update samples from Release-160 as a part of 1.0.76 SDK release

remove deprecated auto_prepare_environment

configuration.ipynb

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.0.74 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.0.76 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

contrib/batch_inferencing/README.md

@@ -0,0 +1,127 @@
+# Azure Machine Learning Batch Inference
+
+Azure Machine Learning Batch Inference targets large inference jobs that are not time-sensitive. Batch Inference provides cost-effective inference compute scaling, with unparalleled throughput for asynchronous applications. It is optimized for high-throughput, fire-and-forget inference over large collections of data.
+
+# Getting Started with Batch Inference Public Preview
+
+Batch inference public preview offers a platform in which to do large inference or generic parallel map-style operations. Below introduces the major steps to use this new functionality. For a quick try, please follow the prerequisites and simply run the sample notebooks provided in this directory.
+
+## Prerequisites
+
+### Python package installation
+Following the convention of most AzureML Public Preview features, Batch Inference SDK is currently available as a contrib package.
+
+If you're unfamiliar with creating a new Python environment, you may follow this example for [creating a conda environment](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#local).  Batch Inference package can be installed through the following pip command.
+```
+pip install azureml-contrib-pipeline-steps
+```
+
+### Creation of Azure Machine Learning Workspace
+If you do not already have a Azure ML Workspace, please run the [configuration Notebook](../../configuration.ipynb).
+
+## Configure a Batch Inference job
+
+To run a Batch Inference job, you will need to gather some configuration data.
+
+1. **ParallelRunConfig**
+    - **entry_script**: the local file path to the scoring script. If source_directory is specified, use relative path, otherwise use any path accessible on machine.
+    - **error_threshold**: the number of record failures for TabularDataset and file failures for FileDataset that should be ignored during processing. If the aggregated error count (across all workers) goes above this value, then the job will be aborted. Set to -1 to ignore all failures during processing.
+    - **output_action**: one of the following values
+        - **"append_row"**: all values output by run() method invocations will be aggregated into one unique file named parallel_run_step.txt that is created in the output location.
+        - **"summary_only"** – scoring script will handle the output by itself.  The script still needs to return one output row per successfully-processed input item. This is used for error threshold calculation (the actual value of the output row is ignored).
+    - **source_directory**: supporting files for scoring (optional)
+    - **compute_target**: only **AmlCompute** is supported currently
+    - **node_count**: number of compute nodes to use.
+    - **process_count_per_node**: number of processes per node (optional, default value is 1).
+    - **mini_batch_size**: the approximate amount of input data passed to each run() invocation.  For FileDataset input, this is number of files user script can process in one run() call. For TabularDataset input it is approximate size of data user script can process in one run() call. E.g. 1024, 1024KB, 10MB, 1GB (optional, default value 10 files for FileDataset and 1MB for TabularDataset.)
+    - **logging_level**: log verbosity. Values in increasing verbosity are: 'WARNING', 'INFO', 'DEBUG' (optional, default value is 'INFO').
+    - **run_invocation_timeout**: run method invocation timeout period in seconds (optional, default value is 60).
+    - **environment**: The environment definition. This field configures the Python environment. It can be configured to use an existing Python environment or to set up a temp environment for the experiment. The definition is also responsible for setting the required application dependencies.
+    - **description**: name given to batch service.
+
+2. **Scoring (entry) script**: entry point for execution, scoring script should contain two functions:
+    - **init()**: this function should be used for any costly or common preparation for subsequent inferences, e.g., deserializing and loading the model into a global object.
+    - **run(mini_batch)**: The method to be parallelized. Each invocation will have one minibatch.
+        - **mini_batch**: Batch inference will invoke run method and pass either a list or Pandas DataFrame as an argument to the method. Each entry in min_batch will be - a filepath if input is a FileDataset, a Pandas DataFrame if input is a TabularDataset.
+        - **return value**: run() method should return a Pandas DataFrame or an array. For append_row output_action, these returned elements are appended into the common output file. For summary_only, the contents of the elements are ignored. For all output actions, each returned output element indicates one successful inference of input element in the input mini-batch.
+
+3. **Base image** (optional)
+    - if GPU is required, use DEFAULT_GPU_IMAGE as base image in environment. [Example GPU environment](./file-dataset-image-inference-mnist.ipynb#specify-the-environment-to-run-the-script)
+
+Example image pull:
+```python
+from azureml.core.runconfig import ContainerRegistry
+
+# use an image available in public Container Registry without authentication
+public_base_image = "mcr.microsoft.com/azureml/o16n-sample-user-base/ubuntu-miniconda"
+
+# or use an image available in a private Container Registry
+base_image = "myregistry.azurecr.io/mycustomimage:1.0"
+base_image_registry = ContainerRegistry()
+base_image_registry.address = "myregistry.azurecr.io"
+base_image_registry.username = "username"
+base_image_registry.password = "password"
+```
+
+
+## Create a batch inference job
+
+**ParallelRunStep** is a newly added step in the azureml.contrib.pipeline.steps package. You will use it to add a step to create a batch inference job with your Azure machine learning pipeline. (Use batch inference without an Azure machine learning pipeline is not supported yet). ParallelRunStep has all the following parameters:
+  - **name**: this name will be used to register batch inference service, has the following naming restrictions: (unique, 3-32 chars and regex ^\[a-z\]([-a-z0-9]*[a-z0-9])?$)
+  - **models**: zero or more model names already registered in Azure Machine Learning model registry.
+  - **parallel_run_config**: ParallelRunConfig as defined above.
+  - **inputs**: one or more Dataset objects.
+  - **output**: this should be a PipelineData object encapsulating an Azure BLOB container path. 
+  - **arguments**: list of custom arguments passed to scoring script (optional)
+  - **allow_reuse**: optional, default value is True. If the inputs remain the same as a previous run, it will make the previous run results immediately available (skips re-computing the step).
+
+## Passing arguments from pipeline submission to script
+
+Many tasks require arguments to be passed from job submission to the distributed runs. Below is an example to pass such information.
+```
+# from script which creates pipeline job
+parallelrun_step = ParallelRunStep(
+  ...
+  arguments=["--model_name", "mosaic"]     # name of the model we want to use, in case we have more than one option
+)
+```
+```
+# from driver.py/score.py/task.py
+import argparse
+
+parser.add_argument('--model_name', dest="model_name")
+
+args, unknown_args = parser.parse_known_args()
+
+# to access values
+args.model_name # "mosaic"
+```
+
+## Submit a batch inference job
+
+You can submit a batch inference job by pipeline_run, or through REST calls with a published pipeline. To control node count using REST API/experiment, please use aml_node_count(special) pipeline parameter. A typical use case follows:
+
+```python
+pipeline = Pipeline(workspace=ws, steps=[parallelrun_step])
+pipeline_run = Experiment(ws, 'name_of_pipeline_run').submit(pipeline)
+```
+
+## Monitor your batch inference job
+
+A batch inference job can take a long time to finish. You can monitor your job's progress from Azure portal, using Azure ML widgets, view console output through SDK, or check out overview.txt in log/azureml directory.
+
+```python
+# view with widgets (will display GUI inside a browser)
+from azureml.widgets import RunDetails
+RunDetails(pipeline_run).show()
+
+# simple console output
+pipeline_run.wait_for_completion(show_output=True)
+```
+
+# Sample notebooks
+
+-  [file-dataset-image-inference-mnist.ipynb](./file-dataset-image-inference-mnist.ipynb) demonstrates how to run batch inference on an MNIST dataset.
+-  [tabular-dataset-inference-iris.ipynb](./tabular-dataset-inference-iris.ipynb) demonstrates how to run batch inference on an IRIS dataset.
+
+![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/batch_inferencing/README.png) 

contrib/batch_inferencing/file-dataset-image-inference-mnist.ipynb

@@ -8,6 +8,13 @@
         "Licensed under the MIT License."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/contrib/batch_inferencing/file-dataset-image-inference-mnist.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -29,8 +36,6 @@
         "- Register the pretrained MNIST model into the model registry. \n",
         "- Use the registered model to do batch inference on the images in the data blob container.\n",
         "\n",
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/contrib/batch_inferencing/file-dataset-image-inference-mnist.png)\n",
-        "\n",
         "## Prerequisites\n",
         "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first. This sets you up with a working config file that has information on your workspace, subscription id, etc. "
       ]
@@ -485,7 +490,7 @@
       "source": [
         "## Cleanup Compute resources\n",
         "\n",
-        "For re-occuring jobs, it may be wise to keep compute the compute resources and allow compute nodes to scale down to 0. However, since this is just a single-run job, we are free to release the allocated compute resources."
+        "For re-occurring jobs, it may be wise to keep compute the compute resources and allow compute nodes to scale down to 0. However, since this is just a single-run job, we are free to release the allocated compute resources."
       ]
     },
     {
@@ -514,6 +519,27 @@
         "name": "tracych"
       }
     ],
+    "friendly_name": "MNIST data inferencing using ParallelRunStep",
+    "exclude_from_index": false,
+    "index_order": 1,
+    "category": "Other notebooks",
+    "compute": [
+      "AML Compute"
+    ],
+    "datasets": [
+      "MNIST"
+    ],
+    "deployment": [
+      "None"
+    ],
+    "framework": [
+      "None"
+    ],
+    "tags": [
+      "Batch Inferencing",
+      "Pipeline"
+    ],
+    "task": "Digit identification",
     "kernelspec": {
       "display_name": "Python 3.6",
       "language": "python",

contrib/batch_inferencing/file-dataset-image-inference-mnist.yml

@@ -3,5 +3,4 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-contrib-pipeline-steps
-  - pandas
   - azureml-widgets

contrib/batch_inferencing/tabular-dataset-inference-iris.ipynb

@@ -8,6 +8,13 @@
         "Licensed under the MIT License."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/contrib/batch_inferencing/tabular-dataset-inference-iris.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -28,9 +35,7 @@
         "- Use the registered model to do batch inference on the CSV files in the data blob container.\n",
         "\n",
         "## Prerequisites\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first. This sets you up with a working config file that has information on your workspace, subscription id, etc. \n"
-        "\n",
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/contrib/batch_inferencing/tabular-dataset-inference-iris.png)"
       ]
     },
     {
@@ -460,7 +465,7 @@
       "metadata": {},
       "source": [
         "## Cleanup compute resources\n",
-        "For re-occuring jobs, it may be wise to keep compute the compute resources and allow compute nodes to scale down to 0. However, since this is just a single run job, we are free to release the allocated compute resources."
+        "For re-occurring jobs, it may be wise to keep compute the compute resources and allow compute nodes to scale down to 0. However, since this is just a single run job, we are free to release the allocated compute resources."
       ]
     },
     {
@@ -489,6 +494,27 @@
         "name": "tracych"
       }
     ],
+    "friendly_name": "IRIS data inferencing using ParallelRunStep",
+    "exclude_from_index": false,
+    "index_order": 1,
+    "category": "Other notebooks",
+    "compute": [
+      "AML Compute"
+    ],
+    "datasets": [
+      "IRIS"
+    ],
+    "deployment": [
+      "None"
+    ],
+    "framework": [
+      "None"
+    ],
+    "tags": [
+      "Batch Inferencing",
+      "Pipeline"
+    ],
+    "task": "Recognize flower type",
     "kernelspec": {
       "display_name": "Python 3.6",
       "language": "python",
@@ -505,8 +531,7 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.2"
-    },
+    }
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved.\u00e2\u20ac\u00afLicensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2

contrib/batch_inferencing/tabular-dataset-inference-iris.yml

@@ -3,5 +3,4 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-contrib-pipeline-steps
-  - pandas
   - azureml-widgets

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

@@ -27,10 +27,10 @@ dependencies:
   - azureml-explain-model
   - azureml-pipeline
   - azureml-contrib-interpret
-  - pandas_ml
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - joblib
+  - onnxruntime==0.4.0
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
 
 channels:

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

@@ -28,10 +28,10 @@ dependencies:
   - azureml-explain-model
   - azureml-pipeline
   - azureml-contrib-interpret
-  - pandas_ml
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - joblib
+  - onnxruntime==0.4.0
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz  
 
 channels:

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

@@ -293,7 +293,6 @@
         "|**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)"
       ]
@@ -325,7 +324,6 @@
         "                             training_data = train_data,\n",
         "                             label_column_name = label,\n",
         "                             validation_data = validation_dataset,\n",
-        "                             model_explainability=True,\n",
         "                             **automl_settings\n",
         "                            )"
       ]
@@ -458,72 +456,6 @@
         "RunDetails(remote_run).show() "
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Retrieve the Best Model's explanation\n",
-        "Retrieve the explanation from the best_run which includes explanations for engineered features and raw features. Make sure that the run for generating explanations for the best model is completed."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Wait for the best model explanation run to complete\n",
-        "from azureml.train.automl.run import AutoMLRun\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.wait_for_completion()\n",
-        "\n",
-        "# Get the best run object\n",
-        "best_run, fitted_model = remote_run.get_output()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Download engineered feature importance from artifact store\n",
-        "You can use ExplanationClient to download the engineered feature explanations from the artifact store of the best_run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "client = ExplanationClient.from_run(best_run)\n",
-        "engineered_explanations = client.download_model_explanation(raw=False)\n",
-        "exp_data = engineered_explanations.get_feature_importance_dict()\n",
-        "exp_data"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Download raw feature importance from artifact store\n",
-        "You can use ExplanationClient to download the raw feature explanations from the artifact store of the best_run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "client = ExplanationClient.from_run(best_run)\n",
-        "engineered_explanations = client.download_model_explanation(raw=True)\n",
-        "exp_data = engineered_explanations.get_feature_importance_dict()\n",
-        "exp_data"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -557,7 +489,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.automl.core.onnx_convert import OnnxConverter\n",
+        "from azureml.automl.runtime.onnx_convert import OnnxConverter\n",
         "onnx_fl_path = \"./best_model.onnx\"\n",
         "OnnxConverter.save_onnx_model(onnx_mdl, onnx_fl_path)"
       ]
@@ -566,17 +498,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Predict with the ONNX model, using onnxruntime package\n",
+        "### Predict with the ONNX model, using onnxruntime package"
-        "#### Note: The code will install the onnxruntime==0.4.0 if not installed. Newer versions of the onnxruntime have compatibility issues."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "test_df = test_dataset.to_pandas_dataframe()"
       ]
     },
     {
@@ -595,21 +517,8 @@
         "else:\n",
         "    python_version_compatible = False\n",
         "\n",
-        "onnxrt_present = False\n",
-        "try:\n",
         "import onnxruntime\n",
-        "    from azureml.automl.core.onnx_convert import OnnxInferenceHelper    \n",
+        "from azureml.automl.runtime.onnx_convert import OnnxInferenceHelper\n",
-        "    from onnxruntime import __version__ as ORT_VER\n",
-        "    if ORT_VER == '0.4.0':\n",
-        "        onnxrt_present = True\n",
-        "except ImportError:\n",
-        "    onnxrt_present = False\n",
-        "    \n",
-        "# Install the onnxruntime if the version 0.4.0 is not installed.\n",
-        "if not onnxrt_present:\n",
-        "    print(\"Installing the onnxruntime version 0.4.0.\")\n",
-        "    !{sys.executable} -m pip install --user --force-reinstall onnxruntime==0.4.0\n",
-        "    onnxrt_present = True\n",
         "\n",
         "def get_onnx_res(run):\n",
         "    res_path = 'onnx_resource.json'\n",
@@ -618,7 +527,8 @@
         "        onnx_res = json.load(f)\n",
         "    return onnx_res\n",
         "\n",
-        "if onnxrt_present and python_version_compatible:    \n",
+        "if python_version_compatible:\n",
+        "    test_df = test_dataset.to_pandas_dataframe()\n",
         "    mdl_bytes = onnx_mdl.SerializeToString()\n",
         "    onnx_res = get_onnx_res(best_run)\n",
         "\n",
@@ -628,10 +538,7 @@
         "    print(pred_onnx)\n",
         "    print(pred_prob_onnx)\n",
         "else:\n",
-        "    if not python_version_compatible:\n",
+        "    print('Please use Python version 3.6 or 3.7 to run the inference helper.')"
-        "        print('Please use Python version 3.6 or 3.7 to run the inference helper.')    \n",
-        "    if not onnxrt_present:\n",
-        "        print('Please install the onnxruntime package to do the prediction with ONNX model.')"
       ]
     },
     {

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

@@ -451,7 +451,7 @@
       "AML Compute"
     ],
     "datasets": [
-      "creditcard"
+      "Creditcard"
     ],
     "deployment": [
       "None"

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

@@ -522,6 +522,9 @@
     "datasets": [
       "None"
     ],
+    "compute": [
+      "AML Compute"
+    ],
     "deployment": [
       "None"
     ],

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

@@ -323,7 +323,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.train.automl import AutoMLStep, AutoMLConfig\n",
+        "from azureml.train.automl import AutoMLConfig\n",
+        "from azureml.train.automl.runtime import AutoMLStep\n",
         "\n",
         "automl_settings = {\n",
         "    \"iteration_timeout_minutes\": 20,\n",

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

@@ -301,7 +301,7 @@
       "source": [
         "### Setting forecaster maximum horizon \n",
         "\n",
-        "The forecast horizon is the number of periods into the future that the model should predict. Here, we set the horizon to 4 periods (i.e. 4 months). Notice that this is much shorter than the number of days in the test set; we will need to use a rolling test to evaluate the performance on the whole test set. For more discussion of forecast horizons and guiding principles for setting them, please see the [energy demand notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand).  "
+        "The forecast horizon is the number of periods into the future that the model should predict. Here, we set the horizon to 12 periods (i.e. 12 months). Notice that this is much shorter than the number of months in the test set; we will need to use a rolling test to evaluate the performance on the whole test set. For more discussion of forecast horizons and guiding principles for setting them, please see the [energy demand notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand).  "
       ]
     },
     {

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

@@ -161,7 +161,7 @@
       "source": [
         "## Data\n",
         "\n",
-        "The [Machine Learning service workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-workspace), is paired with the storage account, which contains the default data store. We will use it to upload the bike share data and create [tabular dataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) for training. A tabular dataset defines a series of lazily-evaluated, immutable operations to load data from the data source into tabular representation."
+        "The [Machine Learning service workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-workspace) is paired with the storage account, which contains the default data store. We will use it to upload the bike share data and create [tabular dataset](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) for training. A tabular dataset defines a series of lazily-evaluated, immutable operations to load data from the data source into tabular representation."
       ]
     },
     {
@@ -586,7 +586,7 @@
     ],
     "category": "tutorial",
     "compute": [
-      "remote"
+      "Remote"
     ],
     "datasets": [
       "BikeShare"
@@ -625,7 +625,7 @@
     "tags": [
       "Forecasting"
     ],
-    "task": "forecasting",
+    "task": "Forecasting",
     "version": 3
   },
   "nbformat": 4,

how-to-use-azureml/automated-machine-learning/forecasting-bike-share/forecasting_script.py

@@ -1,6 +1,6 @@
 import argparse
 import azureml.train.automl
-from azureml.automl.core._vendor.automl.client.core.runtime import forecasting_models
+from azureml.automl.runtime._vendor.automl.client.core.runtime import forecasting_models
 from azureml.core import Run
 from sklearn.externals import joblib
 import forecasting_helper

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

@@ -730,14 +730,7 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.8"
-    },
+    }
-    "star_tag": [
-      "featured"
-    ],
-    "tags": [
-      ""
-    ],
-    "task": "Forecasting"
   },
   "nbformat": 4,
   "nbformat_minor": 2

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

@@ -152,7 +152,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# upload data to your default datastore\n",
+        "# upload training and test data to your default datastore\n",
         "ds = ws.get_default_datastore()\n",
         "ds.upload(src_dir='./data', target_path='groupdata', overwrite=True, show_progress=True)"
       ]
@@ -178,7 +178,7 @@
         "\n",
         "#### Create or Attach existing AmlCompute\n",
         "\n",
-        "You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
+        "You will need to create a compute target for your automated ML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
         "#### Creation of AmlCompute takes approximately 5 minutes. \n",
         "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
         "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this article on the default limits and how to request more quota."

how-to-use-azureml/automated-machine-learning/forecasting-grouping/build.py

@@ -11,7 +11,7 @@ from azureml.core.dataset import Dataset
 from azureml.pipeline.core import PipelineData, PipelineParameter, TrainingOutput, StepSequence
 from azureml.pipeline.steps import PythonScriptStep
 from azureml.train.automl import AutoMLConfig
-from azureml.train.automl import AutoMLStep
+from azureml.train.automl.runtime import AutoMLStep
 
 
 def _get_groups(data: Dataset, group_column_names: List[str]) -> pd.DataFrame:
@@ -33,7 +33,7 @@ def _get_configs(automlconfig: AutoMLConfig,
         group_name = "#####".join(str(x) for x in group.values)
         group_name = valid_chars.sub('', group_name)
         for key in group.index:
-            single = data._dataflow.filter(data._dataflow[key] == group[key])
+            single = single._dataflow.filter(data._dataflow[key] == group[key])
         group_conf = copy.deepcopy(automlconfig)
         group_conf.user_settings['training_data'] = single
         group_conf.user_settings['label_column_name'] = target_column
@@ -106,6 +106,13 @@ def build_pipeline_steps(automlconfig: AutoMLConfig,
 
     final_steps = steps
     if deploy:
+        # modify the conda dependencies to ensure we pick up correct
+        # versions of azureml-defaults and azureml-train-automl
+        cd = CondaDependencies.create(pip_packages=['azureml-defaults', 'azureml-train-automl'])
+        automl_deps = CondaDependencies(conda_dependencies_file_path='deploy/myenv.yml')
+        cd._merge_dependencies(automl_deps)
+        cd.save('deploy/myenv.yml')
+
         # add deployment step
         pp_group_column_names = PipelineParameter(
             "group_column_names",

how-to-use-azureml/automated-machine-learning/forecasting-grouping/deploy/deploy.py

@@ -1,10 +1,11 @@
 import argparse
-from azureml.core import Run, Model
-from azureml.core import Workspace
-from azureml.core.webservice import AciWebservice
-from azureml.core.model import InferenceConfig
 import json
 
+from azureml.core import Run, Model, Workspace
+from azureml.core.conda_dependencies import CondaDependencies
+from azureml.core.model import InferenceConfig
+from azureml.core.webservice import AciWebservice
+
 
 script_file_name = 'score.py'
 conda_env_file_name = 'myenv.yml'

how-to-use-azureml/automated-machine-learning/forecasting-grouping/deploy/myenv.yml

@@ -1,15 +1,11 @@
-name: project_environment
+name: automl_grouping_env
 dependencies:
   # The python interpreter version.
 
   # Currently Azure ML only supports 3.5.2 and later.
 
 - python=3.6.2
-
+- numpy>=1.16.0,<=1.16.2
-- pip:
+- scikit-learn>=0.19.0,<=0.20.3
-  - azureml-defaults
-  - azureml-train-automl
-- numpy
-- scikit-learn
 - conda-forge::fbprophet==0.5
 

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

@@ -448,7 +448,7 @@
         "#### Distribution forecasts\n",
         "\n",
         "Often the figure of interest is not just the point prediction, but the prediction at some quantile of the distribution. \n",
-        "This arises when the forecast is used to control some kind of inventory, for example of grocery items of virtual machines for a cloud service. In such case, the control point is usually something like \"we want the item to be in stock and not run out 99% of the time\". This is called a \"service level\". Here is how you get quantile forecasts."
+        "This arises when the forecast is used to control some kind of inventory, for example of grocery items or virtual machines for a cloud service. In such case, the control point is usually something like \"we want the item to be in stock and not run out 99% of the time\". This is called a \"service level\". Here is how you get quantile forecasts."
       ]
     },
     {
@@ -472,7 +472,7 @@
       "source": [
         "#### Destination-date forecast: \"just do something\"\n",
         "\n",
-        "In some scenarios, the X_test is not known. The forecast is likely to be weak, becaus it is missing contemporaneous predictors, which we will need to impute. If you still wish to predict forward under the assumption that the last known values will be carried forward, you can forecast out to \"destination date\". The destination date still needs to fit within the maximum horizon from training."
+        "In some scenarios, the X_test is not known. The forecast is likely to be weak, because it is missing contemporaneous predictors, which we will need to impute. If you still wish to predict forward under the assumption that the last known values will be carried forward, you can forecast out to \"destination date\". The destination date still needs to fit within the maximum horizon from training."
       ]
     },
     {
@@ -551,7 +551,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "How should we read that eror message? The forecast origin is at the last time themodel saw an actual values of `y` (the target). That was at the end of the training data! Because the model received all `NaN` (and not an actual target value), it is attempting to forecast from the end of training data. But the requested forecast periods are past the maximum horizon. We need to provide a define `y` value to establish the forecast origin.\n",
+        "How should we read that eror message? The forecast origin is at the last time the model saw an actual value of `y` (the target). That was at the end of the training data! Because the model received all `NaN` (and not an actual target value), it is attempting to forecast from the end of training data. But the requested forecast periods are past the maximum horizon. We need to provide a define `y` value to establish the forecast origin.\n",
         "\n",
         "We will use this helper function to take the required amount of context from the data preceding the testing data. It's definition is intentionally simplified to keep the idea in the clear."
       ]
@@ -711,7 +711,7 @@
     ],
     "category": "tutorial",
     "compute": [
-      "remote"
+      "Remote"
     ],
     "datasets": [
       "None"
@@ -746,7 +746,7 @@
       "Forecasting",
       "Confidence Intervals"
     ],
-    "task": "forecasting"
+    "task": "Forecasting"
   },
   "nbformat": 4,
   "nbformat_minor": 2

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

@@ -325,9 +325,9 @@
         "\n",
         "For forecasting tasks, there are some additional parameters that can be set: the name of the column holding the date/time, the grain column names, and the maximum forecast horizon. A time column is required for forecasting, while the grain is optional. If a grain is not given, AutoML assumes that the whole dataset is a single time-series. We also pass a list of columns to drop prior to modeling. The _logQuantity_ column is completely correlated with the target quantity, so it must be removed to prevent a target leak.\n",
         "\n",
-        "The forecast horizon is given in units of the time-series frequency; for instance, the OJ series frequency is weekly, so a horizon of 20 means that a trained model will estimate sales up-to 20 weeks beyond the latest date in the training data for each series. In this example, we set the maximum horizon to the number of samples per series in the test set (n_test_periods). Generally, the value of this parameter will be dictated by business needs. For example, a demand planning organizaion that needs to estimate the next month of sales would set the horizon accordingly. Please see the [energy_demand notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand) for more discussion of forecast horizon.\n",
+        "The forecast horizon is given in units of the time-series frequency; for instance, the OJ series frequency is weekly, so a horizon of 20 means that a trained model will estimate sales up to 20 weeks beyond the latest date in the training data for each series. In this example, we set the maximum horizon to the number of samples per series in the test set (n_test_periods). Generally, the value of this parameter will be dictated by business needs. For example, a demand planning organizaion that needs to estimate the next month of sales would set the horizon accordingly. Please see the [energy_demand notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand) for more discussion of forecast horizon.\n",
         "\n",
-        "Finally, a note about the cross-validation (CV) procedure for time-series data. AutoML uses out-of-sample error estimates to select a best pipeline/model, so it is important that the CV fold splitting is done correctly. Time-series can violate the basic statistical assumptions of the canonical K-Fold CV strategy, so AutoML implements a [rolling origin validation](https://robjhyndman.com/hyndsight/tscv/) procedure to create CV folds for time-series data. To use this procedure, you just need to specify the desired number of CV folds in the AutoMLConfig object. It is also possible to bypass CV and use your own validation set by setting the *X_valid* and *y_valid* parameters of AutoMLConfig.\n",
+        "Finally, a note about the cross-validation (CV) procedure for time-series data. AutoML uses out-of-sample error estimates to select a best pipeline/model, so it is important that the CV fold splitting is done correctly. Time-series can violate the basic statistical assumptions of the canonical K-Fold CV strategy, so AutoML implements a [rolling origin validation](https://robjhyndman.com/hyndsight/tscv/) procedure to create CV folds for time-series data. To use this procedure, you just need to specify the desired number of CV folds in the AutoMLConfig object. It is also possible to bypass CV and use your own validation set by setting the *validation_data* parameter of AutoMLConfig.\n",
         "\n",
         "Here is a summary of AutoMLConfig parameters used for training the OJ model:\n",
         "\n",
@@ -693,7 +693,7 @@
     "category": "tutorial",
     "celltoolbar": "Raw Cell Format",
     "compute": [
-      "remote"
+      "Remote"
     ],
     "datasets": [
       "Orange Juice Sales"
@@ -705,6 +705,9 @@
     "framework": [
       "Azure ML AutoML"
     ],
+    "tags": [
+      "None"
+    ],
     "friendly_name": "Forecasting orange juice sales with deployment",
     "index_order": 1,
     "kernelspec": {

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

@@ -634,7 +634,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.train.automl.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations\n",
+        "from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations\n",
         "explainer_setup_class = automl_setup_model_explanations(fitted_model, 'regression', X_test=X_test)"
       ]
     },
@@ -653,11 +653,11 @@
       "outputs": [],
       "source": [
         "from azureml.explain.model._internal.explanation_client import ExplanationClient\n",
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard\n",
+        "from interpret_community.widget import ExplanationDashboard\n",
         "client = ExplanationClient.from_run(automl_run)\n",
         "engineered_explanations = client.download_model_explanation(raw=False)\n",
         "print(engineered_explanations.get_feature_importance_dict())\n",
-        "ExplanationDashboard(engineered_explanations, explainer_setup_class.automl_estimator, explainer_setup_class.X_test_transform)"
+        "ExplanationDashboard(engineered_explanations, explainer_setup_class.automl_estimator, datasetX=explainer_setup_class.X_test_transform)"
       ]
     },
     {
@@ -676,7 +676,7 @@
       "source": [
         "raw_explanations = client.download_model_explanation(raw=True)\n",
         "print(raw_explanations.get_feature_importance_dict())\n",
-        "ExplanationDashboard(raw_explanations, explainer_setup_class.automl_pipeline, explainer_setup_class.X_test_raw)"
+        "ExplanationDashboard(raw_explanations, explainer_setup_class.automl_pipeline, datasetX=explainer_setup_class.X_test_raw)"
       ]
     },
     {

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

@@ -5,7 +5,8 @@ import os
 import pickle
 import azureml.train.automl
 import azureml.explain.model
-from azureml.train.automl.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations
+from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, \
+    automl_setup_model_explanations
 from sklearn.externals import joblib
 from azureml.core.model import Model
 

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

@@ -6,7 +6,8 @@ from azureml.core.run import Run
 from azureml.core.experiment import Experiment
 from sklearn.externals import joblib
 from azureml.core.dataset import Dataset
-from azureml.train.automl.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations
+from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, \
+    automl_setup_model_explanations
 from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel
 from azureml.explain.model.mimic_wrapper import MimicWrapper
 from automl.client.core.common.constants import MODEL_PATH

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

@@ -140,6 +140,9 @@
     "framework": [
       "Azure ML AutoML"
     ],
+    "tags": [
+      ""
+    ],
     "friendly_name": "Forecasting with automated ML SQL integration",
     "index_order": 1,
     "kernelspec": {
@@ -151,9 +154,6 @@
       "name": "sql",
       "version": ""
     },
-    "tags": [
-      ""
-    ],
     "task": "Forecasting"
   },
   "nbformat": 4,

how-to-use-azureml/automated-machine-learning/sql-server/setup/auto-ml-sql-setup.ipynb

@@ -560,6 +560,9 @@
     "framework": [
       "Azure ML AutoML"
     ],
+    "tags": [
+      ""
+    ],
     "friendly_name": "Setup automated ML SQL integration",
     "index_order": 1,
     "kernelspec": {
@@ -571,9 +574,6 @@
       "name": "sql",
       "version": ""
     },
-    "tags": [
-      ""
-    ],
     "task": "None"
   },
   "nbformat": 4,

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

@@ -175,6 +175,7 @@
       "source": [
         "#deploy to ACI\n",
         "from azureml.core.webservice import AciWebservice, Webservice\n",
+        "from azureml.exceptions import WebserviceException\n",
         "from azureml.core.model import InferenceConfig\n",
         "\n",
         "myaci_config = AciWebservice.deploy_configuration(cpu_cores = 2, \n",
@@ -182,11 +183,19 @@
         "                                                  tags = {'name':'Databricks Azure ML ACI'}, \n",
         "                                                  description = 'This is for ADB and AML example.')\n",
         "\n",
+        "service_name = 'aciws'\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",
         "inference_config = InferenceConfig(runtime= 'spark-py', \n",
         "                                   entry_script='score_sparkml.py',\n",
         "                                   conda_file='mydeployenv.yml')\n",
         "\n",
-        "myservice = Model.deploy(ws, 'aciws', [mymodel], inference_config, myaci_config)\n",
+        "myservice = Model.deploy(ws, service_name, [mymodel], inference_config, myaci_config)\n",
         "myservice.wait_for_deployment(show_output=True)"
       ]
     },
@@ -199,18 +208,6 @@
         "help(Webservice)"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# List images by ws\n",
-        "\n",
-        "for i in ContainerImage.list(workspace = ws):\n",
-        "    print('{}(v.{} [{}]) stored at {} with build log {}'.format(i.name, i.version, i.creation_state, i.image_location, i.image_build_log_uri))"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,

how-to-use-azureml/azure-databricks/amlsdk/deploy-to-aks-05.ipynb

@@ -163,12 +163,17 @@
         "#it may take 20-25 minutes to create a new cluster\n",
         "\n",
         "from azureml.core.compute import AksCompute, ComputeTarget\n",
-        "\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
-        "# Use the default configuration (can also provide parameters to customize)\n",
-        "prov_config = AksCompute.provisioning_configuration()\n",
         "\n",
         "aks_name = 'ps-aks-demo2' \n",
         "\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",
@@ -188,15 +193,24 @@
       "source": [
         "#deploy to AKS\n",
         "from azureml.core.webservice import AksWebservice, Webservice\n",
+        "from azureml.exceptions import WebserviceException\n",
         "from azureml.core.model import InferenceConfig\n",
         "\n",
         "aks_config = AksWebservice.deploy_configuration(enable_app_insights=True)\n",
         "\n",
+        "service_name = 'ps-aks-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",
         "inference_config = InferenceConfig(runtime = 'spark-py', \n",
         "                                   entry_script ='score_sparkml.py',\n",
         "                                   conda_file ='mydeployenv.yml')\n",
         "\n",
-        "aks_service = Model.deploy(ws, 'ps-aks-service', [mymodel], inference_config, aks_config, aks_target)\n",
+        "aks_service = Model.deploy(ws, service_name, [mymodel], inference_config, aks_config, aks_target)\n",
         "aks_service.wait_for_deployment(show_output=True)"
       ]
     },
@@ -288,7 +302,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.6"
+      "version": "3.6.8"
     },
     "name": "deploy-to-aks-existingimage-05",
     "notebookId": 1030695628045968

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

@@ -661,6 +661,7 @@
         "# this will take 10-15 minutes to finish\n",
         "\n",
         "from azureml.core.webservice import AciWebservice, Webservice\n",
+        "from azureml.exceptions import WebserviceException\n",
         "from azureml.core.model import InferenceConfig\n",
         "from azureml.core.model import Model\n",
         "import uuid\n",
@@ -677,6 +678,13 @@
         "\n",
         "guid = str(uuid.uuid4()).split(\"-\")[0]\n",
         "service_name = \"myservice-{}\".format(guid)\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",
         "print(\"Creating service with name: {}\".format(service_name))\n",
         "\n",
         "myservice = Model.deploy(ws, service_name, [model], inference_config, myaci_config)\n",
@@ -795,7 +803,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.5"
+      "version": "3.6.8"
     },
     "name": "auto-ml-classification-local-adb",
     "notebookId": 2733885892129020

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

@@ -116,7 +116,8 @@
       "execution_count": null,
       "metadata": {
         "tags": [
-          "register model from file"
+          "register model from file",
+          "sample-model-register"
         ]
       },
       "outputs": [],

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

@@ -96,7 +96,8 @@
       "execution_count": null,
       "metadata": {
         "tags": [
-          "register model from file"
+          "register model from file",
+          "sample-model-register"
         ]
       },
       "outputs": [],

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

@@ -345,9 +345,11 @@
     ],
     "category": "tutorial",
     "compute": [
-      "local"
+      "Local"
+    ],
+    "datasets": [
+      "None"
     ],
-    "datasets": [],
     "deployment": [
       "Local"
     ],

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

@@ -431,7 +431,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "aks_service.update(enable_app_insights=False)"
+        "aks_service.update(enable_app_insights=False)\n",
+        "aks_service.wait_for_deployment(show_output = True)"
       ]
     },
     {

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

@@ -755,7 +755,7 @@
     ],
     "category": "deployment",
     "compute": [
-      "local"
+      "Local"
     ],
     "datasets": [
       "Emotion FER"

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

@@ -763,7 +763,7 @@
     ],
     "category": "deployment",
     "compute": [
-      "local"
+      "Local"
     ],
     "datasets": [
       "MNIST"

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

@@ -373,7 +373,7 @@
     ],
     "category": "deployment",
     "compute": [
-      "local"
+      "Local"
     ],
     "datasets": [
       "ImageNet"

how-to-use-azureml/explain-model/README.md

@@ -1,11 +1,14 @@
-## Using explain model APIs
+## Using AzureML Interpret APIs
 
 <a name="samples"></a>
-# Explain Model SDK Sample Notebooks
+# AzureML Interpret SDK Sample Notebooks
 
-Follow these sample notebooks to learn:
+You can run the interpret-community SDK to explain models locally without Azure.
+For notebooks on the local experience, please see:
+https://github.com/interpretml/interpret-community/tree/master/notebooks
 
-1. [Explain tabular data locally](tabular-data): Basic examples of explaining model trained on tabular data.
+Follow these sample notebooks to learn about the model interpretability integration with Azure:
-2. [Explain on remote AMLCompute](azure-integration/remote-explanation): Explain a model on a remote AMLCompute target.
+
-3. [Explain tabular data with Run History](azure-integration/run-history): Explain a model with Run History.
+1. [Explain on remote AMLCompute](azure-integration/remote-explanation): Explain a model on a remote AMLCompute target.
-4. [Operationalize model explanation](azure-integration/scoring-time): Operationalize model explanation as a web service.
+2. [Explain tabular data with Run History](azure-integration/run-history): Explain a model with Run History.
+3. [Operationalize model explanation](azure-integration/scoring-time): Operationalize model explanation as a web service.

how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb

@@ -669,7 +669,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard"
+        "from interpret_community.widget import ExplanationDashboard"
       ]
     },
     {
@@ -678,7 +678,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ExplanationDashboard(global_explanation, original_model, x_test)"
+        "ExplanationDashboard(global_explanation, original_model, datasetX=x_test)"
       ]
     },
     {

how-to-use-azureml/explain-model/azure-integration/remote-explanation/train_explain.py

@@ -61,4 +61,4 @@ global_explanation = tabular_explainer.explain_global(X_test)
 # Uploading model explanation data for storage or visualization in webUX
 # The explanation can then be downloaded on any compute
 comment = 'Global explanation on regression model trained on boston dataset'
-client.upload_model_explanation(global_explanation, comment=comment)
+client.upload_model_explanation(global_explanation, comment=comment, model_id=original_model.id)

how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.ipynb

@@ -564,7 +564,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard"
+        "from interpret_community.widget import ExplanationDashboard"
       ]
     },
     {
@@ -573,7 +573,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ExplanationDashboard(downloaded_global_explanation, model, x_test)"
+        "ExplanationDashboard(downloaded_global_explanation, model, datasetX=x_test)"
       ]
     },
     {

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb

@@ -290,7 +290,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard"
+        "from interpret_community.widget import ExplanationDashboard"
       ]
     },
     {
@@ -299,7 +299,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ExplanationDashboard(global_explanation, clf, x_test)"
+        "ExplanationDashboard(global_explanation, clf, datasetX=x_test)"
       ]
     },
     {

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb

@@ -355,7 +355,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard"
+        "from interpret_community.widget import ExplanationDashboard"
       ]
     },
     {
@@ -364,7 +364,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "ExplanationDashboard(global_explanation, original_svm_model, x_test)"
+        "ExplanationDashboard(global_explanation, original_svm_model, datasetX=x_test)"
       ]
     },
     {

how-to-use-azureml/explain-model/azure-integration/scoring-time/train_explain.py

@@ -116,7 +116,7 @@ global_explanation = tabular_explainer.explain_global(x_test)
 
 # uploading model explanation data for storage or visualization
 comment = 'Global explanation on classification model trained on IBM employee attrition dataset'
-client.upload_model_explanation(global_explanation, comment=comment)
+client.upload_model_explanation(global_explanation, comment=comment, model_id=original_model.id)
 
 # also create a lightweight explainer for scoring time
 scoring_explainer = LinearScoringExplainer(tabular_explainer)

how-to-use-azureml/explain-model/tabular-data/advanced-feature-transformations-explain-local.ipynb

@@ -1,509 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/tabular-data/advanced-feature-transformations-explain-local.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Explain binary classification model predictions with raw feature transformations\n",
-        "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to explain and visualize a binary classification model that uses advanced many to one or many to many feature transformations.**_\n",
-        "\n",
-        "\n",
-        "\n",
-        "## Table of Contents\n",
-        "\n",
-        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#Setup)\n",
-        "1. [Run model explainer locally at training time](#Explain)\n",
-        "    1. Apply feature transformations\n",
-        "    1. Train a binary classification model\n",
-        "    1. Explain the model on raw features\n",
-        "        1. Generate global explanations\n",
-        "        1. Generate local explanations\n",
-        "1. [Visualize results](#Visualize)\n",
-        "1. [Next steps](#Next)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Introduction\n",
-        "\n",
-        "This notebook illustrates creating explanations for a binary classification model, Titanic passenger data classification, that uses many to one and many to many feature transformations from raw data to engineered features. For the many to one transformation, we sum 2 features `age` and `fare`. For many to many transformations two features are computed: one that is product of `age` and `fare` and another that is square of this product. Our tabular data explainer is then used to get the explanation object with the flag `allow_all_transformations` passed. The object is then used to get raw feature importances.\n",
-        "\n",
-        "\n",
-        "We will showcase raw feature transformations with three tabular data explainers: TabularExplainer (SHAP), MimicExplainer (global surrogate), and PFIExplainer.\n",
-        "\n",
-        "| ![Interpretability Toolkit Architecture](./img/interpretability-architecture.png) |\n",
-        "|:--:|\n",
-        "| *Interpretability Toolkit Architecture* |\n",
-        "\n",
-        "Problem: Titanic passenger data classification with scikit-learn (run model explainer locally)\n",
-        "\n",
-        "1. Transform raw features to engineered features\n",
-        "2. Train a Logistic Regression model using Scikit-learn\n",
-        "3. Run 'explain_model' globally and locally with full dataset in local mode, which doesn't contact any Azure services.\n",
-        "4. Visualize the global and local explanations with the visualization dashboard.\n",
-        "---\n",
-        "\n",
-        "Setup: If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
-        "If you are using Jupyter Labs run the following command:\n",
-        "```\n",
-        "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
-        "```\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain\n",
-        "\n",
-        "### Run model explainer locally at training time"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.pipeline import Pipeline\n",
-        "from sklearn.impute import SimpleImputer\n",
-        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
-        "from sklearn.linear_model import LogisticRegression\n",
-        "import pandas as pd\n",
-        "import numpy as np\n",
-        "\n",
-        "# Explainers:\n",
-        "# 1. SHAP Tabular Explainer\n",
-        "from interpret.ext.blackbox import TabularExplainer\n",
-        "\n",
-        "# OR\n",
-        "\n",
-        "# 2. Mimic Explainer\n",
-        "from interpret.ext.blackbox import MimicExplainer\n",
-        "# You can use one of the following four interpretable models as a global surrogate to the black box model\n",
-        "from interpret.ext.glassbox import LGBMExplainableModel\n",
-        "from interpret.ext.glassbox import LinearExplainableModel\n",
-        "from interpret.ext.glassbox import SGDExplainableModel\n",
-        "from interpret.ext.glassbox import DecisionTreeExplainableModel\n",
-        "\n",
-        "# OR\n",
-        "\n",
-        "# 3. PFI Explainer\n",
-        "from interpret.ext.blackbox import PFIExplainer "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Load the Titanic passenger data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "titanic_url = ('https://raw.githubusercontent.com/amueller/'\n",
-        "               'scipy-2017-sklearn/091d371/notebooks/datasets/titanic3.csv')\n",
-        "data = pd.read_csv(titanic_url)\n",
-        "# fill missing values\n",
-        "data = data.fillna(method=\"ffill\")\n",
-        "data = data.fillna(method=\"bfill\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Similar to example [here](https://scikit-learn.org/stable/auto_examples/compose/plot_column_transformer_mixed_types.html#sphx-glr-auto-examples-compose-plot-column-transformer-mixed-types-py), use a subset of columns"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.model_selection import train_test_split\n",
-        "\n",
-        "numeric_features = ['age', 'fare']\n",
-        "categorical_features = ['embarked', 'sex', 'pclass']\n",
-        "\n",
-        "y = data['survived'].values\n",
-        "X = data[categorical_features + numeric_features]\n",
-        "\n",
-        "# Split data into train and test\n",
-        "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Transform raw features"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can explain raw features by either using a `sklearn.compose.ColumnTransformer` or a list of fitted transformer tuples. The cell below uses `sklearn.compose.ColumnTransformer`. In case you want to run the example with the list of fitted transformer tuples, comment the cell below and uncomment the cell that follows after. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# We add many to one and many to many transformations for illustration purposes.\n",
-        "# The support for raw feature explanations with many to one and many to many transformations are only supported \n",
-        "# When allow_all_transformations is set to True on explainer creation\n",
-        "from sklearn.preprocessing import FunctionTransformer\n",
-        "many_to_one_transformer = FunctionTransformer(lambda x: x.sum(axis=1).reshape(-1, 1))\n",
-        "many_to_many_transformer = FunctionTransformer(lambda x: np.hstack(\n",
-        "    (np.prod(x, axis=1).reshape(-1, 1), (np.prod(x, axis=1)**2).reshape(-1, 1))\n",
-        "))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.compose import ColumnTransformer\n",
-        "\n",
-        "transformations = ColumnTransformer([\n",
-        "    (\"age_fare_1\", Pipeline(steps=[\n",
-        "        ('imputer', SimpleImputer(strategy='median')),\n",
-        "        ('scaler', StandardScaler())\n",
-        "    ]), [\"age\", \"fare\"]),\n",
-        "    (\"age_fare_2\", many_to_one_transformer, [\"age\", \"fare\"]),\n",
-        "    (\"age_fare_3\", many_to_many_transformer, [\"age\", \"fare\"]),\n",
-        "    (\"embarked\", Pipeline(steps=[\n",
-        "        (\"imputer\", SimpleImputer(strategy='constant', fill_value='missing')), \n",
-        "        (\"encoder\", OneHotEncoder(sparse=False))]), [\"embarked\"]),\n",
-        "    (\"sex_pclass\", OneHotEncoder(sparse=False), [\"sex\", \"pclass\"])    \n",
-        "])\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "'''\n",
-        "# Uncomment below if sklearn-pandas is not installed\n",
-        "#!pip install sklearn-pandas\n",
-        "from sklearn_pandas import DataFrameMapper\n",
-        "\n",
-        "# Impute, standardize the numeric features and one-hot encode the categorical features.    \n",
-        "\n",
-        "transformations = [\n",
-        "    ([\"age\", \"fare\"], Pipeline(steps=[\n",
-        "        ('imputer', SimpleImputer(strategy='median')),\n",
-        "        ('scaler', StandardScaler())\n",
-        "    ])),\n",
-        "    ([\"age\", \"fare\"], many_to_one_transformer),\n",
-        "    ([\"age\", \"fare\"], many_to_many_transformer),\n",
-        "    ([\"embarked\"], Pipeline(steps=[\n",
-        "        (\"imputer\", SimpleImputer(strategy='constant', fill_value='missing')), \n",
-        "        (\"encoder\", OneHotEncoder(sparse=False))])),\n",
-        "    ([\"sex\", \"pclass\"], OneHotEncoder(sparse=False))    \n",
-        "]\n",
-        "\n",
-        "\n",
-        "# Append classifier to preprocessing pipeline.\n",
-        "# Now we have a full prediction pipeline.\n",
-        "clf = Pipeline(steps=[('preprocessor', DataFrameMapper(transformations)),\n",
-        "                      ('classifier', LogisticRegression(solver='lbfgs'))])\n",
-        "'''"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Train a Logistic Regression model, which you want to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Append classifier to preprocessing pipeline.\n",
-        "# Now we have a full prediction pipeline.\n",
-        "clf = Pipeline(steps=[('preprocessor', transformations),\n",
-        "                      ('classifier', LogisticRegression(solver='lbfgs'))])\n",
-        "model = clf.fit(x_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Explain predictions on your local machine"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# 1. Using SHAP TabularExplainer\n",
-        "# When the last parameter allow_all_transformations is passed, we handle many to one and many to many transformations to \n",
-        "# generate approximations to raw feature importances. When this flag is passed, for transformations not recognized as one to \n",
-        "# many, we distribute feature importances evenly to raw features generating them.\n",
-        "# clf.steps[-1][1] returns the trained classification model\n",
-        "explainer = TabularExplainer(clf.steps[-1][1], \n",
-        "                             initialization_examples=x_train, \n",
-        "                             features=x_train.columns, \n",
-        "                             transformations=transformations, \n",
-        "                             allow_all_transformations=True)\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "# 2. Using MimicExplainer\n",
-        "# augment_data is optional and if true, oversamples the initialization examples to improve surrogate model accuracy to fit original model.  Useful for high-dimensional data where the number of rows is less than the number of columns. \n",
-        "# max_num_of_augmentations is optional and defines max number of times we can increase the input data size.\n",
-        "# LGBMExplainableModel can be replaced with LinearExplainableModel, SGDExplainableModel, or DecisionTreeExplainableModel\n",
-        "# explainer = MimicExplainer(clf.steps[-1][1], \n",
-        "#                            x_train, \n",
-        "#                            LGBMExplainableModel, \n",
-        "#                            augment_data=True, \n",
-        "#                            max_num_of_augmentations=10, \n",
-        "#                            features=x_train.columns, \n",
-        "#                            transformations=transformations, \n",
-        "#                            allow_all_transformations=True)\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "# 3. Using PFIExplainer\n",
-        "\n",
-        "# Use the parameter \"metric\" to pass a metric name or function to evaluate the permutation. \n",
-        "# Note that if a metric function is provided a higher value must be better.\n",
-        "# Otherwise, take the negative of the function or set the parameter \"is_error_metric\" to True.\n",
-        "# Default metrics: \n",
-        "# F1 Score for binary classification, F1 Score with micro average for multiclass classification and\n",
-        "# Mean absolute error for regression\n",
-        "\n",
-        "\n",
-        "# explainer = PFIExplainer(clf.steps[-1][1], \n",
-        "#                          features=x_train.columns, \n",
-        "#                          transformations=transformations)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Generate global explanations\n",
-        "Explain overall model predictions (global explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
-        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
-        "\n",
-        "global_explanation = explainer.explain_global(x_test)\n",
-        "\n",
-        "# Note: if you used the PFIExplainer in the previous step, use the next line of code instead\n",
-        "# global_explanation = explainer.explain_global(x_test, true_labels=y_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Sorted SHAP values\n",
-        "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
-        "# Corresponding feature names\n",
-        "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
-        "# Feature ranks (based on original order of features)\n",
-        "print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n",
-        "# Per class feature names\n",
-        "print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n",
-        "# Per class feature importance values\n",
-        "print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Print out a dictionary that holds the sorted feature importance names and values\n",
-        "print('global importance rank: {}'.format(global_explanation.get_feature_importance_dict()))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Explain overall model predictions as a collection of local (instance-level) explanations"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# feature shap values for all features and all data points in the training data\n",
-        "print('local importance values: {}'.format(global_explanation.local_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Generate local explanations\n",
-        "Explain local data points (individual instances)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Note: PFIExplainer does not support local explanations\n",
-        "# You can pass a specific data point or a group of data points to the explain_local function\n",
-        "\n",
-        "# E.g., Explain the first data point in the test set\n",
-        "instance_num = 1\n",
-        "local_explanation = explainer.explain_local(x_test[:instance_num])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Get the prediction for the first member of the test set and explain why model made that prediction\n",
-        "prediction_value = clf.predict(x_test)[instance_num]\n",
-        "\n",
-        "sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
-        "sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
-        "\n",
-        "print('local importance values: {}'.format(sorted_local_importance_values))\n",
-        "print('local importance names: {}'.format(sorted_local_importance_names))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Visualize\n",
-        "Load the visualization dashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ExplanationDashboard(global_explanation, model, x_test)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Next\n",
-        "Learn about other use cases of the explain package on a:\n",
-        "       \n",
-        "1. [Training time: regression problem](./explain-regression-local.ipynb)\n",
-        "1. [Training time: binary classification problem](./explain-binary-classification-local.ipynb)\n",
-        "1. [Training time: multiclass classification problem](./explain-multiclass-classification-local.ipynb)\n",
-        "1. [Explain models with simple feature transformations](./simple-feature-transformations-explain-local.ipynb)\n",
-        "1. [Save model explanations via Azure Machine Learning Run History](../azure-integration/run-history/save-retrieve-explanations-run-history.ipynb)\n",
-        "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb)\n",
-        "1. Inferencing time: deploy a classification model and explainer:\n",
-        "    1. [Deploy a locally-trained model and explainer](../azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",
-        "    1. [Deploy a remotely-trained model and explainer](../azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "mesameki"
-      }
-    ],
-    "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/explain-model/tabular-data/advanced-feature-transformations-explain-local.yml

@@ -1,9 +0,0 @@
-name: advanced-feature-transformations-explain-local
-dependencies:
-- pip:
-  - azureml-sdk
-  - interpret
-  - azureml-interpret
-  - azureml-contrib-interpret
-  - sklearn-pandas
-  - ipywidgets

how-to-use-azureml/explain-model/tabular-data/explain-binary-classification-local.ipynb

@@ -1,390 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/tabular-data/explain-binary-classification-local.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Explain binary classification model predictions\n",
-        "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to explain and visualize a binary classification model predictions.**_\n",
-        "\n",
-        "\n",
-        "## Table of Contents\n",
-        "\n",
-        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#Setup)\n",
-        "1. [Run model explainer locally at training time](#Explain)\n",
-        "    1. Train a binary classification model\n",
-        "    1. Explain the model\n",
-        "        1. Generate global explanations\n",
-        "        1. Generate local explanations\n",
-        "1. [Visualize results](#Visualize)\n",
-        "1. [Next steps](#Next)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Introduction\n",
-        "\n",
-        "This notebook illustrates how to explain a binary classification model predictions locally at training time without contacting any Azure services.\n",
-        "It demonstrates the API calls that you need to make to get the global and local explanations and a visualization dashboard that provides an interactive way of discovering patterns in data and explanations.\n",
-        "\n",
-        "We will showcase three tabular data explainers: TabularExplainer (SHAP), MimicExplainer (global surrogate), and PFIExplainer.\n",
-        "\n",
-        "| ![Interpretability Toolkit Architecture](./img/interpretability-architecture.png) |\n",
-        "|:--:|\n",
-        "| *Interpretability Toolkit Architecture* |\n",
-        "\n",
-        "Problem: Breast cancer diagnosis classification with scikit-learn (run model explainer locally)\n",
-        "\n",
-        "1. Train a SVM classification model using Scikit-learn\n",
-        "2. Run 'explain_model' globally and locally with full dataset in local mode, which doesn't contact any Azure services.\n",
-        "3. Visualize the global and local explanations with the visualization dashboard.\n",
-        "---\n",
-        "\n",
-        "Setup: If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
-        "If you are using Jupyter Labs run the following command:\n",
-        "```\n",
-        "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
-        "```\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain\n",
-        "\n",
-        "### Run model explainer locally at training time"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.datasets import load_breast_cancer\n",
-        "from sklearn import svm\n",
-        "\n",
-        "# Explainers:\n",
-        "# 1. SHAP Tabular Explainer\n",
-        "from interpret.ext.blackbox import TabularExplainer\n",
-        "\n",
-        "# OR\n",
-        "\n",
-        "# 2. Mimic Explainer\n",
-        "from interpret.ext.blackbox import MimicExplainer\n",
-        "# You can use one of the following four interpretable models as a global surrogate to the black box model\n",
-        "from interpret.ext.glassbox import LGBMExplainableModel\n",
-        "from interpret.ext.glassbox import LinearExplainableModel\n",
-        "from interpret.ext.glassbox import SGDExplainableModel\n",
-        "from interpret.ext.glassbox import DecisionTreeExplainableModel\n",
-        "\n",
-        "# OR\n",
-        "\n",
-        "# 3. PFI Explainer\n",
-        "from interpret.ext.blackbox import PFIExplainer "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Load the breast cancer diagnosis data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "breast_cancer_data = load_breast_cancer()\n",
-        "classes = breast_cancer_data.target_names.tolist()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Split data into train and test\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "x_train, x_test, y_train, y_test = train_test_split(breast_cancer_data.data, breast_cancer_data.target, test_size=0.2, random_state=0)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Train a SVM classification model, which you want to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "clf = svm.SVC(gamma=0.001, C=100., probability=True)\n",
-        "model = clf.fit(x_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Explain predictions on your local machine"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# 1. Using SHAP TabularExplainer\n",
-        "explainer = TabularExplainer(model, \n",
-        "                             x_train, \n",
-        "                             features=breast_cancer_data.feature_names, \n",
-        "                             classes=classes)\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "# 2. Using MimicExplainer\n",
-        "# augment_data is optional and if true, oversamples the initialization examples to improve surrogate model accuracy to fit original model.  Useful for high-dimensional data where the number of rows is less than the number of columns. \n",
-        "# max_num_of_augmentations is optional and defines max number of times we can increase the input data size.\n",
-        "# LGBMExplainableModel can be replaced with LinearExplainableModel, SGDExplainableModel, or DecisionTreeExplainableModel\n",
-        "# explainer = MimicExplainer(model, \n",
-        "#                            x_train, \n",
-        "#                            LGBMExplainableModel, \n",
-        "#                            augment_data=True, \n",
-        "#                            max_num_of_augmentations=10, \n",
-        "#                            features=breast_cancer_data.feature_names, \n",
-        "#                            classes=classes)\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "# 3. Using PFIExplainer\n",
-        "\n",
-        "# Use the parameter \"metric\" to pass a metric name or function to evaluate the permutation. \n",
-        "# Note that if a metric function is provided a higher value must be better.\n",
-        "# Otherwise, take the negative of the function or set the parameter \"is_error_metric\" to True.\n",
-        "# Default metrics: \n",
-        "# F1 Score for binary classification, F1 Score with micro average for multiclass classification and\n",
-        "# Mean absolute error for regression\n",
-        "\n",
-        "# explainer = PFIExplainer(model, \n",
-        "#                          features=breast_cancer_data.feature_names, \n",
-        "#                          classes=classes)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Generate global explanations\n",
-        "Explain overall model predictions (global explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
-        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
-        "global_explanation = explainer.explain_global(x_test)\n",
-        "\n",
-        "# Note: if you used the PFIExplainer in the previous step, use the next line of code instead\n",
-        "# global_explanation = explainer.explain_global(x_test, true_labels=y_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Sorted SHAP values\n",
-        "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
-        "# Corresponding feature names\n",
-        "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
-        "# Feature ranks (based on original order of features)\n",
-        "print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n",
-        "\n",
-        "# Note: PFIExplainer does not support per class explanations\n",
-        "# Per class feature names\n",
-        "print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n",
-        "# Per class feature importance values\n",
-        "print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Print out a dictionary that holds the sorted feature importance names and values\n",
-        "print('global importance rank: {}'.format(global_explanation.get_feature_importance_dict()))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Explain overall model predictions as a collection of local (instance-level) explanations"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# feature shap values for all features and all data points in the training data\n",
-        "print('local importance values: {}'.format(global_explanation.local_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Generate local explanations\n",
-        "Explain local data points (individual instances)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Note: PFIExplainer does not support local explanations\n",
-        "# You can pass a specific data point or a group of data points to the explain_local function\n",
-        "\n",
-        "# E.g., Explain the first data point in the test set\n",
-        "instance_num = 0\n",
-        "local_explanation = explainer.explain_local(x_test[instance_num,:])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Get the prediction for the first member of the test set and explain why model made that prediction\n",
-        "prediction_value = clf.predict(x_test)[instance_num]\n",
-        "\n",
-        "sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
-        "sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
-        "\n",
-        "print('local importance values: {}'.format(sorted_local_importance_values))\n",
-        "print('local importance names: {}'.format(sorted_local_importance_names))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Visualize\n",
-        "Load the visualization dashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ExplanationDashboard(global_explanation, model, x_test)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Next\n",
-        "Learn about other use cases of the explain package on a:\n",
-        "       \n",
-        "1. [Training time: regression problem](./explain-regression-local.ipynb)\n",
-        "1. [Training time: multiclass classification problem](./explain-multiclass-classification-local.ipynb)\n",
-        "1. Explain models with engineered features:\n",
-        "    1. [Simple feature transformations](./simple-feature-transformations-explain-local.ipynb)\n",
-        "    1. [Advanced feature transformations](./advanced-feature-transformations-explain-local.ipynb)\n",
-        "1. [Save model explanations via Azure Machine Learning Run History](../azure-integration/run-history/save-retrieve-explanations-run-history.ipynb)\n",
-        "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb)\n",
-        "1. Inferencing time: deploy a classification model and explainer:\n",
-        "    1. [Deploy a locally-trained model and explainer](../azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",
-        "    1. [Deploy a remotely-trained model and explainer](../azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "mesameki"
-      }
-    ],
-    "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/explain-model/tabular-data/explain-binary-classification-local.yml

@@ -1,8 +0,0 @@
-name: explain-binary-classification-local
-dependencies:
-- pip:
-  - azureml-sdk
-  - interpret
-  - azureml-interpret
-  - azureml-contrib-interpret
-  - ipywidgets

how-to-use-azureml/explain-model/tabular-data/explain-multiclass-classification-local.ipynb

@@ -1,388 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/tabular-data/explain-multiclass-classification-local.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Explain multiclass classification model's predictions\n",
-        "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to explain and visualize a multiclass classification model predictions.**_\n",
-        "\n",
-        "\n",
-        "\n",
-        "## Table of Contents\n",
-        "\n",
-        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#Setup)\n",
-        "1. [Run model explainer locally at training time](#Explain)\n",
-        "    1. Train a multiclass classification model\n",
-        "    1. Explain the model\n",
-        "        1. Generate global explanations\n",
-        "        1. Generate local explanations\n",
-        "1. [Visualize results](#Visualize)\n",
-        "1. [Next steps](#Next)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Introduction\n",
-        "\n",
-        "This notebook illustrates how to explain a multiclass classification model predictions locally at training time without contacting any Azure services.\n",
-        "It demonstrates the API calls that you need to make to get the global and local explanations and a visualization dashboard that provides an interactive way of discovering patterns in data and explanations.\n",
-        "\n",
-        "We will showcase three tabular data explainers: TabularExplainer (SHAP), MimicExplainer (global surrogate), and PFIExplainer.\n",
-        "\n",
-        "| ![Interpretability Toolkit Architecture](./img/interpretability-architecture.png) |\n",
-        "|:--:|\n",
-        "| *Interpretability Toolkit Architecture* |\n",
-        "\n",
-        "Problem: Iris flower classification with scikit-learn (run model explainer locally)\n",
-        "\n",
-        "1. Train a SVM classification model using Scikit-learn\n",
-        "2. Run 'explain_model' globally and locally with full dataset in local mode, which doesn't contact any Azure services.\n",
-        "3. Visualize the global and local explanations with the visualization dashboard.\n",
-        "---\n",
-        "\n",
-        "Setup: If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
-        "If you are using Jupyter Labs run the following command:\n",
-        "```\n",
-        "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
-        "```\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain\n",
-        "\n",
-        "### Run model explainer locally at training time"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.datasets import load_iris\n",
-        "from sklearn import svm\n",
-        "\n",
-        "# Explainers:\n",
-        "# 1. SHAP Tabular Explainer\n",
-        "from interpret.ext.blackbox import TabularExplainer\n",
-        "\n",
-        "# OR\n",
-        "\n",
-        "# 2. Mimic Explainer\n",
-        "from interpret.ext.blackbox import MimicExplainer\n",
-        "# You can use one of the following four interpretable models as a global surrogate to the black box model\n",
-        "from interpret.ext.glassbox import LGBMExplainableModel\n",
-        "from interpret.ext.glassbox import LinearExplainableModel\n",
-        "from interpret.ext.glassbox import SGDExplainableModel\n",
-        "from interpret.ext.glassbox import DecisionTreeExplainableModel\n",
-        "\n",
-        "# OR\n",
-        "\n",
-        "# 3. PFI Explainer\n",
-        "from interpret.ext.blackbox import PFIExplainer "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Load the Iris flower dataset"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "iris = load_iris()\n",
-        "X = iris['data']\n",
-        "y = iris['target']\n",
-        "classes = iris['target_names']\n",
-        "feature_names = iris['feature_names']"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Split data into train and test\n",
-        "from sklearn.model_selection import train_test_split\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": [
-        "### Train a SVM classification model, which you want to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "clf = svm.SVC(gamma=0.001, C=100., probability=True)\n",
-        "model = clf.fit(x_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Explain predictions on your local machine"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# 1. Using SHAP TabularExplainer\n",
-        "explainer = TabularExplainer(model, \n",
-        "                             x_train, \n",
-        "                             features=feature_names, \n",
-        "                             classes=classes)\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "# 2. Using MimicExplainer\n",
-        "# augment_data is optional and if true, oversamples the initialization examples to improve surrogate model accuracy to fit original model.  Useful for high-dimensional data where the number of rows is less than the number of columns. \n",
-        "# max_num_of_augmentations is optional and defines max number of times we can increase the input data size.\n",
-        "# LGBMExplainableModel can be replaced with LinearExplainableModel, SGDExplainableModel, or DecisionTreeExplainableModel\n",
-        "# explainer = MimicExplainer(model, \n",
-        "#                            x_train, \n",
-        "#                            LGBMExplainableModel, \n",
-        "#                            augment_data=True, \n",
-        "#                            max_num_of_augmentations=10, \n",
-        "#                            features=feature_names, \n",
-        "#                            classes=classes)\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "# 3. Using PFIExplainer\n",
-        "\n",
-        "# Use the parameter \"metric\" to pass a metric name or function to evaluate the permutation. \n",
-        "# Note that if a metric function is provided a higher value must be better.\n",
-        "# Otherwise, take the negative of the function or set the parameter \"is_error_metric\" to True.\n",
-        "# Default metrics: \n",
-        "# F1 Score for binary classification, F1 Score with micro average for multiclass classification and\n",
-        "# Mean absolute error for regression\n",
-        "\n",
-        "# explainer = PFIExplainer(model, \n",
-        "#                          features=feature_names, \n",
-        "#                          classes=classes)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Generate global explanations\n",
-        "Explain overall model predictions (global explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
-        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
-        "global_explanation = explainer.explain_global(x_test)\n",
-        "\n",
-        "# Note: if you used the PFIExplainer in the previous step, use the next line of code instead\n",
-        "# global_explanation = explainer.explain_global(x_test, true_labels=y_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Sorted SHAP values\n",
-        "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
-        "# Corresponding feature names\n",
-        "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
-        "# Feature ranks (based on original order of features)\n",
-        "print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n",
-        "\n",
-        "# Note: PFIExplainer does not support per class explanations\n",
-        "# Per class feature names\n",
-        "print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n",
-        "# Per class feature importance values\n",
-        "print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Print out a dictionary that holds the sorted feature importance names and values\n",
-        "print('global importance rank: {}'.format(global_explanation.get_feature_importance_dict()))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Explain overall model predictions as a collection of local (instance-level) explanations"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# feature shap values for all features and all data points in the training data\n",
-        "print('local importance values: {}'.format(global_explanation.local_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Generate local explanations\n",
-        "Explain local data points (individual instances)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Note: PFIExplainer does not support local explanations\n",
-        "# You can pass a specific data point or a group of data points to the explain_local function\n",
-        "\n",
-        "# E.g., Explain the first data point in the test set\n",
-        "instance_num = 0\n",
-        "local_explanation = explainer.explain_local(x_test[instance_num,:])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Get the prediction for the first member of the test set and explain why model made that prediction\n",
-        "prediction_value = clf.predict(x_test)[instance_num]\n",
-        "\n",
-        "sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
-        "sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
-        "\n",
-        "print('local importance values: {}'.format(sorted_local_importance_values))\n",
-        "print('local importance names: {}'.format(sorted_local_importance_names))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Visualize\n",
-        "Load the visualization dashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ExplanationDashboard(global_explanation, model, x_test)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Next\n",
-        "Learn about other use cases of the explain package on a:\n",
-        "\n",
-        "1. [Training time: regression problem](./explain-regression-local.ipynb)       \n",
-        "1. [Training time: binary classification problem](./explain-binary-classification-local.ipynb)\n",
-        "1. Explain models with engineered features:\n",
-        "    1. [Simple feature transformations](./simple-feature-transformations-explain-local.ipynb)\n",
-        "    1. [Advanced feature transformations](./advanced-feature-transformations-explain-local.ipynb)\n",
-        "1. [Save model explanations via Azure Machine Learning Run History](../azure-integration/run-history/save-retrieve-explanations-run-history.ipynb)\n",
-        "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb)\n",
-        "1. Inferencing time: deploy a classification model and explainer:\n",
-        "    1. [Deploy a locally-trained model and explainer](../azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",
-        "    1. [Deploy a remotely-trained model and explainer](../azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)\n",
-        "\u00e2\u20ac\u2039\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "mesameki"
-      }
-    ],
-    "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/explain-model/tabular-data/explain-multiclass-classification-local.yml

@@ -1,8 +0,0 @@
-name: explain-multiclass-classification-local
-dependencies:
-- pip:
-  - azureml-sdk
-  - interpret
-  - azureml-interpret
-  - azureml-contrib-interpret
-  - ipywidgets

how-to-use-azureml/explain-model/tabular-data/explain-regression-local.ipynb

@@ -1,383 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/tabular-data/explain-regression-local.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Explain regression model predictions\n",
-        "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to explain and visualize a regression model predictions.**_\n",
-        "\n",
-        "\n",
-        "## Table of Contents\n",
-        "\n",
-        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#Setup)\n",
-        "1. [Run model explainer locally at training time](#Explain)\n",
-        "    1. Train a regressor model\n",
-        "    1. Explain the model\n",
-        "        1. Generate global explanations\n",
-        "        1. Generate local explanations\n",
-        "1. [Visualize results](#Visualize)\n",
-        "1. [Next steps](#Next)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Introduction\n",
-        "\n",
-        "This notebook illustrates how to explain regression model predictions locally at training time without contacting any Azure services.\n",
-        "It demonstrates the API calls that you need to make to get the global and local explanations and a visualization dashboard that provides an interactive way of discovering patterns in data and explanations.\n",
-        "\n",
-        "We will showcase three tabular data explainers: TabularExplainer (SHAP), MimicExplainer (global surrogate), and PFIExplainer.\n",
-        "\n",
-        "| ![Interpretability Toolkit Architecture](./img/interpretability-architecture.png) |\n",
-        "|:--:|\n",
-        "| *Interpretability Toolkit Architecture* |\n",
-        "\n",
-        "Problem: Boston Housing Price Prediction with scikit-learn (run model explainer locally)\n",
-        "\n",
-        "1. Train a GradientBoosting regression model using Scikit-learn\n",
-        "2. Run 'explain_model' globally and locally with full dataset in local mode, which doesn't contact any Azure services.\n",
-        "3. Visualize the global and local explanations with the visualization dashboard.\n",
-        "---\n",
-        "\n",
-        "Setup: If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
-        "If you are using Jupyter Labs run the following command:\n",
-        "```\n",
-        "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
-        "```\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain\n",
-        "\n",
-        "### Run model explainer locally at training time"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn import datasets\n",
-        "from sklearn.ensemble import GradientBoostingRegressor\n",
-        "\n",
-        "# Explainers:\n",
-        "# 1. SHAP Tabular Explainer\n",
-        "from interpret.ext.blackbox import TabularExplainer\n",
-        "\n",
-        "# OR\n",
-        "\n",
-        "# 2. Mimic Explainer\n",
-        "from interpret.ext.blackbox import MimicExplainer\n",
-        "# You can use one of the following four interpretable models as a global surrogate to the black box model\n",
-        "from interpret.ext.glassbox import LGBMExplainableModel\n",
-        "from interpret.ext.glassbox import LinearExplainableModel\n",
-        "from interpret.ext.glassbox import SGDExplainableModel\n",
-        "from interpret.ext.glassbox import DecisionTreeExplainableModel\n",
-        "\n",
-        "# OR\n",
-        "\n",
-        "# 3. PFI Explainer\n",
-        "from interpret.ext.blackbox import PFIExplainer "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Load the Boston house price data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "boston_data = datasets.load_boston()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Split data into train and test\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "x_train, x_test, y_train, y_test = train_test_split(boston_data.data, boston_data.target, test_size=0.2, random_state=0)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Train a GradientBoosting regression model, which you want to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "reg = GradientBoostingRegressor(n_estimators=100, max_depth=4,\n",
-        "                                learning_rate=0.1, loss='huber',\n",
-        "                                random_state=1)\n",
-        "model = reg.fit(x_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Explain predictions on your local machine"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# 1. Using SHAP TabularExplainer\n",
-        "explainer = TabularExplainer(model, \n",
-        "                             x_train, \n",
-        "                             features = boston_data.feature_names)\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "# 2. Using MimicExplainer\n",
-        "# augment_data is optional and if true, oversamples the initialization examples to improve surrogate model accuracy to fit original model.  Useful for high-dimensional data where the number of rows is less than the number of columns. \n",
-        "# max_num_of_augmentations is optional and defines max number of times we can increase the input data size.\n",
-        "# LGBMExplainableModel can be replaced with LinearExplainableModel, SGDExplainableModel, or DecisionTreeExplainableModel\n",
-        "# explainer = MimicExplainer(model, \n",
-        "#                            x_train, \n",
-        "#                            LGBMExplainableModel, \n",
-        "#                            augment_data=True, \n",
-        "#                            max_num_of_augmentations=10, \n",
-        "#                            features=boston_data.feature_names)\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "# 3. Using PFIExplainer\n",
-        "\n",
-        "# Use the parameter \"metric\" to pass a metric name or function to evaluate the permutation. \n",
-        "# Note that if a metric function is provided a higher value must be better.\n",
-        "# Otherwise, take the negative of the function or set the parameter \"is_error_metric\" to True.\n",
-        "# Default metrics: \n",
-        "# F1 Score for binary classification, F1 Score with micro average for multiclass classification and\n",
-        "# Mean absolute error for regression\n",
-        "\n",
-        "# explainer = PFIExplainer(model, \n",
-        "#                          features=boston_data.feature_names)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Generate global explanations\n",
-        "Explain overall model predictions (global explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
-        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
-        "global_explanation = explainer.explain_global(x_test)\n",
-        "\n",
-        "# Note: if you used the PFIExplainer in the previous step, use the next line of code instead\n",
-        "# global_explanation = explainer.explain_global(x_test, true_labels=y_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Sorted SHAP values \n",
-        "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
-        "# Corresponding feature names\n",
-        "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
-        "# Feature ranks (based on original order of features)\n",
-        "print('global importance rank: {}'.format(global_explanation.global_importance_rank))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Print out a dictionary that holds the sorted feature importance names and values\n",
-        "print('global importance rank: {}'.format(global_explanation.get_feature_importance_dict()))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Explain overall model predictions as a collection of local (instance-level) explanations"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Note: PFIExplainer does not support local explanations\n",
-        "# feature shap values for all features and all data points in the training data\n",
-        "print('local importance values: {}'.format(global_explanation.local_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Generate local explanations\n",
-        "Explain local data points (individual instances)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Note: PFIExplainer does not support local explanations\n",
-        "# You can pass a specific data point or a group of data points to the explain_local function\n",
-        "\n",
-        "# E.g., Explain the first data point in the test set\n",
-        "local_explanation = explainer.explain_local(x_test[0,:])\n",
-        "\n",
-        "# E.g., Explain the first five data points in the test set\n",
-        "# local_explanation_group = explainer.explain_local(x_test[0:4,:])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Sorted local feature importance information; reflects the original feature order\n",
-        "sorted_local_importance_names = local_explanation.get_ranked_local_names()\n",
-        "sorted_local_importance_values = local_explanation.get_ranked_local_values()\n",
-        "\n",
-        "print('sorted local importance names: {}'.format(sorted_local_importance_names))\n",
-        "print('sorted local importance values: {}'.format(sorted_local_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Visualize\n",
-        "Load the visualization dashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ExplanationDashboard(global_explanation, model, x_test)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Next\n",
-        "Learn about other use cases of the explain package on a:\n",
-        "       \n",
-        "1. [Training time: binary classification problem](./explain-binary-classification-local.ipynb)\n",
-        "1. [Training time: multiclass classification problem](./explain-multiclass-classification-local.ipynb)\n",
-        "1. Explain models with engineered features:\n",
-        "    1. [Simple feature transformations](./simple-feature-transformations-explain-local.ipynb)\n",
-        "    1. [Advanced feature transformations](./advanced-feature-transformations-explain-local.ipynb)\n",
-        "1. [Save model explanations via Azure Machine Learning Run History](../azure-integration/run-history/save-retrieve-explanations-run-history.ipynb)\n",
-        "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb)\n",
-        "1. Inferencing time: deploy a classification model and explainer:\n",
-        "    1. [Deploy a locally-trained model and explainer](../azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",
-        "    1. [Deploy a remotely-trained model and explainer](../azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "mesameki"
-      }
-    ],
-    "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/explain-model/tabular-data/explain-regression-local.yml

@@ -1,8 +0,0 @@
-name: explain-regression-local
-dependencies:
-- pip:
-  - azureml-sdk
-  - interpret
-  - azureml-interpret
-  - azureml-contrib-interpret
-  - ipywidgets

how-to-use-azureml/explain-model/tabular-data/simple-feature-transformations-explain-local.ipynb

@@ -1,517 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/tabular-data/simple-feature-transformations-explain-local.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Explain binary classification model predictions with raw feature transformations\n",
-        "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to explain and visualize a binary classification model that uses one to one and one to many feature transformations.**_\n",
-        "\n",
-        "\n",
-        "## Table of Contents\n",
-        "\n",
-        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#Setup)\n",
-        "1. [Run model explainer locally at training time](#Explain)\n",
-        "    1. Apply feature transformations\n",
-        "    1. Train a binary classification model\n",
-        "    1. Explain the model on raw features\n",
-        "        1. Generate global explanations\n",
-        "        1. Generate local explanations\n",
-        "1. [Visualize results](#Visualize)\n",
-        "1. [Next steps](#Next%20steps)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Introduction\n",
-        "\n",
-        "This notebook illustrates creating explanations for a binary classification model, IBM employee attrition classification, that uses one to one and one to many feature transformations from raw data to engineered features. The one to many feature transformations include one hot encoding on categorical features. The one to one feature transformations apply standard scaling on numeric features. Our tabular data explainer is then used to get raw feature importances.\n",
-        "\n",
-        "\n",
-        "We will showcase raw feature transformations with three tabular data explainers: TabularExplainer (SHAP), MimicExplainer (global surrogate), and PFIExplainer.\n",
-        "\n",
-        "| ![Interpretability Toolkit Architecture](./img/interpretability-architecture.png) |\n",
-        "|:--:|\n",
-        "| *Interpretability Toolkit Architecture* |\n",
-        "\n",
-        "Problem: IBM employee attrition classification with scikit-learn (run model explainer locally)\n",
-        "\n",
-        "1. Transform raw features to engineered features\n",
-        "2. Train a SVC classification model using Scikit-learn\n",
-        "3. Run 'explain_model' globally and locally with full dataset in local mode, which doesn't contact any Azure services.\n",
-        "4. Visualize the global and local explanations with the visualization dashboard.\n",
-        "---\n",
-        "\n",
-        "Setup: If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
-        "If you are using Jupyter Labs run the following command:\n",
-        "```\n",
-        "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
-        "```\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explain\n",
-        "\n",
-        "### Run model explainer locally at training time"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.pipeline import Pipeline\n",
-        "from sklearn.impute import SimpleImputer\n",
-        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
-        "from sklearn.svm import SVC\n",
-        "import pandas as pd\n",
-        "import numpy as np\n",
-        "\n",
-        "# Explainers:\n",
-        "# 1. SHAP Tabular Explainer\n",
-        "from interpret.ext.blackbox import TabularExplainer\n",
-        "\n",
-        "# OR\n",
-        "\n",
-        "# 2. Mimic Explainer\n",
-        "from interpret.ext.blackbox import MimicExplainer\n",
-        "# You can use one of the following four interpretable models as a global surrogate to the black box model\n",
-        "from interpret.ext.glassbox import LGBMExplainableModel\n",
-        "from interpret.ext.glassbox import LinearExplainableModel\n",
-        "from interpret.ext.glassbox import SGDExplainableModel\n",
-        "from interpret.ext.glassbox import DecisionTreeExplainableModel\n",
-        "\n",
-        "# OR\n",
-        "\n",
-        "# 3. PFI Explainer\n",
-        "from interpret.ext.blackbox import PFIExplainer "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Load the IBM employee attrition data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# get the IBM employee attrition dataset\n",
-        "outdirname = 'dataset.6.21.19'\n",
-        "try:\n",
-        "    from urllib import urlretrieve\n",
-        "except ImportError:\n",
-        "    from urllib.request import urlretrieve\n",
-        "import zipfile\n",
-        "zipfilename = outdirname + '.zip'\n",
-        "urlretrieve('https://publictestdatasets.blob.core.windows.net/data/' + zipfilename, zipfilename)\n",
-        "with zipfile.ZipFile(zipfilename, 'r') as unzip:\n",
-        "    unzip.extractall('.')\n",
-        "attritionData = pd.read_csv('./WA_Fn-UseC_-HR-Employee-Attrition.csv')\n",
-        "\n",
-        "# Dropping Employee count as all values are 1 and hence attrition is independent of this feature\n",
-        "attritionData = attritionData.drop(['EmployeeCount'], axis=1)\n",
-        "# Dropping Employee Number since it is merely an identifier\n",
-        "attritionData = attritionData.drop(['EmployeeNumber'], axis=1)\n",
-        "\n",
-        "attritionData = attritionData.drop(['Over18'], axis=1)\n",
-        "\n",
-        "# Since all values are 80\n",
-        "attritionData = attritionData.drop(['StandardHours'], axis=1)\n",
-        "\n",
-        "# Converting target variables from string to numerical values\n",
-        "target_map = {'Yes': 1, 'No': 0}\n",
-        "attritionData[\"Attrition_numerical\"] = attritionData[\"Attrition\"].apply(lambda x: target_map[x])\n",
-        "target = attritionData[\"Attrition_numerical\"]\n",
-        "\n",
-        "attritionXData = attritionData.drop(['Attrition_numerical', 'Attrition'], axis=1)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Split data into train and test\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "x_train, x_test, y_train, y_test = train_test_split(attritionXData, \n",
-        "                                                    target, \n",
-        "                                                    test_size = 0.2,\n",
-        "                                                    random_state=0,\n",
-        "                                                    stratify=target)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Creating dummy columns for each categorical feature\n",
-        "categorical = []\n",
-        "for col, value in attritionXData.iteritems():\n",
-        "    if value.dtype == 'object':\n",
-        "        categorical.append(col)\n",
-        "        \n",
-        "# Store the numerical columns in a list numerical\n",
-        "numerical = attritionXData.columns.difference(categorical)        "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Transform raw features"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can explain raw features by either using a `sklearn.compose.ColumnTransformer` or a list of fitted transformer tuples. The cell below uses `sklearn.compose.ColumnTransformer`. In case you want to run the example with the list of fitted transformer tuples, comment the cell below and uncomment the cell that follows after. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.compose import ColumnTransformer\n",
-        "\n",
-        "# We create the preprocessing pipelines for both numeric and categorical data.\n",
-        "numeric_transformer = Pipeline(steps=[\n",
-        "    ('imputer', SimpleImputer(strategy='median')),\n",
-        "    ('scaler', StandardScaler())])\n",
-        "\n",
-        "categorical_transformer = Pipeline(steps=[\n",
-        "    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n",
-        "    ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n",
-        "\n",
-        "transformations = ColumnTransformer(\n",
-        "    transformers=[\n",
-        "        ('num', numeric_transformer, numerical),\n",
-        "        ('cat', categorical_transformer, categorical)])\n",
-        "\n",
-        "# Append classifier to preprocessing pipeline.\n",
-        "# Now we have a full prediction pipeline.\n",
-        "clf = Pipeline(steps=[('preprocessor', transformations),\n",
-        "                      ('classifier', SVC(C = 1.0, probability=True))])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "'''\n",
-        "# Uncomment below if sklearn-pandas is not installed\n",
-        "#!pip install sklearn-pandas\n",
-        "from sklearn_pandas import DataFrameMapper\n",
-        "\n",
-        "# Impute, standardize the numeric features and one-hot encode the categorical features.    \n",
-        "\n",
-        "\n",
-        "numeric_transformations = [([f], Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())])) for f in numerical]\n",
-        "\n",
-        "categorical_transformations = [([f], OneHotEncoder(handle_unknown='ignore', sparse=False)) for f in categorical]\n",
-        "\n",
-        "transformations = numeric_transformations + categorical_transformations\n",
-        "\n",
-        "# Append classifier to preprocessing pipeline.\n",
-        "# Now we have a full prediction pipeline.\n",
-        "clf = Pipeline(steps=[('preprocessor', transformations),\n",
-        "                      ('classifier', SVC(C = 1.0, probability=True))]) \n",
-        "\n",
-        "\n",
-        "\n",
-        "'''"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Train a SVM classification model, which you want to explain"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model = clf.fit(x_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Explain predictions on your local machine"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# 1. Using SHAP TabularExplainer\n",
-        "# clf.steps[-1][1] returns the trained classification model\n",
-        "explainer = TabularExplainer(clf.steps[-1][1], \n",
-        "                             initialization_examples=x_train, \n",
-        "                             features=attritionXData.columns, \n",
-        "                             classes=[\"Not leaving\", \"leaving\"], \n",
-        "                             transformations=transformations)\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "# 2. Using MimicExplainer\n",
-        "# augment_data is optional and if true, oversamples the initialization examples to improve surrogate model accuracy to fit original model.  Useful for high-dimensional data where the number of rows is less than the number of columns. \n",
-        "# max_num_of_augmentations is optional and defines max number of times we can increase the input data size.\n",
-        "# LGBMExplainableModel can be replaced with LinearExplainableModel, SGDExplainableModel, or DecisionTreeExplainableModel\n",
-        "# explainer = MimicExplainer(clf.steps[-1][1], \n",
-        "#                            x_train, \n",
-        "#                            LGBMExplainableModel, \n",
-        "#                            augment_data=True, \n",
-        "#                            max_num_of_augmentations=10, \n",
-        "#                            features=attritionXData.columns, \n",
-        "#                            classes=[\"Not leaving\", \"leaving\"], \n",
-        "#                            transformations=transformations)\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "# 3. Using PFIExplainer\n",
-        "\n",
-        "# Use the parameter \"metric\" to pass a metric name or function to evaluate the permutation. \n",
-        "# Note that if a metric function is provided a higher value must be better.\n",
-        "# Otherwise, take the negative of the function or set the parameter \"is_error_metric\" to True.\n",
-        "# Default metrics: \n",
-        "# F1 Score for binary classification, F1 Score with micro average for multiclass classification and\n",
-        "# Mean absolute error for regression\n",
-        "\n",
-        "# explainer = PFIExplainer(clf.steps[-1][1], \n",
-        "#                          features=x_train.columns, \n",
-        "#                          transformations=transformations,\n",
-        "#                          classes=[\"Not leaving\", \"leaving\"])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Generate global explanations\n",
-        "Explain overall model predictions (global explanation)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
-        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
-        "global_explanation = explainer.explain_global(x_test)\n",
-        "\n",
-        "# Note: if you used the PFIExplainer in the previous step, use the next line of code instead\n",
-        "# global_explanation = explainer.explain_global(x_test, true_labels=y_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Sorted SHAP values\n",
-        "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
-        "# Corresponding feature names\n",
-        "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
-        "# Feature ranks (based on original order of features)\n",
-        "print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n",
-        "\n",
-        "# Note: PFIExplainer does not support per class explanations\n",
-        "# Per class feature names\n",
-        "print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n",
-        "# Per class feature importance values\n",
-        "print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Print out a dictionary that holds the sorted feature importance names and values\n",
-        "print('global importance rank: {}'.format(global_explanation.get_feature_importance_dict()))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Explain overall model predictions as a collection of local (instance-level) explanations"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# feature shap values for all features and all data points in the training data\n",
-        "print('local importance values: {}'.format(global_explanation.local_importance_values))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Generate local explanations\n",
-        "Explain local data points (individual instances)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Note: PFIExplainer does not support local explanations\n",
-        "# You can pass a specific data point or a group of data points to the explain_local function\n",
-        "\n",
-        "# E.g., Explain the first data point in the test set\n",
-        "instance_num = 1\n",
-        "local_explanation = explainer.explain_local(x_test[:instance_num])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Get the prediction for the first member of the test set and explain why model made that prediction\n",
-        "prediction_value = clf.predict(x_test)[instance_num]\n",
-        "\n",
-        "sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
-        "sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
-        "\n",
-        "print('local importance values: {}'.format(sorted_local_importance_values))\n",
-        "print('local importance names: {}'.format(sorted_local_importance_names))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Visualize\n",
-        "Load the visualization dashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ExplanationDashboard(global_explanation, model, x_test)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Next\n",
-        "Learn about other use cases of the explain package on a:\n",
-        "       \n",
-        "1. [Training time: regression problem](./explain-regression-local.ipynb)\n",
-        "1. [Training time: binary classification problem](./explain-binary-classification-local.ipynb)\n",
-        "1. [Training time: multiclass classification problem](./explain-multiclass-classification-local.ipynb)\n",
-        "1. [Explain models with advanced feature transformations](./advanced-feature-transformations-explain-local.ipynb)\n",
-        "1. [Save model explanations via Azure Machine Learning Run History](../azure-integration/run-history/save-retrieve-explanations-run-history.ipynb)\n",
-        "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb)\n",
-        "1. Inferencing time: deploy a classification model and explainer:\n",
-        "    1. [Deploy a locally-trained model and explainer](../azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",
-        "    1. [Deploy a remotely-trained model and explainer](../azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "mesameki"
-      }
-    ],
-    "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/explain-model/tabular-data/simple-feature-transformations-explain-local.yml

@@ -1,9 +0,0 @@
-name: simple-feature-transformations-explain-local
-dependencies:
-- pip:
-  - azureml-sdk
-  - interpret
-  - azureml-interpret
-  - azureml-contrib-interpret
-  - sklearn-pandas
-  - ipywidgets

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

@@ -34,7 +34,8 @@
         "| Azure Data Lake Storage Gen 1 | Yes | Yes |\n",
         "| Azure Data Lake Storage Gen 2 | Yes | Yes |\n",
         "| Azure SQL Database | Yes | Yes |\n",
-        "| Azure Database for PostgreSQL | Yes | No |"
+        "| Azure Database for PostgreSQL | Yes | Yes |",
+        "| Azure Database for MySQL | Yes | Yes |"
       ]
     },
     {
@@ -342,8 +343,8 @@
       "source": [
         "\n",
         "mysql_datastore_name=\"MySqlDatastore\"\n",
-        "server_name=os.getenv(\"MYSQL_SERVERNAME_62\", \"<my-server-name>\") # Name of PostgreSQL server \n",
+        "server_name=os.getenv(\"MYSQL_SERVERNAME_62\", \"<my-server-name>\") # Name of MySQL server \n",
-        "database_name=os.getenv(\"MYSQL_DATBASENAME_62\", \"<my-database-name>\") # Name of PostgreSQL database\n",
+        "database_name=os.getenv(\"MYSQL_DATBASENAME_62\", \"<my-database-name>\") # Name of MySQL database\n",
         "user_id=os.getenv(\"MYSQL_USERID_62\", \"<my-user-id>\") # user id\n",
         "user_password=os.getenv(\"MYSQL_USERPW_62\", \"<my-user-password>\") # user password\n",
         "\n",

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

@@ -23,9 +23,9 @@
         "# How to create Module, ModuleVersion, and use them in a pipeline with ModuleStep.\n",
         "In this notebook, we introduce the concept of versioned modules and how to use them in an Azure Machine Learning Pipeline.\n",
         "\n",
-        "The core idea behind introducing Module, ModuleVersion and ModuleStep is to allow the separation between a reusable executable components and their actual usage. These reusable software components (such as scripts or executables) can be used in different scenarios and by different users. This follows the same idea of separating software frameworks/libraries and their actual usage in applications. Module and ModuleVersion take the role of the reusable executable components where ModuleStep is there to link them to an actual usage.\n",
+        "The core idea behind introducing Module, ModuleVersion and ModuleStep is to allow the separation between reusable executable components and their actual usage. These reusable software components (such as scripts or executables) can be used in different scenarios and by different users. This follows the same idea of separating software frameworks/libraries and their actual usage in applications. Module and ModuleVersion take the role of the reusable executable components where ModuleStep is there to link them to an actual usage.\n",
         "\n",
-        "A module is an elaborated container of its versions, where each version is the actual computational unit. It is up to users to define the semantics of this hierarchical structure of container and versions. For example, they could be different versions for different use cases, development progress, etc.\n",
+        "A module is an elaborated container of its versions, where each version is the actual computational unit. It is up to users to define the semantics of this hierarchical structure of container and versions. For example, there could be different versions for different use cases, development progress, etc.\n",
         "\n",
         "Each ModuleVersion may have inputs, outputs and rely on parameters and its environment configuration to operate.\n",
         "\n",

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-versioned-pipeline-endpoints.ipynb

@@ -522,6 +522,24 @@
         "run_id = pipeline_endpoint_by_name.submit(\"NewName\")\n",
         "print(run_id)"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Use Experiment.Submit() to Submit Pipeline\n",
+        "Run specific pipeline using Experiment submit api"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Experiment\n",
+        "pipeline_run = Experiment(ws, name=\"submit_from_endpoint\").submit(pipeline_endpoint_by_name, tags={'endpoint_tag': \"1\"}, pipeline_version=\"0\")"
+      ]
     }
   ],
   "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 import AutoMLStep\n",
+        "from azureml.train.automl.runtime import AutoMLStep\n",
         "\n",
         "# Check core SDK version number\n",
         "print(\"SDK version:\", azureml.core.VERSION)"

how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb

@@ -822,7 +822,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.train.automl import AutoMLStep\n",
+        "from azureml.train.automl.runtime import AutoMLStep\n",
         "\n",
         "trainWithAutomlStep = AutoMLStep(\n",
         "    name='AutoML_Regression',\n",

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

@@ -322,7 +322,6 @@
         "# Runconfig\n",
         "amlcompute_run_config = RunConfiguration(conda_dependencies=cd)\n",
         "amlcompute_run_config.environment.docker.enabled = True\n",
-        "amlcompute_run_config.environment.docker.gpu_support = True\n",
         "amlcompute_run_config.environment.docker.base_image = DEFAULT_GPU_IMAGE\n",
         "amlcompute_run_config.environment.spark.precache_packages = False"
       ]

how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.ipynb

@@ -104,7 +104,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "sample-interactiveloginauth-tenantid"
+        ]
+      },
       "outputs": [],
       "source": [
         "from azureml.core.authentication import InteractiveLoginAuthentication\n",
@@ -131,7 +135,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "sample-azurecliauth"
+        ]
+      },
       "outputs": [],
       "source": [
         "from azureml.core.authentication import AzureCliAuthentication\n",
@@ -168,7 +176,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "sample-msiauth"
+        ]
+      },
       "outputs": [],
       "source": [
         "from azureml.core.authentication import MsiAuthentication\n",
@@ -245,7 +257,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "sample-serviceprincipalauth-tenantid"
+        ]
+      },
       "outputs": [],
       "source": [
         "import os\n",
@@ -300,7 +316,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "sample-keyvault"
+        ]
+      },
       "outputs": [],
       "source": [
         "import os, uuid\n",

how-to-use-azureml/monitor-models/data-drift/drift-on-aks.ipynb

@@ -480,7 +480,7 @@
     ],
     "category": "tutorial",
     "compute": [
-      "remote"
+      "Remote"
     ],
     "datasets": [
       "NOAA"

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 1.0.74, you are currently running version\", azureml.core.VERSION)"
+        "print(\"This notebook was created using SDK version 1.0.76, you are currently running version\", azureml.core.VERSION)"
       ]
     },
     {
@@ -542,7 +542,9 @@
     "compute": [
       "None"
     ],
-    "datasets": [],
+    "datasets": [
+      "None"
+    ],
     "deployment": [
       "None"
     ],

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

@@ -63,7 +63,6 @@
       "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')"
       ]
@@ -258,6 +257,16 @@
         "metrics = run.get_metrics()\n",
         "print(metrics)"
       ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# register the generated model\n",
+        "model = run.register_model(model_name='iris.model', model_path='outputs/iris.model')\n"
+      ]
     }
   ],
   "metadata": {
@@ -297,7 +306,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.6.2"
     },
     "tags": [
       "None"

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

@@ -76,6 +76,8 @@ train, test = data.randomSplit([0.70, 0.30])
 lr = pyspark.ml.classification.LogisticRegression(regParam=reg)
 model = lr.fit(train)
 
+model.save(os.path.join("outputs", "iris.model"))
+
 # predict on the test set
 prediction = model.transform(test)
 print("Prediction")

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

@@ -685,7 +685,7 @@
     "framework": [
       "None"
     ],
-    "friendly_name": "",
+    "friendly_name": "Train and deploy a model using Python SDK",
     "index_order": 1,
     "kernelspec": {
       "display_name": "Python 3.6",

how-to-use-azureml/training/using-environments/using-environments.ipynb

@@ -27,6 +27,7 @@
         "\n",
         "1. [Introduction](#Introduction)\n",
         "1. [Setup](#Setup)\n",
+        "1. [Use curated environment](#Use-curated-environment)\n",
         "1. [Create environment](#Create-environment)\n",
         "    1. Add Python packages\n",
         "    1. Specify environment variables\n",
@@ -36,6 +37,8 @@
         "1. [Other ways to create environments](#Other-ways-to-create-environments)\n",
         "    1. From existing Conda environment\n",
         "    1. From Conda or pip files\n",
+        "1. [Estimators and environments](#Estimators-and-environments)    \n",
+        "1. [Using environments for inferencing](#Using-environments-for-inferencing)\n",
         "1. [Docker settings](#Docker-settings)\n",
         "1. [Spark and Azure Databricks settings](#Spark-and-Azure-Databricks-settings)\n",
         "1. [Next steps](#Next-steps)\n",
@@ -84,7 +87,57 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Create environment\n",
+        "## Use curated environments\n",
+        "\n",
+        "Curated environments are provided by Azure Machine Learning and are available in your workspace by default. They contain collections of Python packages and settings to help you get started different machine learning frameworks. \n",
+        "\n",
+        "  * The __AzureML-Minimal__ environment contains a minimal set of packages to enable run tracking and asset uploading. You can use it as a starting point for your own environment.\n",
+        "  * The __AzureML-Tutorial__ environment contains common data science packages, such as Scikit-Learn, Pandas and Matplotlib, and larger set of azureml-sdk packages.\n",
+        " \n",
+        "Curated environments are backed by cached Docker images, reducing the run preparation cost.\n",
+        " \n",
+        "You can get a curated environment using"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Environment\n",
+        "\n",
+        "curated_env = Environment.get(workspace=ws, name=\"AzureML-Minimal\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To list curated environments, use following code.\n",
+        "\n",
+        "**Note**: The name prefixes _AzureML_ and _Microsoft_ are reserved for curated environments. Do not use them for your own environments"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "envs = Environment.list(workspace=ws)\n",
+        "\n",
+        "for env in envs:\n",
+        "    if env.startswith(\"AzureML\"):\n",
+        "        print(\"Name\",env)\n",
+        "        print(\"packages\", envs[env].python.conda_dependencies.serialize_to_string())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create your own environment\n",
         "\n",
         "You can create an environment by instantiating ```Environment``` object and then setting its attributes: set of Python packages, environment variables and others.\n",
         "\n",
@@ -99,7 +152,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core import Environment\n",
         "from azureml.core.environment import CondaDependencies\n",
         "\n",
         "myenv = Environment(name=\"myenv\")\n",
@@ -185,6 +237,22 @@
         "run.wait_for_completion(show_output=True)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To audit the environment used by for a run, you can use ```get_environement```."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "run.get_environment()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -256,6 +324,48 @@
         "```\n"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Estimators and environments\n",
+        "\n",
+        "[Estimators](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-ml-models) are backed by environments that define the base images, Python packages and other settings for the training environment. \n",
+        "\n",
+        "For example, to see the environment behind PyTorch Estimator, you can create a dummy instance of the Estimator, and look at the ```run_config.environment``` property."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.dnn import PyTorch\n",
+        "\n",
+        "pt = PyTorch(source_directory=\".\", compute_target=\"local\")\n",
+        "pt.run_config.environment"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Using environments for inferencing\n",
+        "\n",
+        "You can re-use the training environment when you deploy your model as a web service, by specifying inferencing stack version, and adding then environment to ```InferenceConfig```.\n",
+        "\n",
+        "```\n",
+        "from azureml.core.model import InferenceConfig\n",
+        "\n",
+        "myenv.inferencing_stack_version = \"latest\"\n",
+        "\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)\n",
+        "```\n",
+        "\n",
+        "See [Register Model and deploy as Webservice Notebook](../../deployment/deploy-to-cloud/model-register-and-deploy.ipynb) for an end-to-end example of web service deployment."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -299,7 +409,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "You can also specify whether to use GPU or shared volumes, and shm size."
+        "You can also specify shared volumes, and shm size."
       ]
     },
     {
@@ -308,7 +418,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "myenv.docker.gpu_support\n",
         "myenv.docker.shared_volumes\n",
         "myenv.docker.shm_size"
       ]
@@ -336,7 +445,7 @@
         "\n",
         "Learn more about registering and deploying a model:\n",
         "\n",
-        "* [Model Register and Deploy](../../deploy-to-cloud/model-register-and-deploy.ipynb)"
+        "* [Register Model and deploy as Webservice](../../deployment/deploy-to-cloud/model-register-and-deploy.ipynb)"
       ]
     },
     {

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

@@ -414,7 +414,7 @@
     ],
     "category": "tutorial",
     "compute": [
-      "remote"
+      "Remote"
     ],
     "datasets": [
       "NOAA"

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

@@ -522,7 +522,7 @@
     ],
     "category": "tutorial",
     "compute": [
-      "local"
+      "Local"
     ],
     "datasets": [
       "NOAA"

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

@@ -606,11 +606,11 @@
     ],
     "category": "tutorial",
     "compute": [
-      "remote"
+      "Remote"
     ],
     "datasets": [
       "Iris",
-      "Daibetes"
+      "Diabetes"
     ],
     "deployment": [
       "None"

index.md

@@ -18,31 +18,31 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 
 |Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags |
 |:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:|
-| [Forecasting BikeShare Demand](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb) | forecasting | BikeShare | remote | None | Azure ML AutoML | Forecasting |
+| [Forecasting BikeShare Demand](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb) | Forecasting | BikeShare | Remote | None | Azure ML AutoML | Forecasting |
 
-| [Forecasting orange juice sales with deployment](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb) | Forecasting | Orange Juice Sales | remote | Azure Container Instance | Azure ML AutoML |  |
+| [Forecasting orange juice sales with deployment](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb) | Forecasting | Orange Juice Sales | Remote | Azure Container Instance | Azure ML AutoML | None |
 
 | [Forecasting with automated ML SQL integration](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/sql-server/energy-demand/auto-ml-sql-energy-demand.ipynb) | Forecasting | NYC Energy | Local | None | Azure ML AutoML |  |
 
 | [Setup automated ML SQL integration](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/sql-server/setup/auto-ml-sql-setup.ipynb) | None | None | None | None | Azure ML AutoML |  |
 
-| [Register a model and deploy locally](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb) | Deployment |  | local | Local | None | None |
+| [Register a model and deploy locally](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb) | Deployment | None | Local | Local | None | None |
 
-| :star:[Data drift on aks](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/monitor-models/data-drift/drift-on-aks.ipynb) | Filtering | NOAA | remote | AKS | Azure ML | Dataset, Timeseries, Drift |
+| :star:[Data drift on aks](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/monitor-models/data-drift/drift-on-aks.ipynb) | Filtering | NOAA | Remote | AKS | Azure ML | Dataset, Timeseries, Drift |
 
-| [](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb) | Training and deploying a model from a notebook | Diabetes | Local | Azure Container Instance | None | None |
+| [Train and deploy a model using Python SDK](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb) | Training and deploying a model from a notebook | Diabetes | Local | Azure Container Instance | None | None |
 
-| :star:[Data drift quickdemo](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb) | Filtering | NOAA | remote | None | Azure ML | Dataset, Timeseries, Drift |
+| :star:[Data drift quickdemo](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb) | Filtering | NOAA | Remote | None | Azure ML | Dataset, Timeseries, Drift |
 
-| :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/tabular-timeseries-dataset-filtering.ipynb) | Filtering | NOAA | local | None | Azure ML | Dataset, Tabular Timeseries |
+| :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/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.ipynb) | Filtering | Iris, Daibetes | remote | None | Azure ML | Dataset |
+| :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.ipynb) | Filtering | Iris, Diabetes | Remote | None | Azure ML | Dataset |
 
-| [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/automl-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 |
+| [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 |
 
@@ -132,15 +132,15 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 
 |Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags |
 |:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:|
-| [Deploy MNIST digit recognition with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb) | Image Classification | MNIST | local | Azure Container Instance | ONNX | ONNX Model Zoo |
+| [Deploy MNIST digit recognition with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb) | Image Classification | MNIST | Local | Azure Container Instance | ONNX | ONNX Model Zoo |
 
-| [Deploy Facial Expression Recognition (FER+) with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb) | Facial Expression Recognition | Emotion FER | local | Azure Container Instance | ONNX | ONNX Model Zoo |
+| [Deploy Facial Expression Recognition (FER+) with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb) | Facial Expression Recognition | Emotion FER | Local | Azure Container Instance | ONNX | ONNX Model Zoo |
 
 | :star:[Register model and deploy as webservice](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb) | Deploy a model with Azure Machine Learning | Diabetes | None | Azure Container Instance | Scikit-learn | None |
 
 | [Train MNIST in PyTorch, convert, and deploy with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb) | Image Classification | MNIST | AML Compute | Azure Container Instance | ONNX | ONNX Converter |
 
-| [Deploy ResNet50 with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb) | Image Classification | ImageNet | local | Azure Container Instance | ONNX | ONNX Model Zoo |
+| [Deploy ResNet50 with ONNX Runtime](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb) | Image Classification | ImageNet | Local | Azure Container Instance | ONNX | ONNX Model Zoo |
 
 | [Deploy a model as a web service using MLflow](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/using-mlflow/deploy-model/deploy-model.ipynb) | Use MLflow with AML | Diabetes | None | Azure Container Instance | Scikit-learn | None |
 
@@ -151,16 +151,12 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 ## Other Notebooks
 |Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags |
 |:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:|
-| [DNN Text Featurization](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb) | Text featurization using DNNs for classification | None |  | None | None | None |
+| [DNN Text Featurization](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb) | Text featurization using DNNs for classification | None | AML Compute | None | None | None |
 
 | [Automated ML Grouping with Pipeline.](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-grouping/auto-ml-forecasting-grouping.ipynb) | Use AzureML Pipeline to trigger multiple Automated ML runs. | Orange Juice Sales | AML Compute | Azure Container Instance | Scikit-learn, Pytorch | AutomatedML |
 
 | [configuration](https://github.com/Azure/MachineLearningNotebooks/blob/master/configuration.ipynb) |  |  |  |  |  |  |
 
-| [file-dataset-image-inference-mnist](https://github.com/Azure/MachineLearningNotebooks/blob/master//contrib/batch_inferencing/file-dataset-image-inference-mnist.ipynb) |  |  |  |  |  |  |
-
-| [tabular-dataset-inference-iris](https://github.com/Azure/MachineLearningNotebooks/blob/master//contrib/batch_inferencing/tabular-dataset-inference-iris.ipynb) |  |  |  |  |  |  |
-
 | [lightgbm-example](https://github.com/Azure/MachineLearningNotebooks/blob/master//contrib/gbdt/lightgbm/lightgbm-example.ipynb) |  |  |  |  |  |  |
 
 | [azure-ml-with-nvidia-rapids](https://github.com/Azure/MachineLearningNotebooks/blob/master//contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb) |  |  |  |  |  |  |
@@ -169,7 +165,7 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 
 | [auto-ml-forecasting-beer-remote](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb) |  |  |  |  |  |  |
 
-| :star:[auto-ml-forecasting-energy-demand](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb) | Forecasting |  |  |  |  |  |
+| [auto-ml-forecasting-energy-demand](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb) |  |  |  |  |  |  |
 
 | [auto-ml-regression](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb) |  |  |  |  |  |  |
 
@@ -217,16 +213,6 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 
 | [train-explain-model-on-amlcompute-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb) |  |  |  |  |  |  |
 
-| [advanced-feature-transformations-explain-local](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/tabular-data/advanced-feature-transformations-explain-local.ipynb) |  |  |  |  |  |  |
-
-| [explain-binary-classification-local](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/tabular-data/explain-binary-classification-local.ipynb) |  |  |  |  |  |  |
-
-| [explain-multiclass-classification-local](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/tabular-data/explain-multiclass-classification-local.ipynb) |  |  |  |  |  |  |
-
-| [explain-regression-local](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/tabular-data/explain-regression-local.ipynb) |  |  |  |  |  |  |
-
-| [simple-feature-transformations-explain-local](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/tabular-data/simple-feature-transformations-explain-local.ipynb) |  |  |  |  |  |  |
-
 | [nyc-taxi-data-regression-model-building](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb) |  |  |  |  |  |  |
 
 | [pipeline-batch-scoring](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.ipynb) |  |  |  |  |  |  |
@@ -235,7 +221,7 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 
 | [authentication-in-azureml](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.ipynb) |  |  |  |  |  |  |
 
-| [Logging APIs](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb) | Logging APIs and analyzing results |  | None | None | None | None |
+| [Logging APIs](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb) | Logging APIs and analyzing results | None | None | None | None | None |
 
 | [distributed-cntk-with-custom-docker](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/distributed-cntk-with-custom-docker/distributed-cntk-with-custom-docker.ipynb) |  |  |  |  |  |  |
 

setup-environment/configuration.ipynb

@@ -102,7 +102,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.0.74 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.0.76 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },