."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "get scoring uri"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "print(service.scoring_uri)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Test the model\n"
       ]
     },
     {
@@ -150,7 +271,7 @@
       "metadata": {},
       "source": [
         "### Download test data\n",
-        "If you haven't already, download the test data to the **./data/** directory"
+        "Download the test data to the **./data/** directory"
       ]
     },
     {
@@ -159,16 +280,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# download test data\n",
         "import os\n",
-        "import urllib.request\n",
+        "from azureml.core import Dataset\n",
+        "from azureml.opendatasets import MNIST\n",
         "\n",
         "data_folder = os.path.join(os.getcwd(), 'data')\n",
-        "os.makedirs(data_folder, exist_ok = True)\n",
+        "os.makedirs(data_folder, exist_ok=True)\n",
         "\n",
-        "\n",
-        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz', filename=os.path.join(data_folder, 'test-images.gz'))\n",
-        "urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz', filename=os.path.join(data_folder, 'test-labels.gz'))"
+        "mnist_file_dataset = MNIST.get_file_dataset()\n",
+        "mnist_file_dataset.download(data_folder, overwrite=True)"
       ]
     },
     {
@@ -191,8 +311,8 @@
         "\n",
         "data_folder = os.path.join(os.getcwd(), 'data')\n",
         "# note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the neural network converge faster\n",
-        "X_test = load_data(os.path.join(data_folder, 'test-images.gz'), False) / 255.0\n",
-        "y_test = load_data(os.path.join(data_folder, 'test-labels.gz'), True).reshape(-1)"
+        "X_test = load_data(os.path.join(data_folder, 't10k-images-idx3-ubyte.gz'), False) / 255.0\n",
+        "y_test = load_data(os.path.join(data_folder, 't10k-labels-idx1-ubyte.gz'), True).reshape(-1)"
       ]
     },
     {
@@ -201,7 +321,13 @@
       "source": [
         "### Predict test data\n",
         "\n",
-        "Feed the test dataset to the model to get predictions."
+        "Feed the test dataset to the model to get predictions.\n",
+        "\n",
+        "\n",
+        "The following code goes through these steps:\n",
+        "1. Send the data as a JSON array to the web service hosted in ACI. \n",
+        "\n",
+        "1. Use the SDK's `run` API to invoke the service. You can also make raw calls using any HTTP tool such as curl."
       ]
     },
     {
@@ -210,12 +336,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "import pickle\n",
-        "import joblib\n",
-        "\n",
-        "clf = joblib.load( os.path.join(os.getcwd(), 'sklearn_mnist_model.pkl'))\n",
-        "y_hat = clf.predict(X_test)\n",
-        "print(y_hat)"
+        "import json\n",
+        "test = json.dumps({\"data\": X_test.tolist()})\n",
+        "test = bytes(test, encoding='utf8')\n",
+        "y_hat = service.run(input_data=test)"
       ]
     },
     {
@@ -277,209 +401,10 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Deploy as web service\n",
+        "## Show predictions\n",
         "\n",
-        "Once you've tested the model and are satisfied with the results, deploy the model as a web service hosted in ACI. \n",
+        "Test the deployed model with a random sample of 30 images from the test data.  \n",
         "\n",
-        "To build the correct environment for ACI, provide the following:\n",
-        "* A scoring script to show how to use the model\n",
-        "* An environment file to show what packages need to be installed\n",
-        "* A configuration file to build the ACI\n",
-        "* The model you trained before\n",
-        "\n",
-        "### Create scoring script\n",
-        "\n",
-        "Create the scoring script, called score.py, used by the web service call to show how to use the model.\n",
-        "\n",
-        "You must include two required functions into the scoring script:\n",
-        "* The `init()` function, which typically loads the model into a global object. This function is run only once when the Docker container is started. \n",
-        "\n",
-        "* The `run(input_data)` function uses the model to predict a value based on the input data. Inputs and outputs to the run typically use JSON for serialization and de-serialization, but other formats are supported.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile score.py\n",
-        "import json\n",
-        "import numpy as np\n",
-        "import os\n",
-        "import pickle\n",
-        "import joblib\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_mnist_model.pkl')\n",
-        "    model = joblib.load(model_path)\n",
-        "\n",
-        "def run(raw_data):\n",
-        "    data = np.array(json.loads(raw_data)['data'])\n",
-        "    # make prediction\n",
-        "    y_hat = model.predict(data)\n",
-        "    # you can return any data type as long as it is JSON-serializable\n",
-        "    return y_hat.tolist()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create environment file\n",
-        "\n",
-        "Next, create an environment file, called myenv.yml, that specifies all of the script's package dependencies. This file is used to ensure that all of those dependencies are installed in the Docker image. This model needs `scikit-learn` and `azureml-sdk`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "set conda dependencies"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies \n",
-        "\n",
-        "myenv = CondaDependencies()\n",
-        "myenv.add_pip_package(\"scikit-learn==0.22.1\")\n",
-        "myenv.add_pip_package(\"azureml-defaults\")\n",
-        "\n",
-        "with open(\"myenv.yml\",\"w\") as f:\n",
-        "    f.write(myenv.serialize_to_string())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Review the content of the `myenv.yml` file."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "with open(\"myenv.yml\",\"r\") as f:\n",
-        "    print(f.read())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create configuration file\n",
-        "\n",
-        "Create a deployment configuration file and specify the number of CPUs and gigabyte of RAM needed for your ACI container. While it depends on your model, the default of 1 core and 1 gigabyte of RAM is usually sufficient for many models. If you feel you need more later, you would have to recreate the image and redeploy the service."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "configure web 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={\"data\": \"MNIST\",  \"method\" : \"sklearn\"}, \n",
-        "                                               description='Predict MNIST with sklearn')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Deploy in ACI\n",
-        "Estimated time to complete: **about 7-8 minutes**\n",
-        "\n",
-        "Configure the image and deploy. The following code goes through these steps:\n",
-        "\n",
-        "1. Create environment object containing dependencies needed by the model using the environment file (`myenv.yml`)\n",
-        "1. Create inference configuration necessary to deploy the model as a web service using:\n",
-        "   * The scoring file (`score.py`)\n",
-        "   * envrionment object created in previous step\n",
-        "1. Deploy the model to the ACI container.\n",
-        "1. Get the web service HTTP endpoint."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "configure image",
-          "create image",
-          "deploy web service",
-          "aci"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "from azureml.core.webservice import Webservice\n",
-        "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)\n",
-        "\n",
-        "service = Model.deploy(workspace=ws, \n",
-        "                       name='sklearn-mnist-svc', \n",
-        "                       models=[model], \n",
-        "                       inference_config=inference_config, \n",
-        "                       deployment_config=aciconfig)\n",
-        "\n",
-        "service.wait_for_deployment(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Get the scoring web service's HTTP endpoint, which accepts REST client calls. This endpoint can be shared with anyone who wants to test the web service or integrate it into an application."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "get scoring uri"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "print(service.scoring_uri)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Test deployed service\n",
-        "\n",
-        "Earlier you scored all the test data with the local version of the model. Now, you can test the deployed model with a random sample of 30 images from the test data.  \n",
-        "\n",
-        "The following code goes through these steps:\n",
-        "1. Send the data as a JSON array to the web service hosted in ACI. \n",
-        "\n",
-        "1. Use the SDK's `run` API to invoke the service. You can also make raw calls using any HTTP tool such as curl.\n",
         "\n",
         "1. Print the returned predictions and plot them along with the input images. Red font and inverse image (white on black) is used to highlight the misclassified samples. \n",
         "\n",